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Journal articles on the topic 'Histopathological tumor segmentation'
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1
Liu, Yiqing, Qiming He, Hufei Duan, Huijuan Shi, Anjia Han, and Yonghong He. "Using Sparse Patch Annotation for Tumor Segmentation in Histopathological Images." Sensors 22, no. 16 (August 13, 2022): 6053. http://dx.doi.org/10.3390/s22166053.
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Tumor segmentation is a fundamental task in histopathological image analysis. Creating accurate pixel-wise annotations for such segmentation tasks in a fully-supervised training framework requires significant effort. To reduce the burden of manual annotation, we propose a novel weakly supervised segmentation framework based on sparse patch annotation, i.e., only small portions of patches in an image are labeled as ‘tumor’ or ‘normal’. The framework consists of a patch-wise segmentation model called PSeger, and an innovative semi-supervised algorithm. PSeger has two branches for patch classification and image classification, respectively. This two-branch structure enables the model to learn more general features and thus reduce the risk of overfitting when learning sparsely annotated data. We incorporate the idea of consistency learning and self-training into the semi-supervised training strategy to take advantage of the unlabeled images. Trained on the BCSS dataset with only 25% of the images labeled (five patches for each labeled image), our proposed method achieved competitive performance compared to the fully supervised pixel-wise segmentation models. Experiments demonstrate that the proposed solution has the potential to reduce the burden of labeling histopathological images.
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van der Kamp, Ananda, Thomas de Bel, Ludo van Alst, Jikke Rutgers, Marry M. van den Heuvel-Eibrink, Annelies M. C. Mavinkurve-Groothuis, Jeroen van der Laak, and Ronald R. de Krijger. "Automated Deep Learning-Based Classification of Wilms Tumor Histopathology." Cancers 15, no. 9 (May 8, 2023): 2656. http://dx.doi.org/10.3390/cancers15092656.
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(1) Background: Histopathological assessment of Wilms tumors (WT) is crucial for risk group classification to guide postoperative stratification in chemotherapy pre-treated WT cases. However, due to the heterogeneous nature of the tumor, significant interobserver variation between pathologists in WT diagnosis has been observed, potentially leading to misclassification and suboptimal treatment. We investigated whether artificial intelligence (AI) can contribute to accurate and reproducible histopathological assessment of WT through recognition of individual histopathological tumor components. (2) Methods: We assessed the performance of a deep learning-based AI system in quantifying WT components in hematoxylin and eosin-stained slides by calculating the Sørensen–Dice coefficient for fifteen predefined renal tissue components, including six tumor-related components. We trained the AI system using multiclass annotations from 72 whole-slide images of patients diagnosed with WT. (3) Results: The overall Dice coefficient for all fifteen tissue components was 0.85 and for the six tumor-related components was 0.79. Tumor segmentation worked best to reliably identify necrosis (Dice coefficient 0.98) and blastema (Dice coefficient 0.82). (4) Conclusions: Accurate histopathological classification of WT may be feasible using a digital pathology-based AI system in a national cohort of WT patients.
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Zadeh Shirazi, Amin, Eric Fornaciari, Mark D. McDonnell, Mahdi Yaghoobi, Yesenia Cevallos, Luis Tello-Oquendo, Deysi Inca, and Guillermo A. Gomez. "The Application of Deep Convolutional Neural Networks to Brain Cancer Images: A Survey." Journal of Personalized Medicine 10, no. 4 (November 12, 2020): 224. http://dx.doi.org/10.3390/jpm10040224.
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In recent years, improved deep learning techniques have been applied to biomedical image processing for the classification and segmentation of different tumors based on magnetic resonance imaging (MRI) and histopathological imaging (H&E) clinical information. Deep Convolutional Neural Networks (DCNNs) architectures include tens to hundreds of processing layers that can extract multiple levels of features in image-based data, which would be otherwise very difficult and time-consuming to be recognized and extracted by experts for classification of tumors into different tumor types, as well as segmentation of tumor images. This article summarizes the latest studies of deep learning techniques applied to three different kinds of brain cancer medical images (histology, magnetic resonance, and computed tomography) and highlights current challenges in the field for the broader applicability of DCNN in personalized brain cancer care by focusing on two main applications of DCNNs: classification and segmentation of brain cancer tumors images.
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Park, Youngjae, Jinhee Park, and Gil-Jin Jang. "Efficient Perineural Invasion Detection of Histopathological Images Using U-Net." Electronics 11, no. 10 (May 22, 2022): 1649. http://dx.doi.org/10.3390/electronics11101649.
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Perineural invasion (PNI), a sign of poor diagnosis and tumor metastasis, is common in a variety of malignant tumors. The infiltrating patterns and morphologies of tumors vary by organ and histological diversity, making PNI detection difficult in biopsy, which must be performed manually by pathologists. As the diameters of PNI nerves are measured on a millimeter scale, the PNI region is extremely small compared to the whole pathological image. In this study, an efficient deep learning-based method is proposed for detecting PNI regions in multiple types of cancers using only PNI annotations without detailed segmentation maps for each nerve and tumor cells obtained by pathologists. The key idea of the proposed method is to train the adopted deep learning model, U-Net, to capture the boundary regions where two features coexist. A boundary dilation method and a loss combination technique are proposed to improve the detection performance of PNI without requiring full segmentation maps. Experiments were conducted with various combinations of boundary dilation widths and loss functions. It is confirmed that the proposed method effectively improves PNI detection performance from 0.188 to 0.275. Additional experiments were also performed on normal nerve detection to validate the applicability of the proposed method to the general boundary detection tasks. The experimental results demonstrate that the proposed method is also effective for general tasks, and it improved nerve detection performance from 0.511 to 0.693.
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Altini, Nicola, Emilia Puro, Maria Giovanna Taccogna, Francescomaria Marino, Simona De Summa, Concetta Saponaro, Eliseo Mattioli, Francesco Alfredo Zito, and Vitoantonio Bevilacqua. "Tumor Cellularity Assessment of Breast Histopathological Slides via Instance Segmentation and Pathomic Features Explainability." Bioengineering 10, no. 4 (March 23, 2023): 396. http://dx.doi.org/10.3390/bioengineering10040396.
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The segmentation and classification of cell nuclei are pivotal steps in the pipelines for the analysis of bioimages. Deep learning (DL) approaches are leading the digital pathology field in the context of nuclei detection and classification. Nevertheless, the features that are exploited by DL models to make their predictions are difficult to interpret, hindering the deployment of such methods in clinical practice. On the other hand, pathomic features can be linked to an easier description of the characteristics exploited by the classifiers for making the final predictions. Thus, in this work, we developed an explainable computer-aided diagnosis (CAD) system that can be used to support pathologists in the evaluation of tumor cellularity in breast histopathological slides. In particular, we compared an end-to-end DL approach that exploits the Mask R-CNN instance segmentation architecture with a two steps pipeline, where the features are extracted while considering the morphological and textural characteristics of the cell nuclei. Classifiers that are based on support vector machines and artificial neural networks are trained on top of these features in order to discriminate between tumor and non-tumor nuclei. Afterwards, the SHAP (Shapley additive explanations) explainable artificial intelligence technique was employed to perform a feature importance analysis, which led to an understanding of the features processed by the machine learning models for making their decisions. An expert pathologist validated the employed feature set, corroborating the clinical usability of the model. Even though the models resulting from the two-stage pipeline are slightly less accurate than those of the end-to-end approach, the interpretability of their features is clearer and may help build trust for pathologists to adopt artificial intelligence-based CAD systems in their clinical workflow. To further show the validity of the proposed approach, it has been tested on an external validation dataset, which was collected from IRCCS Istituto Tumori “Giovanni Paolo II” and made publicly available to ease research concerning the quantification of tumor cellularity.
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Althubaity, DaifAllah D., Faisal Fahad Alotaibi, Abdalla Mohamed Ahmed Osman, Mugahed Ali Al-khadher, Yahya Hussein Ahmed Abdalla, Sadeq Abdo Alwesabi, Elsadig Eltaher Hamed Abdulrahman, and Maram Abdulkhalek Alhemairy. "Automated Lung Cancer Segmentation in Tissue Micro Array Analysis Histopathological Images Using a Prototype of Computer-Assisted Diagnosis." Journal of Personalized Medicine 13, no. 3 (February 23, 2023): 388. http://dx.doi.org/10.3390/jpm13030388.
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Background: Lung cancer is a fatal disease that kills approximately 85% of those diagnosed with it. In recent years, advances in medical imaging have greatly improved the acquisition, storage, and visualization of various pathologies, making it a necessary component in medicine today. Objective: Develop a computer-aided diagnostic system to detect lung cancer early by segmenting tumor and non-tumor tissue on Tissue Micro Array Analysis (TMA) histopathological images. Method: The prototype computer-aided diagnostic system was developed to segment tumor areas, non-tumor areas, and fundus on TMA histopathological images. Results: The system achieved an average accuracy of 83.4% and an F-measurement of 84.4% in segmenting tumor and non-tumor tissue. Conclusion: The computer-aided diagnostic system provides a second diagnostic opinion to specialists, allowing for more precise diagnoses and more appropriate treatments for lung cancer.
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Musulin, Jelena, Daniel Štifanić, Ana Zulijani, Tomislav Ćabov, Andrea Dekanić, and Zlatan Car. "An Enhanced Histopathology Analysis: An AI-Based System for Multiclass Grading of Oral Squamous Cell Carcinoma and Segmenting of Epithelial and Stromal Tissue." Cancers 13, no. 8 (April 8, 2021): 1784. http://dx.doi.org/10.3390/cancers13081784.
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Oral squamous cell carcinoma is most frequent histological neoplasm of head and neck cancers, and although it is localized in a region that is accessible to see and can be detected very early, this usually does not occur. The standard procedure for the diagnosis of oral cancer is based on histopathological examination, however, the main problem in this kind of procedure is tumor heterogeneity where a subjective component of the examination could directly impact patient-specific treatment intervention. For this reason, artificial intelligence (AI) algorithms are widely used as computational aid in the diagnosis for classification and segmentation of tumors, in order to reduce inter- and intra-observer variability. In this research, a two-stage AI-based system for automatic multiclass grading (the first stage) and segmentation of the epithelial and stromal tissue (the second stage) from oral histopathological images is proposed in order to assist the clinician in oral squamous cell carcinoma diagnosis. The integration of Xception and SWT resulted in the highest classification value of 0.963 (σ = 0.042) AUCmacro and 0.966 (σ = 0.027) AUCmicro while using DeepLabv3+ along with Xception_65 as backbone and data preprocessing, semantic segmentation prediction resulted in 0.878 (σ = 0.027) mIOU and 0.955 (σ = 0.014) F1 score. Obtained results reveal that the proposed AI-based system has great potential in the diagnosis of OSCC.
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Nicolás-Sáenz, Laura, Sara Guerrero-Aspizua, Javier Pascau, and Arrate Muñoz-Barrutia. "Nonlinear Image Registration and Pixel Classification Pipeline for the Study of Tumor Heterogeneity Maps." Entropy 22, no. 9 (August 28, 2020): 946. http://dx.doi.org/10.3390/e22090946.
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We present a novel method to assess the variations in protein expression and spatial heterogeneity of tumor biopsies with application in computational pathology. This was done using different antigen stains for each tissue section and proceeding with a complex image registration followed by a final step of color segmentation to detect the exact location of the proteins of interest. For proper assessment, the registration needs to be highly accurate for the careful study of the antigen patterns. However, accurate registration of histopathological images comes with three main problems: the high amount of artifacts due to the complex biopsy preparation, the size of the images, and the complexity of the local morphology. Our method manages to achieve an accurate registration of the tissue cuts and segmentation of the positive antigen areas.
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Huang, Zhi, Anil V. Parwani, Kun Huang, and Zaibo Li. "Abstract 5436: Developing artificial intelligence algorithms to predict response to neoadjuvant chemotherapy in HER2-positive breast cancer." Cancer Research 83, no. 7_Supplement (April 4, 2023): 5436. http://dx.doi.org/10.1158/1538-7445.am2023-5436.
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Abstract Increasing implementation of whole slide image (WSI) and advances in computing capacity enable the use of artificial intelligence (AI) in pathology, such as quantification of biomarkers, aids in diagnosis and detection of lymph node metastasis. However, predicting therapy response in cancer patients from pre-treatment histopathologic images remains a challenging task, limited by poor understanding of tumor immune microenvironment. In this study, we aimed to develop AI models using multi-source histopathologic images to predict neoadjuvant chemotherapy (NAC) response in HER2-positive (HER2+) breast cancers. First, pretreatment tumor tissues were stained with Hematoxylin and Eosin (H&E) and multiplex immunohistochemistry (IHC) including tumor immune microenvironment markers (PD-L1: immune checkpoint protein; CD8: marker for cytotoxic T-cells; and CD163: marker for type 2 macrophages). Next, we developed an AI-based pipeline to automatically extract histopathologic features from H&E and multiplex IHC WSIs. The pipeline included: A) H&E tissue segmentation based on DeepLabV3 model to generate stroma, tumor, and lymphocyte-rich regions. B) IHC marker segmentation to segment CD8, CD163, and PD-L1 stained cells. C) H&E and IHC non-rigid registration to match H&E and IHC images since they were stained from different levels of tissue. D) Image-based registration and segmentation histopathologic feature construction. A total of 36 histopathological features were constructed to represent tumor immune microenvironment characteristics such as ratios of PD-L1, CD8 and CD163 in tumoral, stromal or lymphocyte-rich regions. They were used to train machine learning (ML) models to predict NAC response in a training dataset with 62 HER2+ breast cancers (38 with complete and 24 with incomplete response). The ML model using logistic regression demonstrated the best performance with an area under curve (AUC) of 0.8975. We also tested ML models using pathologists-derived histopathologic features, but the best performed model showed an AUC of 0.7880. Finally, the developed logistic regression ML model was tested on an external validation dataset with 20 HER2+ breast cancers (10 with complete and 10 with incomplete response) and yielded an AUC of 0.9005. In summary, we described an automatic, accurate and interpretable AI-based pipeline to extract histopathologic features from H&E and IHC WSIs and then used these features to develop machine learning model to accurately predict NAC response in HER2+ breast cancers. The ML model using AI-based extracted features outperformed the model using features manually generated by pathologists. Citation Format: Zhi Huang, Anil V. Parwani, Kun Huang, Zaibo Li. Developing artificial intelligence algorithms to predict response to neoadjuvant chemotherapy in HER2-positive breast cancer. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5436.
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Fagundes, Theara C., Arnoldo Mafra, Rodrigo G. Silva, Ana C. G. Castro, Luciana C. Silva, Priscilla T. Aguiar, Josiane A. Silva, Eduardo P. Junior, Alexei M. Machado, and Marcelo Mamede. "Individualized threshold for tumor segmentation in 18F-FDG PET/CT imaging: The key for response evaluation of neoadjuvant chemoradiation therapy in patients with rectal cancer?" Revista da Associação Médica Brasileira 64, no. 2 (February 2018): 119–26. http://dx.doi.org/10.1590/1806-9282.64.02.119.
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Summary Introduction: The standard treatment for locally advanced rectal cancer (RC) consists of neoadjuvant chemoradiation followed by radical surgery. Regardless the extensive use of SUVmax in 18F-FDG PET tumor uptake as representation of tumor glycolytic consumption, there is a trend to apply metabolic volume instead. Thus, the aim of the present study was to evaluate a noninvasive method for tumor segmentation using the 18F-FDG PET imaging in order to predict response to neoadjuvant chemoradiation therapy in patients with rectal cancer. Method: The sample consisted of stage II and III rectal cancer patients undergoing 18F-FDG PET/CT examination before and eight weeks after neoadjuvant therapy. An individualized tumor segmentation methodology was applied to generate tumor volumes (SUV2SD) and compare with standard SUVmax and fixed threshold (SUV40%, SUV50% and SUV60%) pre- and post-therapy. Therapeutic response was assessed in the resected specimens using Dworak's protocol recommendations. Several variables were generated and compared with the histopathological results. Results: Seventeen (17) patients were included and analyzed. Significant differences were observed between responders (Dworak 3 and 4) and non-responders for SUVmax-2 (p<0.01), SUV2SD-2 (p<0.05), SUV40%-2 (p<0.05), SUV50%-2 (p<0.05) and SUV60%-2 (p<0.05). ROC analyses showed significant areas under the curve (p<0.01) for the proposed methodology with sensitivity and specificity varying from 60% to 83% and 73% to 82%, respectively. Conclusion: The present study confirmed the predictive power of the variables using a noninvasive individualized methodology for tumor segmentation based on 18F-FDG PET/CT imaging for response evaluation in patients with rectal cancer after neoadjuvant chemoradiation therapy.
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Anghel, Cristian, Mugur Cristian Grasu, Denisa Andreea Anghel, Gina-Ionela Rusu-Munteanu, Radu Lucian Dumitru, and Ioana Gabriela Lupescu. "Pancreatic Adenocarcinoma: Imaging Modalities and the Role of Artificial Intelligence in Analyzing CT and MRI Images." Diagnostics 14, no. 4 (February 16, 2024): 438. http://dx.doi.org/10.3390/diagnostics14040438.
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Pancreatic ductal adenocarcinoma (PDAC) stands out as the predominant malignant neoplasm affecting the pancreas, characterized by a poor prognosis, in most cases patients being diagnosed in a nonresectable stage. Image-based artificial intelligence (AI) models implemented in tumor detection, segmentation, and classification could improve diagnosis with better treatment options and increased survival. This review included papers published in the last five years and describes the current trends in AI algorithms used in PDAC. We analyzed the applications of AI in the detection of PDAC, segmentation of the lesion, and classification algorithms used in differential diagnosis, prognosis, and histopathological and genomic prediction. The results show a lack of multi-institutional collaboration and stresses the need for bigger datasets in order for AI models to be implemented in a clinically relevant manner.
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Cancian, Pierandrea, Nina Cortese, Matteo Donadon, Marco Di Maio, Cristiana Soldani, Federica Marchesi, Victor Savevski, et al. "Development of a Deep-Learning Pipeline to Recognize and Characterize Macrophages in Colo-Rectal Liver Metastasis." Cancers 13, no. 13 (July 1, 2021): 3313. http://dx.doi.org/10.3390/cancers13133313.
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Quantitative analysis of Tumor Microenvironment (TME) provides prognostic and predictive information in several human cancers but, with few exceptions, it is not performed in daily clinical practice since it is extremely time-consuming. We recently showed that the morphology of Tumor Associated Macrophages (TAMs) correlates with outcome in patients with Colo-Rectal Liver Metastases (CLM). However, as for other TME components, recognizing and characterizing hundreds of TAMs in a single histopathological slide is unfeasible. To fasten this process, we explored a deep-learning based solution. We tested three Convolutional Neural Networks (CNNs), namely UNet, SegNet and DeepLab-v3, with three different segmentation strategies, semantic segmentation, pixel penalties and instance segmentation. The different experiments are compared according to the Intersection over Union (IoU), a metric describing the similarity between what CNN predicts as TAM and the ground truth, and the Symmetric Best Dice (SBD), which indicates the ability of CNN to separate different TAMs. UNet and SegNet showed intrinsic limitations in discriminating single TAMs (highest SBD 61.34±2.21), whereas DeepLab-v3 accurately recognized TAMs from the background (IoU 89.13±3.85) and separated different TAMs (SBD 79.00±3.72). This deep-learning pipeline to recognize TAMs in digital slides will allow the characterization of TAM-related metrics in the daily clinical practice, allowing the implementation of prognostic tools.
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Mahmoudi, Keon, Daniel H. Kim, Elham Tavakkol, Shingo Kihira, Adam Bauer, Nadejda Tsankova, Fahad Khan, Adilia Hormigo, Vivek Yedavalli, and Kambiz Nael. "Multiparametric Radiogenomic Model to Predict Survival in Patients with Glioblastoma." Cancers 16, no. 3 (January 30, 2024): 589. http://dx.doi.org/10.3390/cancers16030589.
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Background: Clinical, histopathological, and imaging variables have been associated with prognosis in patients with glioblastoma (GBM). We aimed to develop a multiparametric radiogenomic model incorporating MRI texture features, demographic data, and histopathological tumor biomarkers to predict prognosis in patients with GBM. Methods: In this retrospective study, patients were included if they had confirmed diagnosis of GBM with histopathological biomarkers and pre-operative MRI. Tumor segmentation was performed, and texture features were extracted to develop a predictive radiomic model of survival (<18 months vs. ≥18 months) using multivariate analysis and Least Absolute Shrinkage and Selection Operator (LASSO) regularization to reduce the risk of overfitting. This radiomic model in combination with clinical and histopathological data was inserted into a backward stepwise logistic regression model to assess survival. The diagnostic performance of this model was reported for the training and external validation sets. Results: A total of 116 patients were included for model development and 40 patients for external testing validation. The diagnostic performance (AUC/sensitivity/specificity) of the radiomic model generated from seven texture features in determination of ≥18 months survival was 0.71/69.0/70.3. Three variables remained as independent predictors of survival, including radiomics (p = 0.004), age (p = 0.039), and MGMT status (p = 0.025). This model yielded diagnostic performance (AUC/sensitivity/specificity) of 0.77/81.0/66.0 (training) and 0.89/100/78.6 (testing) in determination of survival ≥ 18 months. Conclusions: Results show that our radiogenomic model generated from radiomic features at baseline MRI, age, and MGMT status can predict survival ≥ 18 months in patients with GBM.
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Zováthi, Bendegúz H., Réka Mohácsi, Attila Marcell Szász, and György Cserey. "Breast Tumor Tissue Segmentation with Area-Based Annotation Using Convolutional Neural Network." Diagnostics 12, no. 9 (September 6, 2022): 2161. http://dx.doi.org/10.3390/diagnostics12092161.
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In this paper, we propose a novel approach to segment tumor and normal regions in human breast tissues. Cancer is the second most common cause of death in our society; every eighth woman will be diagnosed with breast cancer in her life. Histological diagnosis is key in the process where oncotherapy is administered. Due to the time-consuming analysis and the lack of specialists alike, obtaining a timely diagnosis is often a difficult process in healthcare institutions, so there is an urgent need for improvement in diagnostics. To reduce costs and speed up the process, an automated algorithm could aid routine diagnostics. We propose an area-based annotation approach generalized by a new rule template to accurately solve high-resolution biological segmentation tasks in a time-efficient way. These algorithm and implementation rules provide an alternative solution for pathologists to make decisions as accurate as manually. This research is based on an individual database from Semmelweis University, containing 291 high-resolution, bright field microscopy breast tumor tissue images. A total of 70% of the 128 × 128-pixel resolution images (206,174 patches) were used for training a convolutional neural network to learn the features of normal and tumor tissue samples. The evaluation of the small regions results in high-resolution histopathological image segmentation; the optimal parameters were calculated on the validation dataset (29 images, 10%), considering the accuracy and time factor as well. The algorithm was tested on the test dataset (61 images, 20%), reaching a 99.10% f1 score on pixel level evaluation within 3 min on average. Besides the quantitative analyses, the system’s accuracy was measured qualitatively by a histopathologist, who confirmed that the algorithm was also accurate in regions not annotated before.
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Zhang, Xiaoxuan, Xiongfeng Zhu, Kai Tang, Yinghua Zhao, Zixiao Lu, and Qianjin Feng. "DDTNet: A dense dual-task network for tumor-infiltrating lymphocyte detection and segmentation in histopathological images of breast cancer." Medical Image Analysis 78 (May 2022): 102415. http://dx.doi.org/10.1016/j.media.2022.102415.
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Hosainey, Sayied Abdol Mohieb, David Bouget, Ingerid Reinertsen, Lisa Millgård Sagberg, Sverre Helge Torp, Asgeir Store Jakola, and Ole Solheim. "Are there predilection sites for intracranial meningioma? A population-based atlas." Neurosurgical Review 45, no. 2 (October 21, 2021): 1543–52. http://dx.doi.org/10.1007/s10143-021-01652-9.
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Abstract Meningioma is the most common benign intracranial tumor and is believed to arise from arachnoid cap cells of arachnoid granulations. We sought to develop a population-based atlas from pre-treatment MRIs to explore the distribution of intracranial meningiomas and to explore risk factors for development of intracranial meningiomas in different locations. All adults (≥ 18 years old) diagnosed with intracranial meningiomas and referred to the department of neurosurgery from a defined catchment region between 2006 and 2015 were eligible for inclusion. Pre-treatment T1 contrast-enhanced MRI-weighted brain scans were used for semi-automated tumor segmentation to develop the meningioma atlas. Patient variables used in the statistical analyses included age, gender, tumor locations, WHO grade and tumor volume. A total of 602 patients with intracranial meningiomas were identified for the development of the brain tumor atlas from a wide and defined catchment region. The spatial distribution of meningioma within the brain is not uniform, and there were more tumors in the frontal region, especially parasagittally, along the anterior part of the falx, and on the skull base of the frontal and middle cranial fossa. More than 2/3 meningioma patients were females (p < 0.001) who also were more likely to have multiple meningiomas (p < 0.01), while men more often have supratentorial meningiomas (p < 0.01). Tumor location was not associated with age or WHO grade. The distribution of meningioma exhibits an anterior to posterior gradient in the brain. Distribution of meningiomas in the general population is not dependent on histopathological WHO grade, but may be gender-related.
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Bundschuh, Lena, Vesna Prokic, Matthias Guckenberger, Stephanie Tanadini-Lang, Markus Essler, and Ralph A. Bundschuh. "A Novel Radiomics-Based Tumor Volume Segmentation Algorithm for Lung Tumors in FDG-PET/CT after 3D Motion Correction—A Technical Feasibility and Stability Study." Diagnostics 12, no. 3 (February 23, 2022): 576. http://dx.doi.org/10.3390/diagnostics12030576.
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Positron emission tomography (PET) provides important additional information when applied in radiation therapy treatment planning. However, the optimal way to define tumors in PET images is still undetermined. As radiomics features are gaining more and more importance in PET image interpretation as well, we aimed to use textural features for an optimal differentiation between tumoral tissue and surrounding tissue to segment-target lesions based on three textural parameters found to be suitable in previous analysis (Kurtosis, Local Entropy and Long Zone Emphasis). Intended for use in radiation therapy planning, this algorithm was combined with a previously described motion-correction algorithm and validated in phantom data. In addition, feasibility was shown in five patients. The algorithms provided sufficient results for phantom and patient data. The stability of the results was analyzed in 20 consecutive measurements of phantom data. Results for textural feature-based algorithms were slightly worse than those of the threshold-based reference algorithm (mean standard deviation 1.2%—compared to 4.2% to 8.6%) However, the Entropy-based algorithm came the closest to the real volume of the phantom sphere of 6 ccm with a mean measured volume of 26.5 ccm. The threshold-based algorithm found a mean volume of 25.0 ccm. In conclusion, we showed a novel, radiomics-based tumor segmentation algorithm in FDG-PET with promising results in phantom studies concerning recovered lesion volume and reasonable results in stability in consecutive measurements. Segmentation based on Entropy was the most precise in comparison with sphere volume but showed the worst stability in consecutive measurements. Despite these promising results, further studies with larger patient cohorts and histopathological standards need to be performed for further validation of the presented algorithms and their applicability in clinical routines. In addition, their application in other tumor entities needs to be studied.
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Zhou, Wentong, Ziheng Deng, Yong Liu, Hui Shen, Hongwen Deng, and Hongmei Xiao. "Global Research Trends of Artificial Intelligence on Histopathological Images: A 20-Year Bibliometric Analysis." International Journal of Environmental Research and Public Health 19, no. 18 (September 15, 2022): 11597. http://dx.doi.org/10.3390/ijerph191811597.
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Cancer has become a major threat to global health care. With the development of computer science, artificial intelligence (AI) has been widely applied in histopathological images (HI) analysis. This study analyzed the publications of AI in HI from 2001 to 2021 by bibliometrics, exploring the research status and the potential popular directions in the future. A total of 2844 publications from the Web of Science Core Collection were included in the bibliometric analysis. The country/region, institution, author, journal, keyword, and references were analyzed by using VOSviewer and CiteSpace. The results showed that the number of publications has grown rapidly in the last five years. The USA is the most productive and influential country with 937 publications and 23,010 citations, and most of the authors and institutions with higher numbers of publications and citations are from the USA. Keyword analysis showed that breast cancer, prostate cancer, colorectal cancer, and lung cancer are the tumor types of greatest concern. Co-citation analysis showed that classification and nucleus segmentation are the main research directions of AI-based HI studies. Transfer learning and self-supervised learning in HI is on the rise. This study performed the first bibliometric analysis of AI in HI from multiple indicators, providing insights for researchers to identify key cancer types and understand the research trends of AI application in HI.
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Jaber, Mustafa I., Christopher W. Szeto, Bing Song, Liudmila Beziaeva, Stephen C. Benz, Patrick Soon-Shiong, and Shahrooz Rabizadeh. "Pathology image-based lung cancer subtyping using deeplearning features and cell-density maps." Electronic Imaging 2020, no. 10 (January 26, 2020): 64–1. http://dx.doi.org/10.2352/issn.2470-1173.2020.10.ipas-064.
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In this paper, we propose a patch-based system to classify non-small cell lung cancer (NSCLC) diagnostic whole slide images (WSIs) into two major histopathological subtypes: adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC). Classifying patients accurately is important for prognosis and therapy decisions. The proposed system was trained and tested on 876 subtyped NSCLC gigapixel-resolution diagnostic WSIs from 805 patients – 664 in the training set and 141 in the test set. The algorithm has modules for: 1) auto-generated tumor/non-tumor masking using a trained residual neural network (ResNet34), 2) cell-density map generation (based on color deconvolution, local drain segmentation, and watershed transformation), 3) patch-level feature extraction using a pre-trained ResNet34, 4) a tower of linear SVMs for different cell ranges, and 5) a majority voting module for aggregating subtype predictions in unseen testing WSIs. The proposed system was trained and tested on several WSI magnifications ranging from x4 to x40 with a best ROC AUC of 0.95 and an accuracy of 0.86 in test samples. This fully-automated histopathology subtyping method outperforms similar published state-of-the-art methods for diagnostic WSIs.
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Kurczyk, Agata, Marta Gawin, Piotr Paul, Ewa Chmielik, Tomasz Rutkowski, Monika Pietrowska, and Piotr Widłak. "Prognostic Value of Molecular Intratumor Heterogeneity in Primary Oral Cancer and Its Lymph Node Metastases Assessed by Mass Spectrometry Imaging." Molecules 27, no. 17 (August 25, 2022): 5458. http://dx.doi.org/10.3390/molecules27175458.
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Different aspects of intra-tumor heterogeneity (ITH), which are associated with the development of cancer and its response to treatment, have postulated prognostic value. Here we searched for potential association between phenotypic ITH analyzed by mass spectrometry imaging (MSI) and prognosis of head and neck cancer. The study involved tissue specimens resected from 77 patients with locally advanced oral squamous cell carcinoma, including 37 patients where matched samples of primary tumor and synchronous lymph node metastases were analyzed. A 3-year follow-up was available for all patients which enabled their separation into two groups: with no evidence of disease (NED, n = 41) and with progressive disease (PD, n = 36). After on-tissue trypsin digestion, peptide maps of all cancer regions were segmented using an unsupervised approach to reveal their intrinsic heterogeneity. We found that intra-tumor similarity of spectra was higher in the PD group and diversity of clusters identified during image segmentation was higher in the NED group, which indicated a higher level of ITH in patients with more favorable outcomes. Signature of molecular components that correlated with long-term outcomes could be associated with proteins involved in the immune functions. Furthermore, a positive correlation between ITH and histopathological lymphocytic host response was observed. Hence, we proposed that a higher level of ITH revealed by MSI in cancers with a better prognosis could reflect the presence of heterotypic components of tumor microenvironment such as infiltrating immune cells enhancing the response to the treatment.
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Eminaga, Okyaz, Mahmoud Abbas, Axel Semjonow, James D. Brooks, and Daniel Rubin. "Determination of biologic and prognostic feature scores from whole slide histology images using deep learning." Journal of Clinical Oncology 38, no. 15_suppl (May 20, 2020): e17527-e17527. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.e17527.
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e17527 Background: In cancer, histopathology is a reflection of the underlying molecular changes in the cancer cells and provides prognostic information on the risk of disease progression. Therefore, whole slide images may harbor histopathological features that have a biological association and are prognostic. Methods: This study has extracted histopathological feature scores generated from hematoxylin and eosin (HE) histology images based on deep learning models developed for the detection of pathological findings related to prostate cancer (PCa). Correlation analyses between the histopathological feature scores and the most relevant genomic alterations related to PCa were performed based on the original results and diagnostic histology images from TCGA PRAD study (n = 251). We extracted feature scores from tumor lesions after applying tumor segmentation and several data transformation using five models developed for detection of cribriform or ductal morphologies, Gleason patterns 3 and 4, and the presumed tumor precursor. For prognostic evaluation, we performed survival analyses of 371 patients from the TCGA PRAD dataset with biochemical recurrence (BCR) using a Cox regression model, Kaplan Meier (KM) curves. We applied the bootstrapping resampling for the uncertainty evaluation and C-statistics for the randomness measurement. Results: The feature scores were significantly correlated with the androgen receptor protein expression, an androgen-signaling score, mRNA expression, and androgen receptor splice variant 7. In addition, feature scores were associated with SPINK1 overexpression, the heterozygous loss of TP53, and SPOP mutations. Additionally, the mRNA and miRNA clusters identified by the TCGA research team for PCa. These features were independent of Gleason grade and were non-random. The survival analyses revealed that a model, including three of five feature scores, achieved a c-index of 0.706 (95% CI: 0.606-0.779). The KM curve showed that these risk groups based on the Cox regression model are significantly discriminative (Log-rank P-value < 0.0001). The low-risk group (n = 177) achieved a 2-year BCR-free survival rate (BFS) of 97.4% (95% CI: 94.9 - 100.0%) and a 5-year PFS of 88.3% (95% CI: 80.6 - 96.7%). In contrast, the high-risk group (n = 194) showed a 2-year PFS of 86.3% (95% CI: 81.1 - 91.8%) and a 5-year BFS of 66.9% (95% CI: 54.6 - 0.82.1%). Conclusions: Our findings uncover the potential of feature scores from histology images as digital biomarkers in precision medicine and as an expanding utility for digital pathology.
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Jung, Jiyoon, Eunsu Kim, Hyeseong Lee, Sung Hak Lee, and Sangjeong Ahn. "Automated Hybrid Model for Detecting Perineural Invasion in the Histology of Colorectal Cancer." Applied Sciences 12, no. 18 (September 13, 2022): 9159. http://dx.doi.org/10.3390/app12189159.
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Perineural invasion (PNI) is a well-established independent prognostic factor for poor outcomes in colorectal cancer (CRC). However, PNI detection in CRC is a cumbersome and time-consuming process, with low inter-and intra-rater agreement. In this study, a deep-learning-based approach was proposed for detecting PNI using histopathological images. We collected 530 regions of histology from 77 whole-slide images (PNI, 100 regions; non-PNI, 430 regions) for training. The proposed hybrid model consists of two components: a segmentation network for tumor and nerve tissues, and a PNI classifier. Unlike a “black-box” model that is unable to account for errors, the proposed approach enables false predictions to be explained and addressed. We presented a high performance, automated PNI detector, with the area under the curve (AUC) for the receiver operating characteristic (ROC) curve of 0.92. Thus, the potential for the use of deep neural networks in PNI screening was proved, and a possible alternative to conventional methods for the pathologic diagnosis of CRC was provided.
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Liu, Yan, Fadila Zerka, Sylvain Bodard, Mehdi Felfli, Charles Voyton, Alexandre Thinnes, Sebastien Jacques, and Antoine Iannessi. "CT based radiomics signature for phenotyping histopathological subtype in patients with non-small cell lung cancer." Journal of Clinical Oncology 41, no. 16_suppl (June 1, 2023): e20599-e20599. http://dx.doi.org/10.1200/jco.2023.41.16_suppl.e20599.
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e20599 Background: The determination of the histopathology of non-small cell lung cancer (NSCLC) is crucial for guiding the appropriate therapeutic strategy, affecting prognosis and recurrence rates. In addition, targetable oncologic mutations are highly correlated to histological subtypes. Conventional methods such as biopsy or surgical excision are the primary methods for histology determination but are invasive, costly, and have limitations such as sampling error. Computed tomography (CT) scans, widely used for NSCLC diagnosis and follow-up, offer a non-invasive alternative through radiomics-based models that provide comprehensive analysis of the entire tumor and surrounding tissue, improving patient selection and stratification in clinical trials and enhancing the development of molecularly targeted drugs. The goal of this study was to evaluate the ability of a CT-based radiomics model to predict the subtype histopathology of NSCLC patients. Methods: A total of 678 patients with advanced-stage NSCLC with at least one measurable lung lesion (≥10mm) were selected from multicenter institutions; 531 were used for training and 147 for independent testing. The test set originates from centers unique from the training cohort. Semi-automatic segmentation of the lung lesions was performed and the segmentations were retrospectively reviewed by an experienced radiologist with > 20 years of experience. A total of 1246 radiomics features were extracted using the Pyradiomics package. Ten Robust features were selected first by removing all features with variances near zero, then by applying Maximum Relevance — Minimum Redundancy to the remaining features. All selected features were standardized before modeling. A support vector machines classifier was trained using a five-fold cross-validation and Random Over-Sampling Examples to classify the histology of NSCLC patients (Squamous vs. Non-Squamous). Results: The model trained with the radiomics features achieved a mean area under the curve (AUC) of 0.80 (95% CI, ±0.05) on the cross-validation and an AUC of 0.77 (95% CI, ±0.05) on the test set. Conclusions: Results showed that the model was able to accurately classify the histology of NSCLC patients as squamous vs. non-squamous in a multicenter setting. It highlights the promise of CT-based radiomics in determining the histopathology or other molecular biomarkers of NSCLC, offering a more efficient, cost-effective, and less invasive alternative to traditional tissue analysis methods. Our findings suggest that this non-invasive method has the potential to improve patient selection and stratification and enhance the development of molecularly targeted drugs. These results underscore the importance of incorporating advanced imaging techniques and machine learning algorithms into oncology practice for better patient outcomes.
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Talwar, Vineet, Kundan Singh Chufal, and Srujana Joga. "Artificial Intelligence: A New Tool in Oncologist's Armamentarium." Indian Journal of Medical and Paediatric Oncology 42, no. 06 (December 2021): 511–17. http://dx.doi.org/10.1055/s-0041-1735577.
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AbstractArtificial intelligence (AI) has become an essential tool in human life because of its pivotal role in communications, transportation, media, and social networking. Inspired by the complex neuronal network and its functions in human beings, AI, using computer-based algorithms and training, had been explored since the 1950s. To tackle the enormous amount of patients' clinical data, imaging, histopathological data, and the increasing pace of research on new treatments and clinical trials, and ever-changing guidelines for treatment with the advent of novel drugs and evidence, AI is the need of the hour. There are numerous publications and active work on AI's role in the field of oncology. In this review, we discuss the fundamental terminology of AI, its applications in oncology on the whole, and its limitations. There is an inter-relationship between AI, machine learning and, deep learning. The virtual branch of AI deals with machine learning. While the physical branch of AI deals with the delivery of different forms of treatment—surgery, targeted drug delivery, and elderly care. The applications of AI in oncology include cancer screening, diagnosis (clinical, imaging, and histopathological), radiation therapy (image acquisition, tumor and organs at risk segmentation, image registration, planning, and delivery), prediction of treatment outcomes and toxicities, prediction of cancer cell sensitivity to therapeutics and clinical decision-making. A specific area of interest is in the development of effective drug combinations tailored to every patient and tumor with the help of AI. Radiomics, the new kid on the block, deals with the planning and administration of radiotherapy. As with any new invention, AI has its fallacies. The limitations include lack of external validation and proof of generalizability, difficulty in data access for rare diseases, ethical and legal issues, no precise logic behind the prediction, and last but not the least, lack of education and expertise among medical professionals. A collaboration between departments of clinical oncology, bioinformatics, and data sciences can help overcome these problems in the near future.
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Khalil, Muhammad-Adil, Yu-Ching Lee, Huang-Chun Lien, Yung-Ming Jeng, and Ching-Wei Wang. "Fast Segmentation of Metastatic Foci in H&E Whole-Slide Images for Breast Cancer Diagnosis." Diagnostics 12, no. 4 (April 14, 2022): 990. http://dx.doi.org/10.3390/diagnostics12040990.
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Breast cancer is the leading cause of death for women globally. In clinical practice, pathologists visually scan over enormous amounts of gigapixel microscopic tissue slide images, which is a tedious and challenging task. In breast cancer diagnosis, micro-metastases and especially isolated tumor cells are extremely difficult to detect and are easily neglected because tiny metastatic foci might be missed in visual examinations by medical doctors. However, the literature poorly explores the detection of isolated tumor cells, which could be recognized as a viable marker to determine the prognosis for T1NoMo breast cancer patients. To address these issues, we present a deep learning-based framework for efficient and robust lymph node metastasis segmentation in routinely used histopathological hematoxylin–eosin-stained (H–E) whole-slide images (WSI) in minutes, and a quantitative evaluation is conducted using 188 WSIs, containing 94 pairs of H–E-stained WSIs and immunohistochemical CK(AE1/AE3)-stained WSIs, which are used to produce a reliable and objective reference standard. The quantitative results demonstrate that the proposed method achieves 89.6% precision, 83.8% recall, 84.4% F1-score, and 74.9% mIoU, and that it performs significantly better than eight deep learning approaches, including two recently published models (v3_DCNN and Xception-65), and three variants of Deeplabv3+ with three different backbones, namely, U-Net, SegNet, and FCN, in precision, recall, F1-score, and mIoU (p<0.001). Importantly, the proposed system is shown to be capable of identifying tiny metastatic foci in challenging cases, for which there are high probabilities of misdiagnosis in visual inspection, while the baseline approaches tend to fail in detecting tiny metastatic foci. For computational time comparison, the proposed method takes 2.4 min for processing a WSI utilizing four NVIDIA Geforce GTX 1080Ti GPU cards and 9.6 min using a single NVIDIA Geforce GTX 1080Ti GPU card, and is notably faster than the baseline methods (4-times faster than U-Net and SegNet, 5-times faster than FCN, 2-times faster than the 3 different variants of Deeplabv3+, 1.4-times faster than v3_DCNN, and 41-times faster than Xception-65).
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Grewal, Mahip, Taha Ahmed, and Ammar Asrar Javed. "Current state of radiomics in hepatobiliary and pancreatic malignancies." Artificial Intelligence Surgery 3, no. 4 (November 28, 2023): 217–32. http://dx.doi.org/10.20517/ais.2023.28.
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Rising in incidence, hepatobiliary and pancreatic (HPB) cancers continue to exhibit dismal long-term survival. The overall poor prognosis of HPB cancers is reflective of the advanced stage at which most patients are diagnosed. Late diagnosis is driven by the often-asymptomatic nature of these diseases, as well as a dearth of screening modalities. Additionally, standard imaging modalities fall short of providing accurate and detailed information regarding specific tumor characteristics, which can better inform surgical planning and sequencing of systemic therapy. Therefore, precise therapeutic planning must be delayed until histopathological examination is performed at the time of resection. Given the current shortcomings in the management of HPB cancers, investigations of numerous noninvasive biomarkers, including circulating tumor cells and DNA, proteomics, immunolomics, and radiomics, are underway. Radiomics encompasses the extraction and analysis of quantitative imaging features. Along with summarizing the general framework of radiomics, this review synthesizes the state of radiomics in HPB cancers, outlining its role in various aspects of management, present limitations, and future applications for clinical integration. Current literature underscores the utility of radiomics in early detection, tumor characterization, therapeutic selection, and prognostication for HPB cancers. Seeing as single-center, small studies constitute the majority of radiomics literature, there is considerable heterogeneity with respect to steps of the radiomics workflow such as segmentation, or delineation of the region of interest on a scan. Nonetheless, the introduction of the radiomics quality score (RQS) demonstrates a step towards greater standardization and reproducibility in the young field of radiomics. Altogether, in the setting of continually improving artificial intelligence algorithms, radiomics represents a promising biomarker avenue for promoting enhanced and tailored management of HPB cancers, with the potential to improve long-term outcomes for patients.
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Rigamonti, Alessandra, Marika Viatore, Rebecca Polidori, Marco Erreni, Maria Fumagalli, Daoud Rahal, Massimo Locati, Alberto Mantovani, and Federica Marchesi. "Abstract 5783: Integration of AI-powered digital pathology and imaging mass cytometry to identify relevant features of the tumor microenvironment." Cancer Research 83, no. 7_Supplement (April 4, 2023): 5783. http://dx.doi.org/10.1158/1538-7445.am2023-5783.
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Abstract Digital pathology coupled to artificial intelligence (AI)-powered approaches are receiving great attention in the oncoimmunology field, as their adoption holds promise to improve current diagnostic workflows and potentiate the analytic outputs. In this work, we aimed at combining different histopathological approaches and AI-aided analytic tools to analyze the ecosystem of tumor tissues. By deploying AI-powered standard H&E and high-dimensional imaging-mass cytometry (IMC) to FFPE tissue samples, we could extract quantitative and standardized features that couldn’t have been easily identified and integrated by eye. One tissue microarray (TMA) slide containing 108 spots of NSCLC specimens (both adenocarcinoma and squamous carcinoma) was stained with H&E and scanned through the Axio Scan.Z1 (ZEISS) to generate high-quality virtual images. A deep learning algorithm was trained and applied to H&E images to identify tumor cells. The consecutive tissue section was stained with metal-labeled antibodies and processed through the Hyperion workflow (StandardBiotools), allowing quantitative detection of a panel of 23 markers related to tumor cells (Pan-cytokeratin), tissue architecture (aSMA, Vimentin, CD31, Collagen I, nuclei), CD45+ immune cells, comprehensive of myeloid cells (CD68, CD14, CD16, CD163, CD63, CCR4), lymphoid cells (CD3, CD4, CD8, FOXP3, CD20) and immune activation (S100A8, HLA-DR, Granzyme-B, KI67, Arginase-1). Data were exported as MCD files, visualized using the MCD viewer and further analyzed with the Qupath software. Cell segmentation was performed by the CellProfiler and Ilastik softwares and main cell populations were identified by a supervised approach through Cytomap. On H&E images, we generated a classifier of tumor heterogeneity, by exploring the spatial localization of tumor cells with the K-function summary statistic, which analyzes the distribution of tumor cells as a function of their distance. The resulting K-score value was then used to classify each tumor spot as diffuse, poorly clustered or highly clustered. Multiparametric computational analysis of the IMC images allowed to grasp immune and stromal classifiers, including frequency of immune cell populations in the tumor nests versus fibrotic stroma and immune cell interactions. In conclusion, AI-powered analysis of H&E slides is a robust approach that can improve manual scoring and unlock tissue relevant features opening to new diagnostic possibilities. Meanwhile, the analysis of the immune ecosystem by multiparametric imaging mass cytometry allows investigating spatial patterns and cell interactions at single-cell level. Integration of these approaches is feasible and allows the identification of tumor patient profiles with clinical relevance. Citation Format: Alessandra Rigamonti, Marika Viatore, Rebecca Polidori, Marco Erreni, Maria Fumagalli, Daoud Rahal, Massimo Locati, Alberto Mantovani, Federica Marchesi. Integration of AI-powered digital pathology and imaging mass cytometry to identify relevant features of the tumor microenvironment. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5783.
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Wu, Wei, Lauren Cech, Victor Olivas, Aubhishek Zaman, Daniel Lucas Kerr, and Trever G. Bivona. "Deep learning-based characterization of the drug tolerant persister cell state in lung cancer." JCO Global Oncology 9, Supplement_1 (August 2023): 141. http://dx.doi.org/10.1200/go.2023.9.supplement_1.141.
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141 Background: Lung cancer is the leading cause of cancer-related lethality globally. Targeted therapies improve the clinical outcome of cancer treatment; however, a subpopulation of cancer cells survive during initial therapy and evolve into drug tolerant persister cells (DTPs) that maintain a residual disease reservoir. Residual disease preludes acquired resistance and tumor progression; therefore, identifying and eliminating DTPs could benefit future treatment paradigms. We have shown that Hippo pathway effector YAP1 (Yes Associated Protein-1) is activated in oncogene-driven lung cancers when cancer cells are exposed to various targeted therapies, such as EGFR, ALK, and RAS inhibitors. YAP1 transcriptional activation during targeted therapy is characterized by its increased nuclear localization and interaction with transcription factors. This activation promotes the expression of genes involved in cell survival, cellular plasticity, and metabolic reprogramming at the residual disease state. We hypothesized that detection of intrinsic or acquired persister cells may aid the development of optimized treatment regimens. In this study, we are building image-based deep learning models to identify YAP1 activation-mediated DTPs from histologically stained slides. Methods: H&E or YAP-stained immunohistochemistry (IHC) images from clinical lung cancer and patient-derived tumor xenograft samples were collected throughout targeted therapy and were annotated with semi-automation using high performance computing clusters. A deep learning model (U-Net algorithm) was used for image segmentation, training, validation, and testing. Results: 1638 images were annotated and over 80,000 patches from these images for YAP positive cells (or regions of interest) comprised the training dataset with semi-supervised automation. Subsequently, we built a customized deep-learning model to detect YAP-mediated DTP cell states from whole histopathological image slides. The deep learning-based model achieved excellent accuracy of 0.8238 and 0.9091 in training, and 0.8040 and 0.8949 in validation datasets for two different annotations, respectively. For a test dataset, the model obtained 0.81 and 0.902 accuracy for the two annotations, respectively. Conclusions: We have constructed a deep learning convolutional neural network model to infer the presence of the YAP1 activation-mediated drug tolerant persister cell state prior to or during targeted treatment of lung cancer. Implementing our AI-based model into routine lung cancer care in the future could identify patient subpopulations with YAP1 activated tumors who would most benefit from receiving YAP1-targeted small molecule inhibitors.
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Di Dio, Michele, Simona Barbuto, Claudio Bisegna, Andrea Bellin, Mario Boccia, Daniele Amparore, Paolo Verri, et al. "Artificial Intelligence-Based Hyper Accuracy Three-Dimensional (HA3D®) Models in Surgical Planning of Challenging Robotic Nephron-Sparing Surgery: A Case Report and Snapshot of the State-of-the-Art with Possible Future Implications." Diagnostics 13, no. 14 (July 10, 2023): 2320. http://dx.doi.org/10.3390/diagnostics13142320.
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Recently, 3D models (3DM) gained popularity in urology, especially in nephron-sparing interventions (NSI). Up to now, the application of artificial intelligence (AI) techniques alone does not allow us to obtain a 3DM adequate to plan a robot-assisted partial nephrectomy (RAPN). Integration of AI with computer vision algorithms seems promising as it allows to speed up the process. Herein, we present a 3DM realized with the integration of AI and a computer vision approach (CVA), displaying the utility of AI-based Hyper Accuracy Three-dimensional (HA3D®) models in preoperative planning and intraoperative decision-making process of challenging robotic NSI. A 54-year-old Caucasian female with no past medical history was referred to the urologist for incidental detection of the right renal mass. Preoperative contrast-enhanced abdominal CT confirmed a 35 × 25 mm lesion on the anterior surface of the upper pole (PADUA 7), with no signs of distant metastasis. CT images in DICOM format were processed to obtain a HA3D® model. RAPN was performed using Da Vinci Xi surgical system in a three-arm configuration. The enucleation strategy was achieved after selective clamping of the tumor-feeding artery. Overall operative time was 85 min (14 min of warm ischemia time). No intra-, peri- and post-operative complications were recorded. Histopathological examination revealed a ccRCC (stage pT1aNxMx). AI is breaking new ground in medical image analysis panorama, with enormous potential in organ/tissue classification and segmentation, thus obtaining 3DM automatically and repetitively. Realized with the integration of AI and CVA, the results of our 3DM were accurate as demonstrated during NSI, proving the potentialities of this approach for HA3D® models’ reconstruction.
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Pasello, Giulia, Alessandra Ferro, Elena Scagliori, Gisella Gennaro, Matilde Costa, Matteo Sepulcri, Marco Schiavon, et al. "Exploratory radiomic analysis of stage III non-small cell lung cancer CT images: Correlation with clinical-pathological characteristics." Journal of Clinical Oncology 40, no. 16_suppl (June 1, 2022): e20574-e20574. http://dx.doi.org/10.1200/jco.2022.40.16_suppl.e20574.
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e20574 Background: Recent evidences have suggested potential applications of radiomics in early diagnosis, prognostic stratification and treatment outcome prediction of Non-Small Cell Lung Cancer (NSCLC) patients. The purpose of this study is to evaluate the ability of radiomic analysis to discriminate between different clinical-pathological conditions in patients with stage III NSCLC. Methods: Baseline CT studies from 59 patients with stage III NSCLC referred to our Institution from 2010 and 2020 were retrospectively reviewed, and the segmentation of the main lung lesion and the extraction of 517 radiomic features performed using a commercial software. The number of features was reduced to 46 by means of principal component analysis applied using the R package “RadAR” (Radiomics Analysis with R). The Kruskal-Wallis test was applied to all the radiomic features in order to evaluate which of them can discriminate between 7 clinical dichotomous characteristics: tumor stage, type, presence of mutation, treatment response, relapse free survival (RFS), smoking habit, patient outcome. P < 0.05 means that there is a statistically significant difference between the two subgroups. Results: The median age at diagnosis was 69 years (range 43-83). Most patients were males (40/59 = 67.8%) and heavy smokers (36/59 = 61.0%). Adenocarcinoma was the most common histology (41/59 = 70.7%), while cases were almost equally splitted between stage IIIA (45.8%) and stage IIIB or IIIC (54.2%). Most selected radiomic features (29/46 = 63.0%) showed a statistically significant difference between patients with and without mutations. Ten (10/46 = 21.7%) radiomic features were associated with patient sex. Seven features (7/45 = 15.2%) were “sensitive” to the tumor clinical stage (stage IIIA vs. stage IIIB+IIIC), 4 (4/46 = 8.7%) to the histological type, and 2 (2/46 = 4.3%) to the patient outcome. None of the selected radiomic features was able to discriminate between responder and non-responder patients, current/previous smokers and never smokers, and patients with RFS lower than 12 months versus RFS equal or higher than 12 months. Conclusions: This preliminary analysis showed that radiomics has the potential of identifying mathematical features associated with clinical and histopathological characteristics in stage III NSCLC patients, which might feed multiparametric predictive models. Larger datasets and further analysis are necessary in order to confirm initial results.
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Pasello, Giulia, Alessandra Ferro, Elena Scagliori, Gisella Gennaro, Matilde Costa, Matteo Sepulcri, Marco Schiavon, et al. "Exploratory radiomic analysis of stage III non-small cell lung cancer CT images: Correlation with clinical-pathological characteristics." Journal of Clinical Oncology 40, no. 16_suppl (June 1, 2022): e20574-e20574. http://dx.doi.org/10.1200/jco.2022.40.16_suppl.e20574.
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e20574 Background: Recent evidences have suggested potential applications of radiomics in early diagnosis, prognostic stratification and treatment outcome prediction of Non-Small Cell Lung Cancer (NSCLC) patients. The purpose of this study is to evaluate the ability of radiomic analysis to discriminate between different clinical-pathological conditions in patients with stage III NSCLC. Methods: Baseline CT studies from 59 patients with stage III NSCLC referred to our Institution from 2010 and 2020 were retrospectively reviewed, and the segmentation of the main lung lesion and the extraction of 517 radiomic features performed using a commercial software. The number of features was reduced to 46 by means of principal component analysis applied using the R package “RadAR” (Radiomics Analysis with R). The Kruskal-Wallis test was applied to all the radiomic features in order to evaluate which of them can discriminate between 7 clinical dichotomous characteristics: tumor stage, type, presence of mutation, treatment response, relapse free survival (RFS), smoking habit, patient outcome. P < 0.05 means that there is a statistically significant difference between the two subgroups. Results: The median age at diagnosis was 69 years (range 43-83). Most patients were males (40/59 = 67.8%) and heavy smokers (36/59 = 61.0%). Adenocarcinoma was the most common histology (41/59 = 70.7%), while cases were almost equally splitted between stage IIIA (45.8%) and stage IIIB or IIIC (54.2%). Most selected radiomic features (29/46 = 63.0%) showed a statistically significant difference between patients with and without mutations. Ten (10/46 = 21.7%) radiomic features were associated with patient sex. Seven features (7/45 = 15.2%) were “sensitive” to the tumor clinical stage (stage IIIA vs. stage IIIB+IIIC), 4 (4/46 = 8.7%) to the histological type, and 2 (2/46 = 4.3%) to the patient outcome. None of the selected radiomic features was able to discriminate between responder and non-responder patients, current/previous smokers and never smokers, and patients with RFS lower than 12 months versus RFS equal or higher than 12 months. Conclusions: This preliminary analysis showed that radiomics has the potential of identifying mathematical features associated with clinical and histopathological characteristics in stage III NSCLC patients, which might feed multiparametric predictive models. Larger datasets and further analysis are necessary in order to confirm initial results.
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Canola, Julio Carlos, and Fabrício Singaretti de Oliveira. "Three-dimensional magnetic resonance reconstruction images before and after surgical therapy of spontaneous canine brain tumors." Ciência Rural 37, no. 4 (August 2007): 1174–77. http://dx.doi.org/10.1590/s0103-84782007000400044.
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Specific software was used for reconstruction of spontaneous intracranial tumor volume from magnetic resonance images (MRI) in three dogs. Histopathologically confirmed meningioma, cystic meningioma, and choroid plexus tumors were evaluated before and after surgery. The software allowed the whole-volume segmentation of the skin, brain, tumor, edema, and cyst. Manipulation of the three-dimensional images (3D) allowed visualization of all anatomical structures, aided clinical understanding, surgical planning, and treatment monitoring.
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Dionisio, Fernando Carrasco Ferreira, Larissa Santos Oliveira, Mateus de Andrade Hernandes, Edgard Eduard Engel, Paulo Mazzoncini de Azevedo-Marques, and Marcello Henrique Nogueira-Barbosa. "Manual versus semiautomatic segmentation of soft-tissue sarcomas on magnetic resonance imaging: evaluation of similarity and comparison of segmentation times." Radiologia Brasileira 54, no. 3 (June 2021): 155–64. http://dx.doi.org/10.1590/0100-3984.2020.0028.
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Abstract Objective: To evaluate the degree of similarity between manual and semiautomatic segmentation of soft-tissue sarcomas on magnetic resonance imaging (MRI). Materials and Methods: This was a retrospective study of 15 MRI examinations of patients with histopathologically confirmed soft-tissue sarcomas acquired before therapeutic intervention. Manual and semiautomatic segmentations were performed by three radiologists, working independently, using the software 3D Slicer. The Dice similarity coefficient (DSC) and the Hausdorff distance were calculated in order to evaluate the similarity between manual and semiautomatic segmentation. To compare the two modalities in terms of the tumor volumes obtained, we also calculated descriptive statistics and intraclass correlation coefficients (ICCs). Results: In the comparison between manual and semiautomatic segmentation, the DSC values ranged from 0.871 to 0.973. The comparison of the volumes segmented by the two modalities resulted in ICCs between 0.9927 and 0.9990. The DSC values ranged from 0.849 to 0.979 for intraobserver variability and from 0.741 to 0.972 for interobserver variability. There was no significant difference between the semiautomatic and manual modalities in terms of the segmentation times (p > 0.05). Conclusion: There appears to be a high degree of similarity between manual and semiautomatic segmentation, with no significant difference between the two modalities in terms of the time required for segmentation.
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Alvarsson, Alexandra, Carl Storey, Brandy Olin Pope, Caleb Stoltzfus, Robert Vierkant, Jessica Tufariello, Aaron Bungum, et al. "Abstract 6624: 3D assessment of the lung cancer microenvironment using multi-resolution open-top light-sheet microscopy." Cancer Research 83, no. 7_Supplement (April 4, 2023): 6624. http://dx.doi.org/10.1158/1538-7445.am2023-6624.
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Abstract Background: Non-small cell lung cancer (NSCLC) tissue is a valuable resource for diagnosis, treatment planning, and drug development. Current 2D histopathological techniques introduce under-sampling error (i.e., a single 5 um section represents 0.5% of a 1 mm thick biopsy), interobserver variability, and fail to capture the biology contained within the entire tissue sample. We have developed a suite of technologies to stain, chemically clarify, image, visualize, and analyze entire intact NSCLC tissue samples. Methods: Human NSCLC tissue, stored frozen in OCT, was fixed in 4% paraformaldehyde, stained with nuclear (TOPRO-3) and general protein (Eosin) fluorescent dyes, and optically cleared using a modified iDISCO protocol with ethyl cinnamate as the refractive index matching solution. Whole, intact tissue samples, roughly 1-5mm3 in volume, were imaged at 2 microns/pixel resolution with an open-top light-sheet microscope (3Di, Alpenglow Biosciences). Smaller regions of interest (ROIs) with key pathologic features were reimaged at higher resolution, 0.17 microns/pixel, to reveal subnuclear features and for cell typing. Visualization was performed using Aivia software. Results: NSCLC tissue samples were successfully imaged in 3D. Low resolution images (2 microns/pixel) were obtained within 4-31 minutes, depending on the tissue volume. The 3D distribution of cancer cells, immune cells, vessels, and fibrosis varied substantially throughout the volume of the tissue. Recognizable histologic features, including nests of tumor cells surrounded by vasculature and immune cells, were readily visualized. Squamous and adenocarcinoma with its subtypes (solid, acinar, lepidic, and micropapillary) morphologies were recognizable in 2D optical sections of the 3D datasets. Imaging quality degraded in tissue deeper than 1 mm due to light scattering. Conclusion: We assessed intact NSCLC tissue samples measuring up to 5 mm3 using our custom light-sheet microscope and tissue clearing techniques. This novel method enables us to visualize key features of NSCLC such as the tumor interfaces, tertiary lymphoid structures, vessels and fibrosis in the entire tissue sample, preventing under sampling error, and potentially enabling new biologic insights. Next steps include segmentation and quantification of key tissue structures such as tumor volume, immune cell distribution, and fibrosis/immune cell exclusion. This proof-of-concept study provides motivation for further investigation into the significance of 3D tissue features in NSCLC tissue samples. Citation Format: Alexandra Alvarsson, Carl Storey, Brandy Olin Pope, Caleb Stoltzfus, Robert Vierkant, Jessica Tufariello, Aaron Bungum, Julia Naso, Cheuk Ki Chan, Eric Edell, Christopher Hartley, Janani Reisenauer, Nicholas Reder. 3D assessment of the lung cancer microenvironment using multi-resolution open-top light-sheet microscopy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6624.
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Hempel, Johann-Martin, Cornelia Brendle, Sasan Darius Adib, Felix Behling, Ghazaleh Tabatabai, Salvador Castaneda Vega, Jens Schittenhelm, Ulrike Ernemann, and Uwe Klose. "Glioma-Specific Diffusion Signature in Diffusion Kurtosis Imaging." Journal of Clinical Medicine 10, no. 11 (May 26, 2021): 2325. http://dx.doi.org/10.3390/jcm10112325.
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Purpose: This study aimed to assess the relationship between mean kurtosis (MK) and mean diffusivity (MD) values from whole-brain diffusion kurtosis imaging (DKI) parametric maps in preoperative magnetic resonance (MR) images from 2016 World Health Organization Classification of Tumors of the Central Nervous System integrated glioma groups. Methods: Seventy-seven patients with histopathologically confirmed treatment-naïve glioma were retrospectively assessed between 1 August 2013 and 30 October 2017. The area on scatter plots with a specific combination of MK and MD values, not occurring in the healthy brain, was labeled, and the corresponding voxels were visualized on the fluid-attenuated inversion recovery (FLAIR) images. Reversely, the labeled voxels were compared to those of the manually segmented tumor volume, and the Dice similarity coefficient was used to investigate their spatial overlap. Results: A specific combination of MK and MD values in whole-brain DKI maps, visualized on a two-dimensional scatter plot, exclusively occurs in glioma tissue including the perifocal infiltrative zone and is absent in tissue of the normal brain or from other intracranial compartments. Conclusions: A unique diffusion signature with a specific combination of MK and MD values from whole-brain DKI can identify diffuse glioma without any previous segmentation. This feature might influence artificial intelligence algorithms for automatic tumor segmentation and provide new aspects of tumor heterogeneity.
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de Matos, Jonathan, Steve Ataky, Alceu de Souza Britto, Luiz Soares de Oliveira, and Alessandro Lameiras Koerich. "Machine Learning Methods for Histopathological Image Analysis: A Review." Electronics 10, no. 5 (February 27, 2021): 562. http://dx.doi.org/10.3390/electronics10050562.
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Histopathological images (HIs) are the gold standard for evaluating some types of tumors for cancer diagnosis. The analysis of such images is time and resource-consuming and very challenging even for experienced pathologists, resulting in inter-observer and intra-observer disagreements. One of the ways of accelerating such an analysis is to use computer-aided diagnosis (CAD) systems. This paper presents a review on machine learning methods for histopathological image analysis, including shallow and deep learning methods. We also cover the most common tasks in HI analysis, such as segmentation and feature extraction. Besides, we present a list of publicly available and private datasets that have been used in HI research.
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Warman, Pranav, Syed M. Adil, Andreas Seas, Daniel Sexton, Evan Calabrese, Nandan P. Lad, Brad Kolls, et al. "381 Glioma Three-Dimensional Shape Predicts Underlying Genetic Mutations." Neurosurgery 70, Supplement_1 (April 2024): 115. http://dx.doi.org/10.1227/neu.0000000000002809_381.
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INTRODUCTION: Phenotype is the detectable expression of genotype. Despite this, little is known about how the three-dimensional structure of brain tumors correlates with their genetic biomarkers. METHODS: The UCSF-PDGM dataset was filtered for histopathologically-proven gliomas. Segmentation masks of the enhancing tumor, non-enhancing/necrotic tumor, and surrounding FLAIR abnormality were obtained from each patient’s preoperative brain MRI. All tumors were evaluated for IDH mutations and 1p/19q codeletion; all grade III and IV tumors were tested for MGMT methylation status. The segmentation masks were processed to create topological features describing the tumor’s 3D shape. These features, without other clinical variables, were used in a custom machine learning pipeline to predict the presence of IDH mutations, 1p/19q codeletion, and MGMT methylation. RESULTS: After filtration, 103 of 494 gliomas tested had an IDH mutation, 29 of 494 had a 1p/19q codeletion, and 247 of 409 had methylation of MGMT. On the blinded test-subset, the machine learning model, using only features describing shape, rendered an AUROC of 0.902 (95% CI: 0.875-0.929), specificity of 90.3% (83.9%-96.6%), and sensitivity of 75.7% (68.0%-83.3%) for IDH mutation. For 1p/19q codeletion, we found an AUROC of 0.949 (0.908-0.990), specificity of 94.5% (84.3%-100%), and sensitivity of 86.6% (79.6%-93.6%). For MGMT methylation, the performance was poor with an AUROC of 0.445 (0.385-0.504). CONCLUSIONS: The three-dimensional shape of a glioma may be used to predict the presence of some key underlying genetic mutations before biopsy. Additional research is needed to validate this, improve its fidelity, and generalize it to other biomarkers.
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Hulahan, Taylor S., Elizabeth N. Wallace, Siri H. Strand, Graham A. Colditz, E. Shelley Hwang, Robert West, Laura Spruill, Jeffrey Marks, Richard R. Drake, and Peggi M. Angel. "Abstract P2-21-03: Unique Collagen Peptide Signatures between Ductal Carcinoma in Situ and Invasive Breast Cancer by Mass Spectrometry Tissue Imaging." Cancer Research 83, no. 5_Supplement (March 1, 2023): P2–21–03—P2–21–03. http://dx.doi.org/10.1158/1538-7445.sabcs22-p2-21-03.
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Abstract Ductal carcinoma in situ (DCIS) is characterized by inter-tumor heterogeneity that poses a therapeutic challenge due to its unpredictable recurrence and progression to invasive breast cancer (IBC). Recent publications have implicated the crucial role of the tumor microenvironment particularly stromal differences between DCIS patients who progress to IBC (progressors) and those that do not (non-progressors). However, spatial regulation of the collagen proteome has yet to be investigated in the context of disease progression in DCIS. In this study, we hypothesized that the collagen proteome was significantly altered between DCIS and IBC and that differentiating collagen peptide signatures could be related to clinical outcomes. Our initial studies investigated collagen peptide signatures in lumpectomies (n=13) annotated as DCIS, DCIS and invasive ductal carcinoma (IDC), or IDC only. We leveraged our previously published method for spatial imaging of collagen proteomics on tissue to report collagen types and collagen post-translational modifications including 40 other extracellular matrix (ECM) proteins involved in the regulation of collagen fibers. Over 1000 peaks were found to be linked to annotated pathologies or adjacent regions. Initial comparison of DCIS to IDC lesions demonstrated 63 differentially expressed peaks between these regions by unpaired, two-tailed t-test (p< 0.001). Image segmentation of the 315,541 pixels demonstrated 16 high-level hierarchical groups designating unique spatially localized ECM proteomic groups. Notably, these groups overlaid with histopathological features and pathological annotations. Next, we investigated collagen peptide signatures in a subset of DCIS samples from the Resource of Archival Breast Tissue (RAHBT) (n=37). Samples were histologically diverse within the tissue microarrays, with cribriform, micropapillary, papillary, solid, and comedo necrosis architectural patterns. In our preliminary analysis, we found two peptide peaks that could distinguish the solid subtype (n=22) from comedo necrosis (n=4) and one peak that could discriminate between the cribriform (n=8) and solid subtype (n=22) by area under the receiver operating curve (AUROC)≥0.75 and Wilson/Brown t-test (p< 0.05). Evaluated per clinical outcome, four ECM peptides showed significantly different peak intensities in progressors with IBC recurrence (n=7) compared to non-progressors (n=26) (AUROC≥0.75; Wilson/Brown t-test p< 0.05). One peptide had significantly different peak intensities between progressors with contralateral IBC recurrence and DCIS recurrence (n=5) and non-progressors (n=26) (AUROC≥0.75; Wilson/Brown t-test p< 0.05). Overall, the data suggest that unique collagen signatures in DCIS could be useful for understanding recurrence and progression to IBC. Further investigation of the spatial distribution of the collagen proteome within DCIS pathologies and relative to clinical outcome is warranted. Citation Format: Taylor S. Hulahan, Elizabeth N. Wallace, Siri H. Strand, Graham A. Colditz, E Shelley Hwang, Robert West, Laura Spruill, Jeffrey Marks, Richard R. Drake, Peggi M. Angel. Unique Collagen Peptide Signatures between Ductal Carcinoma in Situ and Invasive Breast Cancer by Mass Spectrometry Tissue Imaging [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P2-21-03.
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Hulahan, Taylor S., Elizabeth N. Wallace, Siri H. Strand, Robert Michael Angelo, Graham Colditz, Eun-Sil Shelley Hwang, Robert West, et al. "Abstract B019: Discrete regulation of the collagen proteome among pathological features in DCIS and invasive breast cancer by mass spectrometry tissue imaging." Cancer Prevention Research 15, no. 12_Supplement_1 (December 1, 2022): B019. http://dx.doi.org/10.1158/1940-6215.dcis22-b019.
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Abstract Ductal carcinoma in situ (DCIS) is characterized by inter-tumor heterogeneity that poses a therapeutic challenge due to its unpredictable recurrence and progression to invasive breast cancer (IBC). In recent publications, collagen stromal differences have been reported between patients that progressed to IBC (progressors) and those that did not (non-progressors). However, details on the role spatial regulation of the collagen proteome might play in this progression have yet to be studied. Here, we investigated the pathological distribution of collagen post-translational modifications in a cohort of patients classified as progressors with ipsilateral IBC recurrence compared to non-progressors. Previously published methods for collagen proteomics by targeted tissue mass spectrometry imaging were used. The method reports collagen types and post-translational modifications within the collagen triple-helical region as well as approximately 40 other extracellular matrix (ECM) proteins involved in the regulation of collagen fibers. Initial studies investigated collagen variation in lumpectomies (n=7) with DCIS, DCIS plus invasive ductal carcinoma (IDC) or IDC only. Over 590 peptides were found to be linked to annotated pathologies. A preliminary comparison of DCIS (n=2392 spectra) to IBC (n=4696 spectra) using area under the receiver operating curve (AUROC) ≥0.85 demonstrated that 47 peptides could individually discriminate between DCIS and IBC in this limited cohort. Image segmentation of the 405,652 pixels demonstrated 11 high-level hierarchical groups designating unique spatially localized ECM proteomic groups; these groups overlaid with histopathological features and pathological annotations. A total of 87 samples from the Resource of Archival Breast Tissue (RAHBT) matched with clinical characteristics were also investigated. Cores were histologically diverse within the tissue microarrays, with cribriform, micropapillary, papillary, solid, and comedo necrosis architectural patterns. Initial results suggest certain peptides may differentiate between non-progressors and progressors with ipsilateral IBC recurrence. Our current work focuses on correlating collagen signatures to mixed pathologies and the cellular content of cores. Further investigation of the collagen proteome is warranted. Overall, the data suggest unique collagen signatures in DCIS that could be useful for understanding recurrence and progression to IBC. Citation Format: Taylor S. Hulahan, Elizabeth N. Wallace, Siri H. Strand, Robert Michael Angelo, Graham Colditz, Eun-Sil Shelley Hwang, Robert West, Laura Spruill, Jeffrey R. Marks, Richard R. Drake, Peggi M. Angel. Discrete regulation of the collagen proteome among pathological features in DCIS and invasive breast cancer by mass spectrometry tissue imaging [abstract]. In: Proceedings of the AACR Special Conference on Rethinking DCIS: An Opportunity for Prevention?; 2022 Sep 8-11; Philadelphia, PA. Philadelphia (PA): AACR; Can Prev Res 2022;15(12 Suppl_1): Abstract nr B019.
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Xu, Rui, Zhizhen Wang, Zhenbing Liu, Chu Han, Lixu Yan, Huan Lin, Zeyan Xu, et al. "Histopathological Tissue Segmentation of Lung Cancer with Bilinear CNN and Soft Attention." BioMed Research International 2022 (July 7, 2022): 1–10. http://dx.doi.org/10.1155/2022/7966553.
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Automatic tissue segmentation in whole-slide images (WSIs) is a critical task in hematoxylin and eosin- (H&E-) stained histopathological images for accurate diagnosis and risk stratification of lung cancer. Patch classification and stitching the classification results can fast conduct tissue segmentation of WSIs. However, due to the tumour heterogeneity, large intraclass variability and small interclass variability make the classification task challenging. In this paper, we propose a novel bilinear convolutional neural network- (Bilinear-CNN-) based model with a bilinear convolutional module and a soft attention module to tackle this problem. This method investigates the intraclass semantic correspondence and focuses on the more distinguishable features that make feature output variations relatively large between interclass. The performance of the Bilinear-CNN-based model is compared with other state-of-the-art methods on the histopathological classification dataset, which consists of 107.7 k patches of lung cancer. We further evaluate our proposed algorithm on an additional dataset from colorectal cancer. Extensive experiments show that the performance of our proposed method is superior to that of previous state-of-the-art ones and the interpretability of our proposed method is demonstrated by Grad-CAM.
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Zadeh Shirazi, Amin, Mark D. McDonnell, Eric Fornaciari, Narjes Sadat Bagherian, Kaitlin G. Scheer, Michael S. Samuel, Mahdi Yaghoobi, et al. "A deep convolutional neural network for segmentation of whole-slide pathology images identifies novel tumour cell-perivascular niche interactions that are associated with poor survival in glioblastoma." British Journal of Cancer 125, no. 3 (April 29, 2021): 337–50. http://dx.doi.org/10.1038/s41416-021-01394-x.
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Abstract Background Glioblastoma is the most aggressive type of brain cancer with high-levels of intra- and inter-tumour heterogeneity that contribute to its rapid growth and invasion within the brain. However, a spatial characterisation of gene signatures and the cell types expressing these in different tumour locations is still lacking. Methods We have used a deep convolutional neural network (DCNN) as a semantic segmentation model to segment seven different tumour regions including leading edge (LE), infiltrating tumour (IT), cellular tumour (CT), cellular tumour microvascular proliferation (CTmvp), cellular tumour pseudopalisading region around necrosis (CTpan), cellular tumour perinecrotic zones (CTpnz) and cellular tumour necrosis (CTne) in digitised glioblastoma histopathological slides from The Cancer Genome Atlas (TCGA). Correlation analysis between segmentation results from tumour images together with matched RNA expression data was performed to identify genetic signatures that are specific to different tumour regions. Results We found that spatially resolved gene signatures were strongly correlated with survival in patients with defined genetic mutations. Further in silico cell ontology analysis along with single-cell RNA sequencing data from resected glioblastoma tissue samples showed that these tumour regions had different gene signatures, whose expression was driven by different cell types in the regional tumour microenvironment. Our results further pointed to a key role for interactions between microglia/pericytes/monocytes and tumour cells that occur in the IT and CTmvp regions, which may contribute to poor patient survival. Conclusions This work identified key histopathological features that correlate with patient survival and detected spatially associated genetic signatures that contribute to tumour-stroma interactions and which should be investigated as new targets in glioblastoma. The source codes and datasets used are available in GitHub: https://github.com/amin20/GBM_WSSM.
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AlGhamdi, Rayed. "Mitotic Nuclei Segmentation and Classification Using Chaotic Butterfly Optimization Algorithm with Deep Learning on Histopathology Images." Biomimetics 8, no. 6 (October 5, 2023): 474. http://dx.doi.org/10.3390/biomimetics8060474.
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Histopathological grading of the tumors provides insights about the patient’s disease conditions, and it also helps in customizing the treatment plans. Mitotic nuclei classification involves the categorization and identification of nuclei in histopathological images based on whether they are undergoing the cell division (mitosis) process or not. This is an essential procedure in several research and medical contexts, especially in diagnosis and prognosis of cancer. Mitotic nuclei classification is a challenging task since the size of the nuclei is too small to observe, while the mitotic figures possess a different appearance as well. Automated calculation of mitotic nuclei is a stimulating one due to their great similarity to non-mitotic nuclei and their heteromorphic appearance. Both Computer Vision (CV) and Machine Learning (ML) approaches are used in the automated identification and the categorization of mitotic nuclei in histopathological images that endure the procedure of cell division (mitosis). With this background, the current research article introduces the mitotic nuclei segmentation and classification using the chaotic butterfly optimization algorithm with deep learning (MNSC-CBOADL) technique. The main objective of the MNSC-CBOADL technique is to perform automated segmentation and the classification of the mitotic nuclei. In the presented MNSC-CBOADL technique, the U-Net model is initially applied for the purpose of segmentation. Additionally, the MNSC-CBOADL technique applies the Xception model for feature vector generation. For the classification process, the MNSC-CBOADL technique employs the deep belief network (DBN) algorithm. In order to enhance the detection performance of the DBN approach, the CBOA is designed for the hyperparameter tuning model. The proposed MNSC-CBOADL system was validated through simulation using the benchmark database. The extensive results confirmed the superior performance of the proposed MNSC-CBOADL system in the classification of mitotic nuclei.
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Jakola, Asgeir S., David Bouget, Ingerid Reinertsen, Anne J. Skjulsvik, Lisa Millgård Sagberg, Hans Kristian Bø, Sasha Gulati, Kristin Sjåvik, and Ole Solheim. "Spatial distribution of malignant transformation in patients with low-grade glioma." Journal of Neuro-Oncology 146, no. 2 (January 2020): 373–80. http://dx.doi.org/10.1007/s11060-020-03391-1.
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Abstract Background Malignant transformation represents the natural evolution of diffuse low-grade gliomas (LGG). This is a catastrophic event, causing neurocognitive symptoms, intensified treatment and premature death. However, little is known concerning the spatial distribution of malignant transformation in patients with LGG. Materials and methods Patients histopathological diagnosed with LGG and subsequent radiological malignant transformation were identified from two different institutions. We evaluated the spatial distribution of malignant transformation with (1) visual inspection and (2) segmentations of longitudinal tumor volumes. In (1) a radiological transformation site < 2 cm from the tumor on preceding MRI was defined local transformation. In (2) overlap with pretreatment volume after importation into a common space was defined as local transformation. With a centroid model we explored if there were particular patterns of transformations within relevant subgroups. Results We included 43 patients in the clinical evaluation, and 36 patients had MRIs scans available for longitudinal segmentations. Prior to malignant transformation, residual radiological tumor volumes were > 10 ml in 93% of patients. The transformation site was considered local in 91% of patients by clinical assessment. Patients treated with radiotherapy prior to transformation had somewhat lower rate of local transformations (83%). Based upon the segmentations, the transformation was local in 92%. We did not observe any particular pattern of transformations in examined molecular subgroups. Conclusion Malignant transformation occurs locally and within the T2w hyperintensities in most patients. Although LGG is an infiltrating disease, this data conceptually strengthens the role of loco-regional treatments in patients with LGG.
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Wang, Edmond. "Glioblastoma Synthesis and Segmentation with 3D Multi-Modal MRI: A Study using Generative Adversarial Networks." International Journal on Computational Science & Applications 11, no. 6 (December 31, 2021): 1–14. http://dx.doi.org/10.5121/ijcsa.2021.11601.
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The Grade IV cancer Glioblastoma is an extremely common and aggressive brain tumour. It is of significant consequence that histopathologic examinations should be able to identify and capture the tumour’s genetic variability for assistance in treatment. The use of Deep Learning - in particular CNNs and GANs - have become prominent in dealing with various image segmentation and detection tasks. The use of GANs have another importance - to expand the available training set by generating realistic pseudomedical images. Multi-modal MRIs, moreover, are also crucial as they lead to more successful performances. Nonetheless, accurate segmentation and realistic image synthesis remain challenging tasks. In this study, the history and various breakthroughs/challenges of utilising deep learning in glioblastoma detection is outlined and evaluated. To see networks in action, an adjusted and calibrated Vox2Vox network - a 3D implementation of the Pix2Pix translator - is trained on the biggest public brain tumour dataset BraTS 2020. The experimental results demonstrate the versatility and improvability of GAN networks in both fields of augmentation and segmentation. Overall, deep learning in medical imaging remains an extremely intoxicating field full of meticulous and innovative new studies.
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Verghese, Gregory, Mengyuan Li, Amit Lohan, Nikhil Cherian, Swapnil Rane, Fangfang Liu, Aekta Shah, et al. "Abstract 6233: A deep learning pipeline to capture the prognostic immune responses in lymph nodes of breast cancer patients." Cancer Research 82, no. 12_Supplement (June 15, 2022): 6233. http://dx.doi.org/10.1158/1538-7445.am2022-6233.
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Abstract Capturing tumor infiltrating leucocytes (TILS) and systemic immune responses in breast cancer informs disease progression and optimal treatment management. We have previously shown that morphological alterations in axillary lymph nodes (LNs), namely the formation of germinal centers in cancer-free LNs, adds prognostic value to TILs in triple negative breast cancer patients (TNBC) for the development of distant metastasis. Extending manual assessment of LNs beyond the detection of cancer requires the integration of robust deep learning pipelines into the digital pathology workflow. In this retrospective study, we used 1,100 Haematoxylin & Eosin-stained (H&E) Whole Slide Images (WSI) from Guy’s Hospital (London, UK) of metastatic and cancer-free LNs from 151 patients (100 N+) enriched for triple-negative or HER2-positive breast cancer to implement a supervised deep learning pipeline. A subset of 114 WSI, along with 5 breast cancer LN WSIs from each of Barts Hospital (London, UK) and Tianjin University Hospital (Tianjin, China), and 5 head and neck squamous cell carcinomas LN WSI (Guy’s Hospital) were used to develop, train and evaluate the segmentation task. For training Fully Convolutional Networks (FCNs), WSIs manually annotated for both germinal centers and sinuses formed a ground-truth set. Three FCNs were implemented: (i) a standard U-Net architecture; (ii) a U-Net model with an attention gate mechanism; and (iii) a multiscale-U-Net network (MSA-U-Net) that encodes, in parallel, a feature representation of the image at multiple resolutions. The MSA-U-Net achieved the best performance with an average dice score of 0.85 for germinal centers and 0.75 for sinuses. In comparison, the average dice score amongst 4 pathologists assessing 25 LN WSI for germinal centers and sinuses, was 0.67 and 0.61 respectively, demonstrating the robustness of the MSA-U-Net model. To quantify germinal centers and sinuses in LNs across the entire cohort, the trained MSA-U-Net was used in an inference step on all 1,100 WSI. The detected morphological features were initially localized within LNs using image thresholding and contouring techniques, and quantitatively assessed based on their number, area, shape, and Shannon diversity. We found significant morphological differences in metastatic and cancer-free LNs between N0 and N+ patients, with the latter displaying larger germinal centers with more irregular shapes especially in their metastatic LNs. In addition, we found differences in the Sinus area between LNs containing GCs and those without. Here, we propose a robust deep learning pipeline based on a multiscale FCN framework to automatically detect, localize and quantify histopathological immune features in WSI of LNs. By applying our pipeline to LNs of cancer patients, such as breast or head and neck, in prospective studies or clinical trials, we will further evaluate their prognostic and predictive values. Citation Format: Gregory Verghese, Mengyuan Li, Amit Lohan, Nikhil Cherian, Swapnil Rane, Fangfang Liu, Aekta Shah, Pat Gazinska, Selvam Thavaraj, Amit Sethi, Anita Grigoriadis. A deep learning pipeline to capture the prognostic immune responses in lymph nodes of breast cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6233.
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Heiland, Dieter, Robin Ohle, Simon Behringer, Juergen Beck, and Oliver Schnell. "NIMG-63. LONGITUDINAL ANALYSIS OF OLIGODENDROGLIOMA GROWTH PATTERN REVEALED SPATIAL HETEROGENEITY AND DIVERSE TREATMENT RESPONSE." Neuro-Oncology 21, Supplement_6 (November 2019): vi175. http://dx.doi.org/10.1093/neuonc/noz175.732.
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Abstract Oligodendroglioma are defined by a distinct molecular phenotype marked by 1p19q co-deletion and simultaneous presence of an IDH1/2 mutation. These tumors showed a favorable clinical course and long-term survival of around 15 years. Due to the long course of the disease, prospective studies to determine the effectiveness of different therapeutic strategies are difficult, since the percentage of patients with multiple therapies is high. Here we report a computational approach to map the longitudinal growth pattern, to quantify the effect of therapies on tumor growth and to identify similarities and spatial heterogeneity of oligodendroglioma growth. In our study, we included a cohort of 44 histopathologically and molecularly stratified oligodendrogliomas WHO°II (n=23) and WHO°III (n=21). We started our investigation with the longitudinal tumor segmentation. All volumetric data were pinpointed to the times of tumor therapy within all patients. Next, we extracted first-order features of tumor growth and response to chemo- or radiotherapy as well as resection, resulting in a total number of 98 features. An unsupervised cluster was used to identify similarities between patients, which revealed 3 subgroups. The first subgroup contained patients with predominantly frontal oligodendrogliomas marked by increased response to radiotherapy. The second subgroup included temporal oligodendrogliomas with high response rate to PC/PCV chemotherapy and flagged by epilepsy. The third group was heterogeneous with varying growth behaviors. A survival analysis showed a better separation between low- and high-risk patients based on the growth pattern model, in contrast to the WHO grading system. Taken together, our analysis revealed a novel classification of oligodendroglioma based on the longitudinal growth pattern and therapeutical response. We also detected a spatial difference between frontally or temporally localized oligodendrogliomas. We plan to further investigate molecular data that explain these spatial differences, which also may uncover novel therapeutic strategies.
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Wach, Johannes, Claudia Goetz, Kasra Shareghi, Torben Scholz, Volker Heßelmann, Ann-Kathrin Mager, Joachim Gottschalk, Hartmut Vatter, and Paul Kremer. "Dual-Use Intraoperative MRI in Glioblastoma Surgery: Results of Resection, Histopathologic Assessment, and Surgical Site Infections." Journal of Neurological Surgery Part A: Central European Neurosurgery 80, no. 06 (July 4, 2019): 413–22. http://dx.doi.org/10.1055/s-0039-1692975.
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Abstract Background To achieve maximal resection in glioblastoma (GBM) surgery, intraoperative imaging is important. An intraoperative magnetic resonance imaging (iMRI) suite used for both diagnostic and intraoperative imaging is considered being a reasonable concept for modern hospital management. It is still discussed if the dual use increases the risk of surgical site infections (SSI). This article assesses the rate of gross total resection (GTR), extent of resection (EOR), and histopathology after iMRI-guided resections in patients with GBM. The rate of surgical site infections (SSIs) is evaluated. Methods In all, 79 patients with GBM were operated on with iMRI. Additional resection was performed if iMRI depicted contrast enhancing tissue suggestive of residual tumor. GTR and EOR were determined by segmentation and volumetric analysis of the MR images. SSIs and the role of intravenous only or intravenous plus intrathecal antibiotics were evaluated. Statistical analysis was performed to detect the sensitivity, specificity, positive predictive value, and negative predictive value of iMRI-guided extended resections. Pearson's two-tailed chi-square test was performed to evaluate the rates of GTR and variables associated with SSI. Results GTR was achieved in 59 patients (74.68%). Rate of GTR was 35.44% before iMRI and additional resections (p < 0.0001). Mean EOR was 96.27%. Positive predictive value for tumor cells in the additionally resected tissue was 88.6%, negative predictive value was 100%, sensitivity was 100%, and specificity was 70. 6%. Rate of SSIs was 5.06% (n = 4). Two superficial SSIs, one subdural empyema and one cerebritis, were seen. SSI rates with parenteral only and additional intrathecal antibiotics were 0% and 8%, respectively (p = 0.133). Conclusion Increase of extent of tumor resection using iMRI is evident. SSI rate is within the normal range of neurosurgical procedures. A dual-use iMRI suite is a safe concept.
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Walkowski, Slawomir, and Janusz Szymas. "Histopathologic Patterns of Nervous System Tumors Based on Computer Vision Methods and Whole Slide Imaging (WSI)." Analytical Cellular Pathology 35, no. 2 (2012): 117–22. http://dx.doi.org/10.1155/2012/483525.
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Background: Making an automatic diagnosis based on virtual slides and whole slide imaging or even determining whether a case belongs to a single class, representing a specific disease, is a big challenge. In this work we focus on WHO Classification of Tumours of the Central Nervous System. We try to design a method which allows to automatically distinguish virtual slides which contain histopathologic patterns characteristic of glioblastoma – pseudopalisading necrosis and discriminate cases with neurinoma (schwannoma), which contain similar structures – palisading (Verocay bodies).Methods: Our method is based on computer vision approaches like structural analysis and shape descriptors. We start with image segmentation in a virtual slide, find specific patterns and use a set of features which can describe pseudopalisading necrosis and distinguish it from palisades. Type of structures found in a slide decides about its classification.Results: Described method is tested on a set of 49 virtual slides, captured using robotic microscope. Results show that 82% of glioblastoma cases and 90% of neurinoma cases were correctly identified by the proposed algorithm.Conclusion: Our method is a promising approach to automatic detection of nervous system tumors using virtual slides.
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Kanta Maitra, Indra, and Samir Kumar Bandyopadhyay. "CAD Based Method for Detection of Breast Cancer." Oriental journal of computer science and technology 11, no. 3 (September 10, 2018): 154–68. http://dx.doi.org/10.13005/ojcst11.03.04.
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Breast cancer affecting the women is known to cause high mortality unless detected in right time. Detection requires Mammography followed by biopsy of the tumour or lesions present in the breast tissue. Contemporary Mammographic hardware has incorporated digitization of output imagesfor increasing the scope for implementation of computational methods towards Computer Aided Diagnostics (CAD).CAD systems require Medical Image Processing, a multi-disciplinary science that involves development of computational algorithms on medical images. Histopathological slides are examined for determination of malignancy after biopsy is performed. Digital Images are required to be registered and enhanced prior to application of any deterministic algorithm. This paper provides both effective and efficient improvements over existing algorithms and introduces some innovative ideas based on image segmentation process to develop computer aided diagnosis tools that can help the radiologists in making accurate interpretation of the digital mammograms.
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Gui, Chloe, Jonathan C. Lau, and Joseph F. Megyesi. "30 Perceived versus quantified growth trajectory of serially-imaged low-grade gliomas." Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques 45, S3 (June 2018): S6. http://dx.doi.org/10.1017/cjn.2018.274.
Full textAbstract:
Background. Diffuse low-grade gliomas (LGGs) are infiltrative, slow-growing primary brain tumours that remain relatively asymptomatic for long periods of time before transforming into aggressive high-grade gliomas. Surveillance of tumour stability is performed primarily by serial imaging. Methods. We retrospectively identified LGG patients that were managed by observation with numerous (≥8) serial magnetic resonance imaging (MRI) studies. Tumour volumes were measured by manual segmentation on imaging. Demographic information, tumour histopathological data, and radiological interpretations were collected from electronic medical records. MRI radiology reports of tumour volume stability were classified into "growth" and "no growth" interpretations. Results. Of 74 LGG patients, 10 (13.5%) patients were included in the study. A median of 11 MRIs (range, 8-18) over a median of 79.7 months (range, 39.8-113.8 months) were analyzed per patient. Tumour diameter linearly increased at a median rate of 2.17 mm/year. Cox regression analysis showed that initial tumour volume predicted time to clinical intervention, and Mann-Whitney U test found that tumours of patients diagnosed before age 50 grew more slowly. Radiology interpretations that reported "no growth" (n=66) corresponded to a median measured growth of 3.90 mL and 11.0% compared to the comparison scan. Reports of "growth" (n=36) corresponded to median measured volume increases of 9.36 mL and 20.5%. Conclusion. We retrospectively analyzed the natural history of LGGs in serially-imaged patients at a single institution. Comparisons to the literature suggest that this is a subset of particularly slow-growing and low-risk tumours. We also highlight the clinical value of performing accurate LGG volumetric analyses.