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Zanfardino, Pane, Mirabelli, Salvatore, and Franzese. "TCGA-TCIA Impact on Radiogenomics Cancer Research: A Systematic Review." International Journal of Molecular Sciences 20, no. 23 (November 29, 2019): 6033. http://dx.doi.org/10.3390/ijms20236033.
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In the last decade, the development of radiogenomics research has produced a significant amount of papers describing relations between imaging features and several molecular ‘omic signatures arising from next-generation sequencing technology and their potential role in the integrated diagnostic field. The most vulnerable point of many of these studies lies in the poor number of involved patients. In this scenario, a leading role is played by The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA), which make available, respectively, molecular ‘omic data and linked imaging data. In this review, we systematically collected and analyzed radiogenomic studies based on TCGA-TCIA data. We organized literature per tumor type and molecular ‘omic data in order to discuss salient imaging genomic associations and limitations of each study. Finally, we outlined the potential clinical impact of radiogenomics to improve the accuracy of diagnosis and the prediction of patient outcomes in oncology.
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Sanada, Takahiro, Shota Yamamoto, Mio Sakai, Toru Umehara, Hirotaka Sato, Masato Saito, Nobuyuki Mitsui, et al. "NI-13 THE RATIO OF T1-WEIGHTED TO T2-WEIGHTED SIGNAL INTENSITY AND IDH MUTATION IN GLIOMA." Neuro-Oncology Advances 4, Supplement_3 (December 1, 2022): iii17—iii18. http://dx.doi.org/10.1093/noajnl/vdac167.066.
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Abstract Purpose The current study aims to test the hypothesis that the ratio of T1-weighted image to T2-weighted image signal intensity (T1w/T2w-Ratio: rT1/T2), an imaging surrogate developed for myelin integrity, is predictive of histologically lower-grade glioma's IDH mutation status. Materials and Methods 25 histologically and molecularly confirmed lower-grade glioma patients with eight IDH-wildtype (IDHwt) and 17 IDH-mutant (IDHmt) tumors at Asahikawa Medical University Hospital (AMUH) were used as a test cohort. Twenty-nine patients (IDHwt: 13, IDHmt: 16) from Osaka International Cancer Institute (OICI) and 101 patients from the Cancer Imaging Archive (TCIA) / Cancer Genome Atlas (TCGA) dataset (IDHwt: 19, IDHmt: 82) were used as external cohorts. rT1/T2 images were calculated from T1- and T2-weighted images using a recommended signal correction. The relationship between the mean rT1/T2 (mrT1/T2) and the IDH mutation status was investigated. Moreover, t-Distributed Stochastic Neighbor Embedding (t-SNE) investigated the difference in MRI qualities and characteristics between the three cohorts. Results The test cohort at AMUH revealed that mrT1/T2 of IDHwt tumors was significantly higher than that of IDHmt tumors (p < 0.05) and that the optimal cut-off of mrT1/T2 for discriminating IDHmt was 0.666-0.677, (AUC = 0.75, p < 0.05), which finding was validated by the external domestic cohort at OICI (AUC = 0.75, p = 0.02). However, the external international cohort deriving from TCIA/TCGA could not validate this (AUC = 0.63, p = 0.08). t-SNE analysis revealed that the difference in image characteristics within the cohort was more diverse for the TCIA/TCGA than for the two domestic cohorts. Conclusion The current study revealed that mrT1/T2 was able to discriminate IDHwt and IDHmt tumors in two domestic cohorts significantly. This was not validated by the TCIA/TCGA cohort due to the wide variety in the original imaging characteristics of the TCIA/TCGA cohort.
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Clark, Kenneth, Bruce Vendt, Kirk Smith, John Freymann, Justin Kirby, Paul Koppel, Stephen Moore, et al. "The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository." Journal of Digital Imaging 26, no. 6 (July 25, 2013): 1045–57. http://dx.doi.org/10.1007/s10278-013-9622-7.
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Sharma, Ashish, Lawrence Tarbox, Tahsin Kurc, Jonathan Bona, Kirk Smith, Pradeeban Kathiravelu, Erich Bremer, Joel H. Saltz, and Fred Prior. "PRISM: A Platform for Imaging in Precision Medicine." JCO Clinical Cancer Informatics, no. 4 (September 2020): 491–99. http://dx.doi.org/10.1200/cci.20.00001.
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PURPOSE Precision medicine requires an understanding of individual variability, which can only be acquired from large data collections such as those supported by the Cancer Imaging Archive (TCIA). We have undertaken a program to extend the types of data TCIA can support. This, in turn, will enable TCIA to play a key role in precision medicine research by collecting and disseminating high-quality, state-of-the-art, quantitative imaging data that meet the evolving needs of the cancer research community METHODS A modular technology platform is presented that would allow existing data resources, such as TCIA, to evolve into a comprehensive data resource that meets the needs of users engaged in translational research for imaging-based precision medicine. This Platform for Imaging in Precision Medicine (PRISM) helps streamline the deployment and improve TCIA’s efficiency and sustainability. More importantly, its inherent modular architecture facilitates a piecemeal adoption by other data repositories. RESULTS PRISM includes services for managing radiology and pathology images and features and associated clinical data. A semantic layer is being built to help users explore diverse collections and pool data sets to create specialized cohorts. PRISM includes tools for image curation and de-identification. It includes image visualization and feature exploration tools. The entire platform is distributed as a series of containerized microservices with representational state transfer interfaces. CONCLUSION PRISM is helping modernize, scale, and sustain the technology stack that powers TCIA. Repositories can take advantage of individual PRISM services such as de-identification and quality control. PRISM is helping scale image informatics for cancer research at a time when the size, complexity, and demands to integrate image data with other precision medicine data-intensive commons are mounting.
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Castaldo, Rossana, Katia Pane, Emanuele Nicolai, Marco Salvatore, and Monica Franzese. "The Impact of Normalization Approaches to Automatically Detect Radiogenomic Phenotypes Characterizing Breast Cancer Receptors Status." Cancers 12, no. 2 (February 24, 2020): 518. http://dx.doi.org/10.3390/cancers12020518.
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In breast cancer studies, combining quantitative radiomic with genomic signatures can help identifying and characterizing radiogenomic phenotypes, in function of molecular receptor status. Biomedical imaging processing lacks standards in radiomic feature normalization methods and neglecting feature normalization can highly bias the overall analysis. This study evaluates the effect of several normalization techniques to predict four clinical phenotypes such as estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and triple negative (TN) status, by quantitative features. The Cancer Imaging Archive (TCIA) radiomic features from 91 T1-weighted Dynamic Contrast Enhancement MRI of invasive breast cancers were investigated in association with breast invasive carcinoma miRNA expression profiling from the Cancer Genome Atlas (TCGA). Three advanced machine learning techniques (Support Vector Machine, Random Forest, and Naïve Bayesian) were investigated to distinguish between molecular prognostic indicators and achieved an area under the ROC curve (AUC) values of 86%, 93%, 91%, and 91% for the prediction of ER+ versus ER−, PR+ versus PR−, HER2+ versus HER2−, and triple-negative, respectively. In conclusion, radiomic features enable to discriminate major breast cancer molecular subtypes and may yield a potential imaging biomarker for advancing precision medicine.
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Fedorov, Andrey, Reinhard Beichel, Jayashree Kalpathy-Cramer, David Clunie, Michael Onken, Jörg Riesmeier, Christian Herz, et al. "Quantitative Imaging Informatics for Cancer Research." JCO Clinical Cancer Informatics, no. 4 (September 2020): 444–53. http://dx.doi.org/10.1200/cci.19.00165.
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PURPOSE We summarize Quantitative Imaging Informatics for Cancer Research (QIICR; U24 CA180918), one of the first projects funded by the National Cancer Institute (NCI) Informatics Technology for Cancer Research program. METHODS QIICR was motivated by the 3 use cases from the NCI Quantitative Imaging Network. 3D Slicer was selected as the platform for implementation of open-source quantitative imaging (QI) tools. Digital Imaging and Communications in Medicine (DICOM) was chosen for standardization of QI analysis outputs. Support of improved integration with community repositories focused on The Cancer Imaging Archive (TCIA). Priorities included improved capabilities of the standard, toolkits and tools, reference datasets, collaborations, and training and outreach. RESULTS Fourteen new tools to support head and neck cancer, glioblastoma, and prostate cancer QI research were introduced and downloaded over 100,000 times. DICOM was amended, with over 40 correction proposals addressing QI needs. Reference implementations of the standard in a popular toolkit and standalone tools were introduced. Eight datasets exemplifying the application of the standard and tools were contributed. An open demonstration/connectathon was organized, attracting the participation of academic groups and commercial vendors. Integration of tools with TCIA was improved by implementing programmatic communication interface and by refining best practices for QI analysis results curation. CONCLUSION Tools, capabilities of the DICOM standard, and datasets we introduced found adoption and utility within the cancer imaging community. A collaborative approach is critical to addressing challenges in imaging informatics at the national and international levels. Numerous challenges remain in establishing and maintaining the infrastructure of analysis tools and standardized datasets for the imaging community. Ideas and technology developed by the QIICR project are contributing to the NCI Imaging Data Commons currently being developed.
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Treiber, Jeffrey M., Tyler C. Steed, Michael G. Brandel, Kunal S. Patel, Anders M. Dale, Bob S. Carter, and Clark C. Chen. "Molecular physiology of contrast enhancement in glioblastomas: An analysis of The Cancer Imaging Archive (TCIA)." Journal of Clinical Neuroscience 55 (September 2018): 86–92. http://dx.doi.org/10.1016/j.jocn.2018.06.018.
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Luo, Yangkun, Lu Li, Gang Yin, and Jin Yi Lang. "Predicting tumor mutational burden in head and neck squamous cell carcinoma based on CT imaging features: A TCGA/TCIA study." Journal of Clinical Oncology 38, no. 15_suppl (May 20, 2020): e15254-e15254. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.e15254.
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e15254 Background: Immunotherapy has substantially changed the therapeutic strategies for cancers. Unfortunately, only 20–50% of patients with advanced solid tumours respond to treatment. There is therefore a need for the development of methods to identify patients who are most likely to respond to immunotherapy. Tumor mutation burden (TMB) have been served as the most prevalent biomarkers to predict immunotherapy response. This study was designed to investigate the ability of radiomics to predict TMB status in patients with head and neck squamous cell carcinoma (HNSCC). Methods: TMB values were calculated using genomic data obtained from the HNSCC dataset in The Cancer Genome Atlas (TCGA).We identified matching patients (n = 100) who underwent contrast-enhanced CT scan prior to treatment from The Cancer Imaging Archive (TCIA),and patients were grouped based on the cutoff value; high group(>4.2 mutations/Mb) and low group(≤4.2 mutations/Mb). A total of 249 radiomics features(9 non-texture features and 240 scan-texture-parameter features) were extracted from CT images of the tumor. The incorporation of features into multivariable models was performed using logistic regression. The multivariable modeling strategy involved imbalance-adjusted bootstrap resampling in the following four steps leading to final prediction model construction: (1) feature set reduction; (2) feature selection; (3) prediction performance estimation; and (4) computation of model coefficients. The performance was evaluated in terms of area under the curve (AUC), sensitivity, and specificity. Results: Among all the features, twenty features were found to have the most impact on the predictive value; the two top texture parameters were GLCM-Variance and GLCM-Sum Average. In multivariable analysis, the best performance was obtained using a combination of seven texture features that can discriminate between high mutation burden versus low mutation burden. The AUC, sensitivity, and specificity of this model were 0.97 ± 0.01, 0.92 ± 0.04, and 0.92 ±0.01, respectively. Conclusions: The proposed CT-derived predictive model can accurately predict TMB status in patients with HNSCC. It may be helpful in guiding immunotherapy in clinical practice and deserves further analysis.
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Sanada, Takahiro, Shota Yamamoto, Hirotaka Sato, Mio Sakai, Masato Saito, Nobuyuki Mitsui, Satoru Hiroshima, et al. "NI-12 The ratio of T1-Weighted to T2-Weighted Signal Intensity and IDH mutation in glioma." Neuro-Oncology Advances 3, Supplement_6 (December 1, 2021): vi20. http://dx.doi.org/10.1093/noajnl/vdab159.074.
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Abstract Introduction: Prediction of IDH mutation status for Lower-grade glioma (LrGG) is clinically significant. The purpose of this study is to test the hypothesis that the T1-weighted image/T2-weighted image ratio (rT1/T2), an imaging surrogate developed for myelin integrity, is a useful MRI biomarker for predicting the IDH mutation status of LrGG. Methods: Twenty-five LrGG patients (IDHwt: 8, IDHmt: 17) at Asahikawa Medical University Hospital (AMUH) were used as an exploratory cohort. Twenty-nine LrGG patients (IDHwt: 13, IDHmt: 16) from Osaka International Cancer Institute (OICI) and 103 patients from the Cancer Imaging Archive (TCIA) / Cancer Genome Atlas (TCGA) dataset (IDHwt: 19, IDHmt: 84) were used as validation cohorts. rT1/T2 images were calculated from T1- and T2-weighted images using a recommended signal correction. The region-of-interest was defined on T2-weighted images, and the relationship between the mean rT1/T2 (mrT1/T2) and the IDH mutation status was investigated. Results: The mrT1/T2 was able to significantly predict the IDH mutation status for the AMUH exploratory cohort (AUC = 0.75, p = 0.048). The ideal cut-off for detecting mutant IDH was mrT1/T2 < 0.666 ~ 0.677, with a sensitivity of 58.8% and a specificity of 87.5%. This result was further validated by the OICI validation cohort (AUC = 0.75, p = 0.023) with a sensitivity of 56.3% and a specificity of 69.2%. On the other hand, the sensitivity was 42.9% and the specificity was 68.4 % for the TCIA validation cohort (AUC = 0.63, p = 0.068). Conclusion: Our results supported the hypothesis that mrT1/T2 could be a useful image surrogate to predict the IDH mutation status of LrGG using two domestic cohorts. The decline of the accuracy for the TCIA cohort should be further investigated.
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Kawaguchi, Risa K., Masamichi Takahashi, Mototaka Miyake, Manabu Kinoshita, Satoshi Takahashi, Koichi Ichimura, Ryuji Hamamoto, Yoshitaka Narita, and Jun Sese. "Assessing Versatile Machine Learning Models for Glioma Radiogenomic Studies across Hospitals." Cancers 13, no. 14 (July 19, 2021): 3611. http://dx.doi.org/10.3390/cancers13143611.
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Radiogenomics use non-invasively obtained imaging data, such as magnetic resonance imaging (MRI), to predict critical biomarkers of patients. Developing an accurate machine learning (ML) technique for MRI requires data from hundreds of patients, which cannot be gathered from any single local hospital. Hence, a model universally applicable to multiple cohorts/hospitals is required. We applied various ML and image pre-processing procedures on a glioma dataset from The Cancer Image Archive (TCIA, n = 159). The models that showed a high level of accuracy in predicting glioblastoma or WHO Grade II and III glioma using the TCIA dataset, were then tested for the data from the National Cancer Center Hospital, Japan (NCC, n = 166) whether they could maintain similar levels of high accuracy. Results: we confirmed that our ML procedure achieved a level of accuracy (AUROC = 0.904) comparable to that shown previously by the deep-learning methods using TCIA. However, when we directly applied the model to the NCC dataset, its AUROC dropped to 0.383. Introduction of standardization and dimension reduction procedures before classification without re-training improved the prediction accuracy obtained using NCC (0.804) without a loss in prediction accuracy for the TCIA dataset. Furthermore, we confirmed the same tendency in a model for IDH1/2 mutation prediction with standardization and application of dimension reduction that was also applicable to multiple hospitals. Our results demonstrated that overfitting may occur when an ML method providing the highest accuracy in a small training dataset is used for different heterogeneous data sets, and suggested a promising process for developing an ML method applicable to multiple cohorts.
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Alattar, Ali, Jeffrey Treiber, Tyler Steed, Kunal Patel, Michael Brandel, Anders Dale, Bob Carter, and Clark Chen. "NIMG-25. MOLECULAR PHYSIOLOGY OF CONTRAST ENHANCEMENT IN GLIOBLASTOMAS: AN ANALYSIS OF THE CANCER IMAGING ARCHIVE (TCIA)." Neuro-Oncology 20, suppl_6 (November 2018): vi181. http://dx.doi.org/10.1093/neuonc/noy148.751.
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Zheng, Shuhua, and Wensi Tao. "Identification of Novel Transcriptome Signature as a Potential Prognostic Biomarker for Anti-Angiogenic Therapy in Glioblastoma Multiforme." Cancers 13, no. 5 (March 1, 2021): 1013. http://dx.doi.org/10.3390/cancers13051013.
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Glioblastoma multiforme (GBM) is the most common and devastating type of primary brain tumor, with a median survival time of only 15 months. Having a clinically applicable genetic biomarker would lead to a paradigm shift in precise diagnosis, personalized therapeutic decisions, and prognostic prediction for GBM. Radiogenomic profiling connecting radiological imaging features with molecular alterations will offer a noninvasive method for genomic studies of GBM. To this end, we analyzed over 3800 glioma and GBM cases across four independent datasets. The Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) databases were employed for RNA-Seq analysis, whereas the Ivy Glioblastoma Atlas Project (Ivy-GAP) and The Cancer Imaging Archive (TCIA) provided clinicopathological data. The Clinical Proteomic Tumor Analysis Consortium Glioblastoma Multiforme (CPTAC-GBM) was used for proteomic analysis. We identified a simple three-gene transcriptome signature—SOCS3, VEGFA, and TEK—that can connect GBM’s overall prognosis with genes’ expression and simultaneously correlate radiographical features of perfusion imaging with SOCS3 expression levels. More importantly, the rampant development of neovascularization in GBM offers a promising target for therapeutic intervention. However, treatment with bevacizumab failed to improve overall survival. We identified SOCS3 expression levels as a potential selection marker for patients who may benefit from early initiation of angiogenesis inhibitors.
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Rathore, Saima, Suyash Mohan, Spyridon Bakas, Chiharu Sako, Chaitra Badve, Sarthak Pati, Ashish Singh, et al. "Multi-institutional noninvasive in vivo characterization of IDH, 1p/19q, and EGFRvIII in glioma using neuro-Cancer Imaging Phenomics Toolkit (neuro-CaPTk)." Neuro-Oncology Advances 2, Supplement_4 (December 1, 2020): iv22—iv34. http://dx.doi.org/10.1093/noajnl/vdaa128.
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Abstract Background Gliomas represent a biologically heterogeneous group of primary brain tumors with uncontrolled cellular proliferation and diffuse infiltration that renders them almost incurable, thereby leading to a grim prognosis. Recent comprehensive genomic profiling has greatly elucidated the molecular hallmarks of gliomas, including the mutations in isocitrate dehydrogenase 1 and 2 (IDH1 and IDH2), loss of chromosomes 1p and 19q (1p/19q), and epidermal growth factor receptor variant III (EGFRvIII). Detection of these molecular alterations is based on ex vivo analysis of surgically resected tissue specimen that sometimes is not adequate for testing and/or does not capture the spatial tumor heterogeneity of the neoplasm. Methods We developed a method for noninvasive detection of radiogenomic markers of IDH both in lower-grade gliomas (WHO grade II and III tumors) and glioblastoma (WHO grade IV), 1p/19q in IDH-mutant lower-grade gliomas, and EGFRvIII in glioblastoma. Preoperative MRIs of 473 glioma patients from 3 of the studies participating in the ReSPOND consortium (collection I: Hospital of the University of Pennsylvania [HUP: n = 248], collection II: The Cancer Imaging Archive [TCIA; n = 192], and collection III: Ohio Brain Tumor Study [OBTS, n = 33]) were collected. Neuro-Cancer Imaging Phenomics Toolkit (neuro-CaPTk), a modular platform available for cancer imaging analytics and machine learning, was leveraged to extract histogram, shape, anatomical, and texture features from delineated tumor subregions and to integrate these features using support vector machine to generate models predictive of IDH, 1p/19q, and EGFRvIII. The models were validated using 3 configurations: (1) 70–30% training–testing splits or 10-fold cross-validation within individual collections, (2) 70–30% training–testing splits within merged collections, and (3) training on one collection and testing on another. Results These models achieved a classification accuracy of 86.74% (HUP), 85.45% (TCIA), and 75.15% (TCIA) in identifying EGFRvIII, IDH, and 1p/19q, respectively, in configuration I. The model, when applied on combined data in configuration II, yielded a classification success rate of 82.50% in predicting IDH mutation (HUP + TCIA + OBTS). The model when trained on TCIA dataset yielded classification accuracy of 84.88% in predicting IDH in HUP dataset. Conclusions Using machine learning algorithms, high accuracy was achieved in the prediction of IDH, 1p/19q, and EGFRvIII mutation. Neuro-CaPTk encompasses all the pipelines required to replicate these analyses in multi-institutional settings and could also be used for other radio(geno)mic analyses.
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Le, Nguyen Quoc Khanh, Quang Hien Kha, Van Hiep Nguyen, Yung-Chieh Chen, Sho-Jen Cheng, and Cheng-Yu Chen. "Machine Learning-Based Radiomics Signatures for EGFR and KRAS Mutations Prediction in Non-Small-Cell Lung Cancer." International Journal of Molecular Sciences 22, no. 17 (August 26, 2021): 9254. http://dx.doi.org/10.3390/ijms22179254.
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Early identification of epidermal growth factor receptor (EGFR) and Kirsten rat sarcoma viral oncogene homolog (KRAS) mutations is crucial for selecting a therapeutic strategy for patients with non-small-cell lung cancer (NSCLC). We proposed a machine learning-based model for feature selection and prediction of EGFR and KRAS mutations in patients with NSCLC by including the least number of the most semantic radiomics features. We included a cohort of 161 patients from 211 patients with NSCLC from The Cancer Imaging Archive (TCIA) and analyzed 161 low-dose computed tomography (LDCT) images for detecting EGFR and KRAS mutations. A total of 851 radiomics features, which were classified into 9 categories, were obtained through manual segmentation and radiomics feature extraction from LDCT. We evaluated our models using a validation set consisting of 18 patients derived from the same TCIA dataset. The results showed that the genetic algorithm plus XGBoost classifier exhibited the most favorable performance, with an accuracy of 0.836 and 0.86 for detecting EGFR and KRAS mutations, respectively. We demonstrated that a noninvasive machine learning-based model including the least number of the most semantic radiomics signatures could robustly predict EGFR and KRAS mutations in patients with NSCLC.
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Basu, Amrita, Denise Warzel, Aras Eftekhari, Justin S. Kirby, John Freymann, Janice Knable, Ashish Sharma, and Paula Jacobs. "Call for Data Standardization: Lessons Learned and Recommendations in an Imaging Study." JCO Clinical Cancer Informatics, no. 3 (December 2019): 1–11. http://dx.doi.org/10.1200/cci.19.00056.
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PURPOSE Data sharing creates potential cost savings, supports data aggregation, and facilitates reproducibility to ensure quality research; however, data from heterogeneous systems require retrospective harmonization. This is a major hurdle for researchers who seek to leverage existing data. Efforts focused on strategies for data interoperability largely center around the use of standards but ignore the problems of competing standards and the value of existing data. Interoperability remains reliant on retrospective harmonization. Approaches to reduce this burden are needed. METHODS The Cancer Imaging Archive (TCIA) is an example of an imaging repository that accepts data from a diversity of sources. It contains medical images from investigators worldwide and substantial nonimage data. Digital Imaging and Communications in Medicine (DICOM) standards enable querying across images, but TCIA does not enforce other standards for describing nonimage supporting data, such as treatment details and patient outcomes. In this study, we used 9 TCIA lung and brain nonimage files containing 659 fields to explore retrospective harmonization for cross-study query and aggregation. It took 329.5 hours, or 2.3 months, extended over 6 months to identify 41 overlapping fields in 3 or more files and transform 31 of them. We used the Genomic Data Commons (GDC) data elements as the target standards for harmonization. RESULTS We characterized the issues and have developed recommendations for reducing the burden of retrospective harmonization. Once we harmonized the data, we also developed a Web tool to easily explore harmonized collections. CONCLUSION While prospective use of standards can support interoperability, there are issues that complicate this goal. Our work recognizes and reveals retrospective harmonization issues when trying to reuse existing data and recommends national infrastructure to address these issues.
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Agana, Moses A., Chukwuemeka Odi Agwu, and Nsinem A. Ukpoho. "Breast Cancer Prediction and Control Using BiLSTM and Two-Dimensional Convolutional Neural Network." International Journal of Software Innovation 11, no. 1 (January 20, 2023): 1–19. http://dx.doi.org/10.4018/ijsi.316169.
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Breast cancer has a devastating effect on women. Different strategies of breast cancer classification exist with minimal work done on the prediction of the occurrence of the disease in potential carriers. In this study, a breast cancer predictive system has been developed using bidirectional long short-term memory (BiLSTM) for feature extraction and learning while the two-dimensional convolutional neural network (CNN) was used for breast cancer classification. Histopathological images were used for cancer prediction. Python was used as the programming language for implementing the system. The model was tested using datasets from The Cancer Imaging Archive (TCIA) repository. An accuracy level of 98.8% (higher than the most recent existing model) was achieved for the prediction of the future occurrence of breast cancer based on the tests on the dataset. The application of the model using live data from women can help in the prediction and control of the occurrence of breast cancer amongst women.
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Elhalawani, H., A. S. Mohamed, S. Mulder, A. Grossberg, K. E. Smith, G. B. Gunn, S. J. Frank, D. I. Rosenthal, A. S. Garden, and C. D. Fuller. "Radiomics Prediction of Radiation Treatment Outcomes in Oropharyngeal Cancer: A Clinical and Image Repository in Concert with the Cancer Imaging Archive (TCIA)." International Journal of Radiation Oncology*Biology*Physics 102, no. 3 (November 2018): e215-e216. http://dx.doi.org/10.1016/j.ijrobp.2018.07.748.
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Hilal, Suha Raheem, Hussain S. Hasan, and Ali M. Hasan. "Magnetic Resonance Imaging Breast Scan Classification based on Texture Features and Long Short-Term Memory Model." NeuroQuantology 19, no. 7 (August 11, 2021): 41–47. http://dx.doi.org/10.14704/nq.2021.19.7.nq21082.
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The aim of study is building new program for processing MRI images using MATLAB and to investigate different breast MRI detection algorithms that inform normal and abnormal scans of MRI. In this research an algorithm is proposed to extract texture feature and inform normal and abnormal scans of MRI. First, the MRI scans are pre- processed by image enhancement, intensity normalization, background segmentation and detection of mirror symmetry of breast. Second, the proposed gray level co- occurrence matrix (GLCM) and gray level run length matrix (GLRLM) methods are used to extract texture features from MRI T2-weighted and STIR images. Finally, these features are classified into normal and abnormal by using long short term memory (LSTM) model. The research will be validated using 326 datasets that downloaded from cancer imaging archive (TCIA). The achieved classification accuracy was 98.80%.
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Lerner, Seth P., Vinay Duddalwar, Erich Huang, Ersan Altun, Tharakeswara Bathala, Steven Kennish, Juan Ibarra, et al. "Comprehensive radiogenomics analysis of qualitative and quantitative features of cross-sectional imaging in the TCGA project in MIBC." Journal of Clinical Oncology 37, no. 7_suppl (March 1, 2019): 482. http://dx.doi.org/10.1200/jco.2019.37.7_suppl.482.
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482 Background: Quantitative imaging descriptors derived from CT and MRI can be integrated with genomic data that may be used as non-invasive prognostic or predictive biomarkers. We report an integrated radiogenomics project designed to develop subjective and objective parameters extracted from cross-sectional imaging of MIBC from studies archived in the TCIA and linked to the TCGA project. Methods: We reported comprehensive integrated genomic analysis of 412 tumors (Cell 2017). 7 of 33 tissue source sites submitted CT scans to the TCIA (n=106). We developed 17 features describing tumor size/location, metastases sites, and tumor morphology; 9 GU radiologists reviewed the scans in a blinded manner. EH analyzed the data independent of the radiologists. We computed kappa statistics for categorical features and coverage probabilities for quantitative features (Lin et al 2002). The tumor was segmented on an axial image and the segmented image analyzed using a radiomics panel (radiomicslab.usc.edu). Associations between individual features and subtypes were assessed (Fisher’s Exact Test) for categorical features and Kruskal-Wallis Test for quantitative features. Results: Substantial agreement (k≥ 0.6) was observed in 4 features: tumor laterality, tumor within bladder diverticulum, right and left UVJ involvement and hydroureter. We observed weak agreement (95% CI <0.4) for bladder neck, posterior bladder, dome, and trigone involvement, tumor margin, internal architecture, radiographic stage, left upper tract involvement, and metastases. The coverage probability for lesion size was 0.59 (0.544-0.638) (Figure). Tumor morphology was associated with microRNA cluster, with diffuse wall thickening having a higher tendency toward Clusters 3 and 4 (p < .001). Radiomic analysis identified statistically significant associations of mutations in FGFR3, CREBBP, CASP8 and EP300 with multiple radiomic features. Conclusions: This blinded comprehensive assessment of features extracted from CT images highlights many of the ongoing challenges in staging patients with MIBC. Preliminary analysis shows promise in analyzing associations between radiomic features and mutations.
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Chiu, Fang-Ying, and Yun Yen. "Efficient Radiomics-Based Classification of Multi-Parametric MR Images to Identify Volumetric Habitats and Signatures in Glioblastoma: A Machine Learning Approach." Cancers 14, no. 6 (March 14, 2022): 1475. http://dx.doi.org/10.3390/cancers14061475.
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Glioblastoma (GBM) is a fast-growing and aggressive brain tumor of the central nervous system. It encroaches on brain tissue with heterogeneous regions of a necrotic core, solid part, peritumoral tissue, and edema. This study provided qualitative image interpretation in GBM subregions and radiomics features in quantitative usage of image analysis, as well as ratios of these tumor components. The aim of this study was to assess the potential of multi-parametric MR fingerprinting with volumetric tumor phenotype and radiomic features to underlie biological process and prognostic status of patients with cerebral gliomas. Based on efficiently classified and retrieved cerebral multi-parametric MRI, all data were analyzed to derive volume-based data of the entire tumor from local cohorts and The Cancer Imaging Archive (TCIA) cohorts with GBM. Edema was mainly enriched for homeostasis whereas necrosis was associated with texture features. The proportional volume size of the edema was about 1.5 times larger than the size of the solid part tumor. The volume size of the solid part was approximately 0.7 times in the necrosis area. Therefore, the multi-parametric MRI-based radiomics model reveals efficiently classified tumor subregions of GBM and suggests that prognostic radiomic features from routine MRI examination may also be significantly associated with key biological processes as a practical imaging biomarker.
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Takahashi, Masamichi, Risa Kawaguchi, Satoshi Takahashi, Mototaka Miyake, Manabu Kinoshita, Koichi Ichimura, Ryuji Hamamoto, Yoshitaka Narita, and Jun Sese. "NIMG-67. DEVELOPMENT OF VERSATILE MACHINE-LEARNING APPROACHES FOR RADIOGENOMICS OF GLIOMA IN DIFFERENT COHORTS." Neuro-Oncology 21, Supplement_6 (November 2019): vi176. http://dx.doi.org/10.1093/neuonc/noz175.736.
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Abstract BACKGROUND Radiogenomics aims to analyze clinical images and information, and to predict key molecular profiles of tumors. However, imaging protocol is usually different in facilities, and it has been rarely examined whether the performance of developed methods in a dataset is robustly sustained even in other independent datasets. We explored machine learning and matrix decomposition methods using preoperative magnetic resonance images (MRIs) of glioma patients to establish versatile platform regardless of the heterogeneity of the datasets. METHODS Preoperative glioma MRIs and clinical information were obtained from public dataset of The Cancer Imaging Archive (TCIA, N=159) and National Cancer Center Hospital (NCC, N=166). More than 16,000 radiomic features were applied for the prediction of tumor grading and IDH mutation status. Accuracy of prediction was evaluated by AUROC (area under the receiver operating characteristic curves). RESULTS The performances were comparable between the image features regardless of dimension reduction methods (the best accuracy for tumor grading and IDH status prediction was 0.91 and 0.88, respectively), but they were drastically decreased in the transfer learning (0.70 and 0.69). On the other hand, they were successfully improved by applying matrix decomposition and brain embedding (0.86 and 0.79). CONCLUSION Our result and pipeline can be a global benchmark for future studies in heterogeneous datasets. Further evaluation in larger cohorts are planned.
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FH, Tang, Chu CYW, and Cheung EYW. "Radiomics AI prediction for head and neck squamous cell carcinoma (HNSCC) prognosis and recurrence with target volume approach." BJR|Open 3, no. 1 (January 2021): 20200073. http://dx.doi.org/10.1259/bjro.20200073.
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Objectives: To evaluate the performance of radiomics features extracted from planning target volume (PTV) and gross tumor volume (GTV) in the prediction of the death prognosis and cancer recurrence rate for head and neck squamous cell carcinoma (HNSCC). Methods: 188 HNSCC patients’ planning CT images with radiotherapy structures sets were acquired from Cancer Imaging Archive (TCIA). The 3D slicer (v. 4.10.2) with the PyRadiomics extension (Computational Imaging and Bioinformatics Lab, Harvard medical School) was used to extract radiomics features from the radiotherapy planning images. An in-house developed deep learning artificial neural networks (DL-ANN) model was used to predict death prognosis and cancer recurrence rate based on the features extracted from GTV and PTV of the CT images. Results: The PTV radiomics features with DL-ANN model could achieve 77.7% accuracy with overall AUC equal to 0.934 and 0.932 when predicting HNSCC-related death prognosis and cancer recurrence respectively. Furthermore, the DL-ANN model can achieve an accuracy of 74.3% with AUC equal to 0.947 and 0.956 for the HNSCC-related death prognosis and cancer recurrence respectively using GTV features. Conclusion: Using both GTV and PTV radiomics features in the DL-ANN model, can aid in predicting HNSCC-related death prognosis and cancer recurrence. Clinicians may find it helpful in formulating different treatment regimens and facilitate personized medicine based on the predicted outcome when performing GTV and PTV delineation. Advances in knowledge: Radiomics features of GTV and PTV are reliable prognosis and recurrence predicting tools, which may help clinicians in GTV and PTV delineation to facilitate delivery of personalized treatment.
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Yang, Guoqiang, Yongjian Sha, Xiaochun Wang, Yan Tan, and Hui Zhang. "Radiomics Profiling Identifies the Incremental Value of MRI Features beyond Key Molecular Biomarkers for the Risk Stratification of High-Grade Gliomas." Contrast Media & Molecular Imaging 2022 (March 23, 2022): 1–12. http://dx.doi.org/10.1155/2022/8952357.
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Objective. To identify the incremental value of magnetic resonance imaging (MRI) features beyond key molecular biomarkers for the risk stratification of high-grade gliomas (HGGs). Methods. A total of 241 patients with preoperative magnetic resonance (MR) images and clinical and genetic data were retrospectively collected from our institution and The Cancer Genome Atlas/The Cancer Imaging Archive (TCGA/TCIA) dataset. Radiomic features (n = 1702) were extracted from both postcontrast T1-weighted (CE-T1) and T2-weighted fluid attenuation inversion recovery (T2FLAIR) MR images. The least absolute shrinkage and selection operator (LASSO) method was used to select effective features. A multivariate Cox proportional risk regression model was established to explore the prognostic value of clinical features, molecular biomarkers, and radiomic features. Kaplan–Meier survival analysis and the log-rank test were used to evaluate the prognostic model, and a stratified analysis was conducted to demonstrate the incremental value of the radiomics signature. A nomogram was developed to predict the 1-year, 2-year, and 3-year overall survival (OS) probabilities of the patients with HGGs. Results. The radiomics signature provided significant prognostic value for the risk stratification of patients with HGGs. The combined model integrating the radiomics signature with clinical data (age) and O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status had the best prognostic value, with C-index values of 0.752 and 0.792 in the training set and external validation set, respectively. Stratified Kaplan–Meier survival analysis showed that the radiomics signature could identify the risk subgroups in different clinical and molecular subgroups. Conclusion. This radiomics signature can be used for the risk stratification of patients with HGGs and has incremental value beyond key molecular biomarkers, providing a preoperative basis for individualized diagnosis and treatment decision-making.
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Chaddad, Ahmad, Paul Daniel, Siham Sabri, Christian Desrosiers, and Bassam Abdulkarim. "Integration of Radiomic and Multi-omic Analyses Predicts Survival of Newly Diagnosed IDH1 Wild-Type Glioblastoma." Cancers 11, no. 8 (August 10, 2019): 1148. http://dx.doi.org/10.3390/cancers11081148.
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Predictors of patient outcome derived from gene methylation, mutation, or expression are severely limited in IDH1 wild-type glioblastoma (GBM). Radiomics offers an alternative insight into tumor characteristics which can provide complementary information for predictive models. The study aimed to evaluate whether predictive models which integrate radiomic, gene, and clinical (multi-omic) features together offer an increased capacity to predict patient outcome. A dataset comprising 200 IDH1 wild-type GBM patients, derived from The Cancer Imaging Archive (TCIA) (n = 71) and the McGill University Health Centre (n = 129), was used in this study. Radiomic features (n = 45) were extracted from tumor volumes then correlated to biological variables and clinical outcomes. By performing 10-fold cross-validation (n = 200) and utilizing independent training/testing datasets (n = 100/100), an integrative model was derived from multi-omic features and evaluated for predictive strength. Integrative models using a limited panel of radiomic (sum of squares variance, large zone/low gray emphasis, autocorrelation), clinical (therapy type, age), genetic (CIC, PIK3R1, FUBP1) and protein expression (p53, vimentin) yielded a maximal AUC of 78.24% (p = 2.9 × 10−5). We posit that multi-omic models using the limited set of ‘omic’ features outlined above can improve capacity to predict the outcome for IDH1 wild-type GBM patients.
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Trister, Andrew Daniel, Brian Bot, Andrea Hawkins-Daarud, Kellie Fontes, Carly Bridge, Anne Baldock, Russ Rockne, Erich Huang, and Kristin Swanson. "Use of a novel patient-specific model of glioma growth kinetics to elucidate underlying biology as measured by gene expression microarray." Journal of Clinical Oncology 30, no. 30_suppl (October 20, 2012): 71. http://dx.doi.org/10.1200/jco.2012.30.30_suppl.71.
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71 Background: Gliomas are heterogeneous diseases with a wide distribution of growth kinetics that can be estimated prior to treatment and that are prognostic for patient outcome after treatment. Coherent molecular data sets have been made available through cooperative projects such as REMBRANDT and TCGA. We apply our novel patient specific method of measuring the net proliferation and diffusion rate from routinely available preoperative MRI sequences on patients included in these publicly available data sets to assess the underlying biology with imaging. Methods: The normalized microarray data from REMBRANDT (n=475) was used to discover a set of genes differentially expressed among GBM patients when compared with lower grade gliomas (n=853). 647 of these genes were also assessed with probesets in TCGA (n=466). Of these 466 patients, 84 also had preoperative MRI imaging available through The Cancer Imaging Archive (TCIA), for which net diffusion (D) proliferation (ρ) were estimated. Differential gene expression comparing these patients was performed. Results: 37 genes were differentially expressed with D. Genes implicated in cell adhesion, ECM maintenance and the production of focal adhesions are negatively correlated with D. Genes positively correlated with D are related to cell motility and pseudopodia formation. When considering ρ, 20 genes were found to be differentially expressed. A subset of these genes is related to hypoxia and therapy resistance. Some genes are also increased after radiation in cell-lines. Clustering on these genes revealed two classes; one with a survival advantage (p=0.0002). Conclusions: This work demonstrates the potential to assess underlying differences in biology in a heterogeneous disease through patient specific assessment of routinely available imaging. We find that more diffuse tumors will under-express genes involved in focal-adhesions and production of ECM, while they express genes in pathways related to motility and pseudopodia formation. Furthermore, tumors with high ρ are seen to express genes related to treatment resistance, which may explain worse survival in these patients. Future work will verify these markers in model organisms.
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West, Derek L., Aikaterini Kotrotsou, Andrew Scott Niekamp, Tagwa Idris, Dunia Giniebra Camejo, Nicolas James Mazal, Nicolas James Cardenas, Jackson L. Goldberg, and Rivka R. Colen. "CT-based radiomic analysis of hepatocellular carcinoma patients to predict key genomic information." Journal of Clinical Oncology 35, no. 15_suppl (May 20, 2017): e15623-e15623. http://dx.doi.org/10.1200/jco.2017.35.15_suppl.e15623.
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e15623 Background: The utilization of computed tomography (CT) has virtually replaced the need for tissue diagnosis in hepatocellular carcinoma (HCC). Imaging features (e.g. size, shape and vascularity) have been associated with patient survival. However, the full potential of CT in HCC diagnosis may not be reached, as high-throughput computing allows for extraction of quantitative features that are not part of radiologists’ lexicon. The purpose of this study was to investigate the ability of radiomic analysis to successfully identify specific doxorubicin chemoresistant genes on CT images of treatment-naïve hepatocellular carcinoma (HCC). Methods: We identified 27 treatment-naïve patients with a single HCC tumor from The Cancer Genome Atlas (TCGA) whom had gene expression profiles. Baseline CT images were obtained from The Cancer Imaging Archive (TCIA). 3D Slicer software was used for manual tumor segmentation and final segmented images were reviewed by a board-certified radiologist. Following tumor segmentation, texture analysis was performed on MATLAB environment. A total of 310 rotation invariant texture features, which measure tumor heterogeneity, were obtained (first-order histogram and grey level co-occurrence matrix). The mRMR method was used to select the most relevant radiomic features. ROC analysis and LOOCV were used to assess the performance of five specific genes known to confer doxorubicin chemoresistance (TP53, TOP2A, CTNNB1, CDKN2A and AKT1). Results: Radiomic analysis identified TP53 (AUC = 86.61%, Specificity = 92.31%, Sensitivity = 92.9%), TOP2A (AUC = 78.0%, Specificity = 69%, Sensitivity = 85.7%), CTNNB1 (AUC = 86.8%, Specificity = 92.3%, Sensitivity = 85.7%), CDKN2A (AUC = 76.9%, Specificity = 76.9%, Sensitivity = 78.6%) and AKT1 (AUC = 72.5%, Specificity = 69.2%, Sensitivity = 85.7%) in treatment-naïve HCC CT studies. Conclusions: The identification of specific genes that confer chemoresistance to doxorubicin can be reliably ascertained via the use of radiomic analysis. This study may help tailor future treatment paradigms via the ability to categorize HCC tumors on genetic level and identify tumors which may not have a favorable response to doxorubicin based therapies.
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Kha, Quang-Hien, Viet-Huan Le, Truong Nguyen Khanh Hung, and Nguyen Quoc Khanh Le. "Development and Validation of an Efficient MRI Radiomics Signature for Improving the Predictive Performance of 1p/19q Co-Deletion in Lower-Grade Gliomas." Cancers 13, no. 21 (October 27, 2021): 5398. http://dx.doi.org/10.3390/cancers13215398.
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The prognosis and treatment plans for patients diagnosed with low-grade gliomas (LGGs) may significantly be improved if there is evidence of chromosome 1p/19q co-deletion mutation. Many studies proved that the codeletion status of 1p/19q enhances the sensitivity of the tumor to different types of therapeutics. However, the current clinical gold standard of detecting this chromosomal mutation remains invasive and poses implicit risks to patients. Radiomics features derived from medical images have been used as a new approach for non-invasive diagnosis and clinical decisions. This study proposed an eXtreme Gradient Boosting (XGBoost)-based model to predict the 1p/19q codeletion status in a binary classification task. We trained our model on the public database extracted from The Cancer Imaging Archive (TCIA), including 159 LGG patients with 1p/19q co-deletion mutation status. The XGBoost was the baseline algorithm, and we combined the SHapley Additive exPlanations (SHAP) analysis to select the seven most optimal radiomics features to build the final predictive model. Our final model achieved an accuracy of 87% and 82.8% on the training set and external test set, respectively. With seven wavelet radiomics features, our XGBoost-based model can identify the 1p/19q codeletion status in LGG-diagnosed patients for better management and address the drawbacks of invasive gold-standard tests in clinical practice.
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Kalen, Joseph D., David A. Clunie, Yanling Liu, James L. Tatum, Paula M. Jacobs, Justin Kirby, John B. Freymann, et al. "Design and Implementation of the Pre-Clinical DICOM Standard in Multi-Cohort Murine Studies." Tomography 7, no. 1 (February 5, 2021): 1–9. http://dx.doi.org/10.3390/tomography7010001.
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The small animal imaging Digital Imaging and Communications in Medicine (DICOM) acquisition context structured report (SR) was developed to incorporate pre-clinical data in an established DICOM format for rapid queries and comparison of clinical and non-clinical datasets. Established terminologies (i.e., anesthesia, mouse model nomenclature, veterinary definitions, NCI Metathesaurus) were utilized to assist in defining terms implemented in pre-clinical imaging and new codes were added to integrate the specific small animal procedures and handling processes, such as housing, biosafety level, and pre-imaging rodent preparation. In addition to the standard DICOM fields, the small animal SR includes fields specific to small animal imaging such as tumor graft (i.e., melanoma), tissue of origin, mouse strain, and exogenous material, including the date and site of injection. Additionally, the mapping and harmonization developed by the Mouse-Human Anatomy Project were implemented to assist co-clinical research by providing cross-reference human-to-mouse anatomies. Furthermore, since small animal imaging performs multi-mouse imaging for high throughput, and queries for co-clinical research requires a one-to-one relation, an imaging splitting routine was developed, new Unique Identifiers (UID’s) were created, and the original patient name and ID were saved for reference to the original dataset. We report the implementation of the small animal SR using MRI datasets (as an example) of patient-derived xenograft mouse models and uploaded to The Cancer Imaging Archive (TCIA) for public dissemination, and also implemented this on PET/CT datasets. The small animal SR enhancement provides researchers the ability to query any DICOM modality pre-clinical and clinical datasets using standard vocabularies and enhances co-clinical studies.
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Agus, Minarno Eko, Sasongko Yoni Bagas, Munarko Yuda, Nugroho Adi Hanung, and Zaidah Ibrahim. "Convolutional Neural Network featuring VGG-16 Model for Glioma Classification." JOIV : International Journal on Informatics Visualization 6, no. 3 (September 30, 2022): 660. http://dx.doi.org/10.30630/joiv.6.3.1230.
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Magnetic Resonance Imaging (MRI) is a body sensing technique that can produce detailed images of the condition of organs and tissues. Specifically related to brain tumors, the resulting images can be analyzed using image detection techniques so that tumor stages can be classified automatically. Detection of brain tumors requires a high level of accuracy because it is related to the effectiveness of medical actions and patient safety. So far, the Convolutional Neural Network (CNN) or its combination with GA has given good results. For this reason, in this study, we used a similar method but with a variant of the VGG-16 architecture. VGG-16 variant adds 16 layers by modifying the dropout layer (using softmax activation) to reduce overfitting and avoid using a lot of hyper-parameters. We also experimented with using augmentation techniques to anticipate data limitations. Experiment using data The Cancer Imaging Archive (TCIA) - The Repository of Molecular Brain Neoplasia Data (REMBRANDT) contains MRI images of 130 patients with different ailments, grades, races, and ages with 520 images. The tumor type was Glioma, and the images were divided into grades II, III, and IV, with the composition of 226, 101, and 193 images, respectively. The data is divided by 68% and 32% for training and testing purposes. We found that VGG-16 was more effective for brain tumor image classification, with an accuracy of up to 100%.
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Wu, Yuxi, Zesheng Peng, Haofei Wang, and Wei Xiang. "Identifying the Hub Genes of Glioma Peritumoral Brain Edema Using Bioinformatical Methods." Brain Sciences 12, no. 6 (June 19, 2022): 805. http://dx.doi.org/10.3390/brainsci12060805.
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Glioma peritumoral brain edema (GPTBE) is a frequent complication in patients with glioma. The severity of peritumoral edema endangers patients’ life and prognosis. However, there are still questions concerning the process of GPTBE formation and evolution. In this study, the patients were split into two groups based on edema scoring findings in the cancer imaging archive (TCIA) comprising 186 TCGA-LGG patients. Using mRNA sequencing data, differential gene (DEG) expression analysis was performed, comparing the two groups to find the key genes affecting GPTBE. A functional enrichment analysis of differentially expressed genes was performed. Then, a protein–protein interaction (PPI) network was established, and important genes were screened. Gene set variation analysis (GSVA) scores were calculated for major gene sets and comparatively correlated with immune cell infiltration. Overall survival (OS) was analyzed using the Kaplan–Meier curve. A total of 59 DEGs were found, with 10 of them appearing as important genes. DEGs were shown to be closely linked to inflammatory reactions. According to the network score, IL10 was in the middle of the network. The presence of the IL10 protein in glioma tissues was verified using the human protein atlas (HPA). Furthermore, the gene sets’ GSVA scores were favorably linked with immune infiltration, particularly, with macrophages. The high-edema group had higher GSVA scores than the low-edema group. Finally, Kaplan–Meier analysis revealed no differences in OS between the two groups, and eight genes were found to be related to prognosis, whereas two genes were not. GPTBE is linked to the expression of inflammatory genes.
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Tang, Fuk-Hay, Eva-Yi-Wah Cheung, Hiu-Lam Wong, Chun-Ming Yuen, Man-Hei Yu, and Pui-Ching Ho. "Radiomics from Various Tumour Volume Sizes for Prognosis Prediction of Head and Neck Squamous Cell Carcinoma: A Voted Ensemble Machine Learning Approach." Life 12, no. 9 (September 5, 2022): 1380. http://dx.doi.org/10.3390/life12091380.
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Background: Traditionally, cancer prognosis was determined by tumours size, lymph node spread and presence of metastasis (TNM staging). Radiomics of tumour volume has recently been used for prognosis prediction. In the present study, we evaluated the effect of various sizes of tumour volume. A voted ensemble approach with a combination of multiple machine learning algorithms is proposed for prognosis prediction for head and neck squamous cell carcinoma (HNSCC). Methods: A total of 215 HNSCC CT image sets with radiotherapy structure sets were acquired from The Cancer Imaging Archive (TCIA). Six tumour volumes, including gross tumour volume (GTV), diminished GTV, extended GTV, planning target volume (PTV), diminished PTV and extended PTV were delineated. The extracted radiomics features were analysed by decision tree, random forest, extreme boost, support vector machine and generalized linear algorithms. A voted ensemble machine learning (VEML) model that optimizes the above algorithms was used. The receiver operating characteristic area under the curve (ROC-AUC) were used to compare the performance of machine learning methods, including accuracy, sensitivity and specificity. Results: The VEML model demonstrated good prognosis prediction ability for all sizes of tumour volumes with reference to GTV and PTV with high accuracy of up to 88.3%, sensitivity of up to 79.9% and specificity of up to 96.6%. There was no significant difference between the various target volumes for the prognostic prediction of HNSCC patients (chi-square test, p > 0.05). Conclusions: Our study demonstrates that the proposed VEML model can accurately predict the prognosis of HNSCC patients using radiomics features from various tumour volumes.
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Ninomiya, Kenta, Hidetaka Arimura, Wai Yee Chan, Kentaro Tanaka, Shinichi Mizuno, Nadia Fareeda Muhammad Gowdh, Nur Adura Yaakup, Chong-Kin Liam, Chee-Shee Chai, and Kwan Hoong Ng. "Robust radiogenomics approach to the identification of EGFR mutations among patients with NSCLC from three different countries using topologically invariant Betti numbers." PLOS ONE 16, no. 1 (January 11, 2021): e0244354. http://dx.doi.org/10.1371/journal.pone.0244354.
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Objectives To propose a novel robust radiogenomics approach to the identification of epidermal growth factor receptor (EGFR) mutations among patients with non-small cell lung cancer (NSCLC) using Betti numbers (BNs). Materials and methods Contrast enhanced computed tomography (CT) images of 194 multi-racial NSCLC patients (79 EGFR mutants and 115 wildtypes) were collected from three different countries using 5 manufacturers’ scanners with a variety of scanning parameters. Ninety-nine cases obtained from the University of Malaya Medical Centre (UMMC) in Malaysia were used for training and validation procedures. Forty-one cases collected from the Kyushu University Hospital (KUH) in Japan and fifty-four cases obtained from The Cancer Imaging Archive (TCIA) in America were used for a test procedure. Radiomic features were obtained from BN maps, which represent topologically invariant heterogeneous characteristics of lung cancer on CT images, by applying histogram- and texture-based feature computations. A BN-based signature was determined using support vector machine (SVM) models with the best combination of features that maximized a robustness index (RI) which defined a higher total area under receiver operating characteristics curves (AUCs) and lower difference of AUCs between the training and the validation. The SVM model was built using the signature and optimized in a five-fold cross validation. The BN-based model was compared to conventional original image (OI)- and wavelet-decomposition (WD)-based models with respect to the RI between the validation and the test. Results The BN-based model showed a higher RI of 1.51 compared with the models based on the OI (RI: 1.33) and the WD (RI: 1.29). Conclusion The proposed model showed higher robustness than the conventional models in the identification of EGFR mutations among NSCLC patients. The results suggested the robustness of the BN-based approach against variations in image scanner/scanning parameters.
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Fedorov, Andriy, David Clunie, Ethan Ulrich, Christian Bauer, Andreas Wahle, Bartley Brown, Michael Onken, et al. "DICOM for quantitative imaging biomarker development: a standards based approach to sharing clinical data and structured PET/CT analysis results in head and neck cancer research." PeerJ 4 (May 24, 2016): e2057. http://dx.doi.org/10.7717/peerj.2057.
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Background.Imaging biomarkers hold tremendous promise for precision medicine clinical applications. Development of such biomarkers relies heavily on image post-processing tools for automated image quantitation. Their deployment in the context of clinical research necessitates interoperability with the clinical systems. Comparison with the established outcomes and evaluation tasks motivate integration of the clinical and imaging data, and the use of standardized approaches to support annotation and sharing of the analysis results and semantics. We developed the methodology and tools to support these tasks in Positron Emission Tomography and Computed Tomography (PET/CT) quantitative imaging (QI) biomarker development applied to head and neck cancer (HNC) treatment response assessment, using the Digital Imaging and Communications in Medicine (DICOM®) international standard and free open-source software.Methods.Quantitative analysis of PET/CT imaging data collected on patients undergoing treatment for HNC was conducted. Processing steps included Standardized Uptake Value (SUV) normalization of the images, segmentation of the tumor using manual and semi-automatic approaches, automatic segmentation of the reference regions, and extraction of the volumetric segmentation-based measurements. Suitable components of the DICOM standard were identified to model the various types of data produced by the analysis. A developer toolkit of conversion routines and an Application Programming Interface (API) were contributed and applied to create a standards-based representation of the data.Results. DICOM Real World Value Mapping, Segmentation and Structured Reporting objects were utilized for standards-compliant representation of the PET/CT QI analysis results and relevant clinical data. A number of correction proposals to the standard were developed. The open-source DICOM toolkit (DCMTK) was improved to simplify the task of DICOM encoding by introducing new API abstractions. Conversion and visualization tools utilizing this toolkit were developed. The encoded objects were validated for consistency and interoperability. The resulting dataset was deposited in the QIN-HEADNECK collection of The Cancer Imaging Archive (TCIA). Supporting tools for data analysis and DICOM conversion were made available as free open-source software.Discussion.We presented a detailed investigation of the development and application of the DICOM model, as well as the supporting open-source tools and toolkits, to accommodate representation of the research data in QI biomarker development. We demonstrated that the DICOM standard can be used to represent the types of data relevant in HNC QI biomarker development, and encode their complex relationships. The resulting annotated objects are amenable to data mining applications, and are interoperable with a variety of systems that support the DICOM standard.
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Lv, Wenbing, Hui Xu, Xu Han, Hao Zhang, Jianhua Ma, Arman Rahmim, and Lijun Lu. "Context-Aware Saliency Guided Radiomics: Application to Prediction of Outcome and HPV-Status from Multi-Center PET/CT Images of Head and Neck Cancer." Cancers 14, no. 7 (March 25, 2022): 1674. http://dx.doi.org/10.3390/cancers14071674.
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Purpose: This multi-center study aims to investigate the prognostic value of context-aware saliency-guided radiomics in 18F-FDG PET/CT images of head and neck cancer (HNC). Methods: 806 HNC patients (training vs. validation vs. external testing: 500 vs. 97 vs. 209) from 9 centers were collected from The Cancer Imaging Archive (TCIA). There were 100/384 and 60/123 oropharyngeal carcinoma (OPC) patients with human papillomavirus (HPV) status in training and testing cohorts, respectively. Six types of images were used for radiomics feature extraction and further model construction, namely (i) the original image (Origin), (ii) a context-aware saliency map (SalMap), (iii, iv) high- or low-saliency regions in the original image (highSal or lowSal), (v) a saliency-weighted image (SalxImg), and finally, (vi) a fused PET-CT image (FusedImg). Four outcomes were evaluated, i.e., recurrence-free survival (RFS), metastasis-free survival (MFS), overall survival (OS), and disease-free survival (DFS), respectively. Multivariate Cox analysis and logistic regression were adopted to construct radiomics scores for the prediction of outcome (Rad_Ocm) and HPV-status (Rad_HPV), respectively. Besides, the prognostic value of their integration (Rad_Ocm_HPV) was also investigated. Results: In the external testing cohort, compared with the Origin model, SalMap and SalxImg achieved the highest C-indices for RFS (0.621 vs. 0.559) and MFS (0.785 vs. 0.739) predictions, respectively, while FusedImg performed the best for both OS (0.685 vs. 0.659) and DFS (0.641 vs. 0.582) predictions. In the OPC HPV testing cohort, FusedImg showed higher AUC for HPV-status prediction compared with the Origin model (0.653 vs. 0.484). In the OPC testing cohort, compared with Rad_Ocm or Rad_HPV alone, Rad_Ocm_HPV performed the best for OS and DFS predictions with C-indices of 0.702 (p = 0.002) and 0.684 (p = 0.006), respectively. Conclusion: Saliency-guided radiomics showed enhanced performance for both outcome and HPV-status predictions relative to conventional radiomics. The radiomics-predicted HPV status also showed complementary prognostic value.
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Chi, Zhang, Yu, Wang, and Wu. "Computed Tomography (CT) Image Quality Enhancement via a Uniform Framework Integrating Noise Estimation and Super-Resolution Networks." Sensors 19, no. 15 (July 30, 2019): 3348. http://dx.doi.org/10.3390/s19153348.
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Computed tomography (CT) imaging technology has been widely used to assist medical diagnosis in recent years. However, noise during the process of imaging, and data compression during the process of storage and transmission always interrupt the image quality, resulting in unreliable performance of the post-processing steps in the computer assisted diagnosis system (CADs), such as medical image segmentation, feature extraction, and medical image classification. Since the degradation of medical images typically appears as noise and low-resolution blurring, in this paper, we propose a uniform deep convolutional neural network (DCNN) framework to handle the de-noising and super-resolution of the CT image at the same time. The framework consists of two steps: Firstly, a dense-inception network integrating an inception structure and dense skip connection is proposed to estimate the noise level. The inception structure is used to extract the noise and blurring features with respect to multiple receptive fields, while the dense skip connection can reuse those extracted features and transfer them across the network. Secondly, a modified residual-dense network combined with joint loss is proposed to reconstruct the high-resolution image with low noise. The inception block is applied on each skip connection of the dense-residual network so that the structure features of the image are transferred through the network more than the noise and blurring features. Moreover, both the perceptual loss and the mean square error (MSE) loss are used to restrain the network, leading to better performance in the reconstruction of image edges and details. Our proposed network integrates the degradation estimation, noise removal, and image super-resolution in one uniform framework to enhance medical image quality. We apply our method to the Cancer Imaging Archive (TCIA) public dataset to evaluate its ability in medical image quality enhancement. The experimental results demonstrate that the proposed method outperforms the state-of-the-art methods on de-noising and super-resolution by providing higher peak signal to noise ratio (PSNR) and structure similarity index (SSIM) values.
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Hrapșa, Iona, Ioan Florian, Sergiu Șușman, Marius Farcaș, Lehel Beni, and Ioan Florian. "External Validation of a Convolutional Neural Network for IDH Mutation Prediction." Medicina 58, no. 4 (April 9, 2022): 526. http://dx.doi.org/10.3390/medicina58040526.
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Background and Objectives: The IDH (isocitrate dehydrogenase) status represents one of the main prognosis factors for gliomas. However, determining it requires invasive procedures and specialized surgical skills. Medical imaging such as MRI is essential in glioma diagnosis and management. Lately, fields such as Radiomics and Radiogenomics emerged as pertinent prediction tools for extracting molecular information out of medical images. These fields are based on Artificial Intelligence algorithms that require external validation in order to evaluate their general performance. The aim of this study was to provide an external validation for the algorithm formulated by Yoon Choi et al. of IDH status prediction using preoperative common MRI sequences and patient age. Material and Methods: We applied Choi’s IDH status prediction algorithm on T1c, T2 and FLAIR preoperative MRI images of gliomas (grades WHO II-IV) of 21 operated adult patients from the Neurosurgery clinic of the Cluj County Emergency Clinical Hospital (CCECH), Cluj-Napoca Romania. We created a script to automate the testing process with DICOM format MRI sequences as input and IDH predicted status as output. Results: In terms of patient characteristics, the mean age was 48.6 ± 15.6; 57% were female and 43% male; 43% were IDH positive and 57% IDH negative. The proportions of WHO grades were 24%, 14% and 62% for II, III and IV, respectively. The validation test achieved a relative accuracy of 76% with 95% CI of (53%, 92%) and an Area Under the Curve (AUC) through DeLong et al. method of 0.74 with 95% CI of (0.53, 0.91) and a p of 0.021. Sensitivity and Specificity were 0.78 with 95% CI of (0.45, 0.96) and 0.75 with 95% CI of (0.47, 0.91), respectively. Conclusions: Although our results match the external test the author made on The Cancer Imaging Archive (TCIA) online dataset, performance of the algorithm on external data is still not high enough for clinical application. Radiogenomic approaches remain a high interest research field that may provide a rapid and accurate diagnosis and prognosis of patients with intracranial glioma.
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Veeraraghavan, Harini, Herbert Alberto Vargas, Alejandro-Jiménez Sánchez, Maura Micco, Eralda Mema, Yulia Lakhman, Mireia Crispin-Ortuzar, et al. "Integrated Multi-Tumor Radio-Genomic Marker of Outcomes in Patients with High Serous Ovarian Carcinoma." Cancers 12, no. 11 (November 17, 2020): 3403. http://dx.doi.org/10.3390/cancers12113403.
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Purpose: Develop an integrated intra-site and inter-site radiomics-clinical-genomic marker of high grade serous ovarian cancer (HGSOC) outcomes and explore the biological basis of radiomics with respect to molecular signaling pathways and the tumor microenvironment (TME). Method: Seventy-five stage III-IV HGSOC patients from internal (N = 40) and external factors via the Cancer Imaging Archive (TCGA) (N = 35) with pre-operative contrast enhanced CT, attempted primary cytoreduction, at least two disease sites, and molecular analysis performed within TCGA were retrospectively analyzed. An intra-site and inter-site radiomics (cluDiss) measure was combined with clinical-genomic variables (iRCG) and compared against conventional (volume and number of sites) and average radiomics (N = 75) for prognosticating progression-free survival (PFS) and platinum resistance. Correlation with molecular signaling and TME derived using a single sample gene set enrichment that was measured. Results: The iRCG model had the best platinum resistance classification accuracy (AUROC of 0.78 [95% CI 0.77 to 0.80]). CluDiss was associated with PFS (HR 1.03 [95% CI: 1.01 to 1.05], p = 0.002), negatively correlated with Wnt signaling, and positively to immune TME. Conclusions: CluDiss and the iRCG prognosticated HGSOC outcomes better than conventional and average radiomic measures and could better stratify patient outcomes if validated on larger multi-center trials.
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Kays, Joshua K., Leonidas G. Koniaris, Caleb A. Cooper, Roberto Pili, Guanglong Jiang, Yunlong Liu, and Teresa A. Zimmers. "The Combination of Low Skeletal Muscle Mass and High Tumor Interleukin-6 Associates with Decreased Survival in Clear Cell Renal Cell Carcinoma." Cancers 12, no. 6 (June 17, 2020): 1605. http://dx.doi.org/10.3390/cancers12061605.
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Clear cell renal carcinoma (ccRCC) is frequently associated with cachexia which is itself associated with decreased survival and quality of life. We examined relationships among body phenotype, tumor gene expression, and survival. Demographic, clinical, computed tomography (CT) scans and tumor RNASeq for 217 ccRCC patients were acquired from the Cancer Imaging Archive and The Cancer Genome Atlas (TCGA). Skeletal muscle and fat masses measured from CT scans and tumor cytokine gene expression were compared with survival by univariate and multivariate analysis. Patients in the lowest skeletal muscle mass (SKM) quartile had significantly shorter overall survival versus the top three SKM quartiles. Patients who fell into the lowest quartiles for visceral adipose mass (VAT) and subcutaneous adipose mass (SCAT) also demonstrated significantly shorter overall survival. Multiple tumor cytokines correlated with mortality, most strongly interleukin-6 (IL-6); high IL-6 expression was associated with significantly decreased survival. The combination of low SKM/high IL-6 was associated with significantly lower overall survival compared to high SKM/low IL-6 expression (26.1 months vs. not reached; p < 0.001) and an increased risk of mortality (HR = 5.95; 95% CI = 2.86–12.38). In conclusion, tumor cytokine expression, body composition, and survival are closely related, with low SKM/high IL-6 expression portending worse prognosis in ccRCC.
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Kalpathy-Cramer, Jayashree, John Blake Freymann, Justin Stephen Kirby, Paul Eugene Kinahan, and Fred William Prior. "Quantitative Imaging Network: Data Sharing and Competitive AlgorithmValidation Leveraging The Cancer Imaging Archive." Translational Oncology 7, no. 1 (February 2014): 147–52. http://dx.doi.org/10.1593/tlo.13862.
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Pan, Zi-Qi, Shu-Jun Zhang, Xiang-Lian Wang, Yu-Xin Jiao, and Jian-Jian Qiu. "Machine Learning Based on a Multiparametric and Multiregional Radiomics Signature Predicts Radiotherapeutic Response in Patients with Glioblastoma." Behavioural Neurology 2020 (October 24, 2020): 1–12. http://dx.doi.org/10.1155/2020/1712604.
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Background and Objective. Although radiotherapy has become one of the main treatment methods for cancer, there is no noninvasive method to predict the radiotherapeutic response of individual glioblastoma (GBM) patients before surgery. The purpose of this study is to develop and validate a machine learning-based radiomics signature to predict the radiotherapeutic response of GBM patients. Methods. The MRI images, genetic data, and clinical data of 152 patients with GBM were analyzed. 122 patients from the TCIA dataset (training set: n = 82 ; validation set: n = 40 ) and 30 patients from local hospitals were used as an independent test dataset. Radiomics features were extracted from multiple regions of multiparameter MRI. Kaplan-Meier survival analysis was used to verify the ability of the imaging signature to predict the response of GBM patients to radiotherapy before an operation. Multivariate Cox regression including radiomics signature and preoperative clinical risk factors was used to further improve the ability to predict the overall survival (OS) of individual GBM patients, which was presented in the form of a nomogram. Results. The radiomics signature was built by eight selected features. The C -index of the radiomics signature in the TCIA and independent test cohorts was 0.703 ( P < 0.001 ) and 0.757 ( P = 0.001 ), respectively. Multivariate Cox regression analysis confirmed that the radiomics signature (HR: 0.290, P < 0.001 ), age (HR: 1.023, P = 0.01 ), and KPS (HR: 0.968, P < 0.001 ) were independent risk factors for OS in GBM patients before surgery. When the radiomics signature and preoperative clinical risk factors were combined, the radiomics nomogram further improved the performance of OS prediction in individual patients ( C ‐ index = 0.764 and 0.758 in the TCIA and test cohorts, respectively). Conclusion. This study developed a radiomics signature that can predict the response of individual GBM patients to radiotherapy and may be a new supplement for precise GBM radiotherapy.
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Kinoshita, Manabu, Hideyuki Arita, Masamichi Takahashi, Takehiro Uda, Junya Fukai, Kenichi Ishibashi, Noriyuki Kijima, et al. "NIMG-11. IMPACT OF INVERSION TIME FOR FLAIR ACQUISITION ON THE T2-FLAIR MISMATCH DETECTABILITY FOR IDH-MUTANT, NON-CODEL ASTROCYTOMAS." Neuro-Oncology 22, Supplement_2 (November 2020): ii149. http://dx.doi.org/10.1093/neuonc/noaa215.624.
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Abstract PURPOSE The current research tested the hypothesis that TI shorter than 2400 ms under 3T for FLAIR can improve the diagnostic accuracy of the T2-FLAIR mismatch sign for identifying IDHmt, non-CODEL astrocytomas. EXPERIMENTAL DESIGN We prepared three different cohorts; 94 MRI from 76 IDHmt, non-CODEL LrGGs, 33 MRI from 31 LrGG under the restriction of FLAIR being acquired with TI < 2400 ms for 3T or 2016 ms for 1.5T, and 103 MRI from 103 patients from the TCIA/TCGA dataset for LrGG. The presence or absence of the “T2-FLAIR mismatch sign” was evaluated, and we compared diagnostic accuracies according to TI used for FLAIR acquisition. RESULTS The T2-FLAIR mismatch sign was more frequently positive when TI was shorter than 2400 ms under 3T for FLAIR acquisition (p = 0.0009, Fisher’s exact test). The T2-FLAIR mismatch sign was positive only for IDHmt, non-CODEL astrocytomas even if we confined the cohort with FLAIR acquired with shorter TI (p = 0.0001, Fisher’s exact test). TCIA/TCGA dataset validated that the sensitivity, specificity, PPV, and NPV of the T2-FLAIR mismatch sign to identify IDHmt, non-CODEL astrocytomas improved from 31%, 90%, 79%, and 51% to 67%, 94%, 92%, and 74%, respectively if we acquired FLAIR with TI shorter than 2400 ms. CONCLUSIONS We revealed that TI for FLAIR impacts diagnostic accuracy of the T2-FLAIR mismatch sign and that FLAIR scanned with TI < 2400 ms in 3T is necessary for LrGG imaging.
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Russell, Pamela, Kelly Fountain, Dulcy Wolverton, and Debashis Ghosh. "TCIApathfinder: An R Client for the Cancer Imaging Archive REST API." Cancer Research 78, no. 15 (June 5, 2018): 4424–26. http://dx.doi.org/10.1158/0008-5472.can-18-0678.
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Patel, Sohil H., Laila M. Poisson, Daniel J. Brat, Yueren Zhou, Lee Cooper, Matija Snuderl, Cheddhi Thomas, et al. "T2–FLAIR Mismatch, an Imaging Biomarker for IDH and 1p/19q Status in Lower-grade Gliomas: A TCGA/TCIA Project." Clinical Cancer Research 23, no. 20 (July 27, 2017): 6078–85. http://dx.doi.org/10.1158/1078-0432.ccr-17-0560.
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Chaddad, Ahmad, Michael Kucharczyk, and Tamim Niazi. "Multimodal Radiomic Features for the Predicting Gleason Score of Prostate Cancer." Cancers 10, no. 8 (July 28, 2018): 249. http://dx.doi.org/10.3390/cancers10080249.
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Background: Novel radiomic features are enabling the extraction of biological data from routine sequences of MRI images. This study’s purpose was to establish a new model, based on the joint intensity matrix (JIM), to predict the Gleason score (GS) of prostate cancer (PCa) patients. Methods: A retrospective dataset comprised of the diagnostic imaging data of 99 PCa patients was used, extracted from The Cancer Imaging Archive’s (TCIA) T2-Weighted (T2-WI) and apparent diffusion coefficient (ADC) images. Radiomic features derived from JIM and the grey level co-occurrence matrix (GLCM) were extracted from the reported tumor locations. The Kruskal-Wallis test and Spearman’s rank correlation identified features related to the GS. The Random Forest classifier model was implemented to identify the best performing signature of JIM and GLCM radiomic features to predict for GS. Results: Five JIM-derived features: contrast, homogeneity, difference variance, dissimilarity, and inverse difference were independent predictors of GS (p < 0.05). Combined JIM and GLCM analysis provided the best performing area-under-the-curve, with values of 78.40% for GS ≤ 6, 82.35% for GS = 3 + 4, and 64.76% for GS ≥ 4 + 3. Conclusion: This retrospective study produced a novel predictive model for GS by the incorporation of JIM data from standard diagnostic MRI images.
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Protonotarios, Nicholas E., Iason Katsamenis, Stavros Sykiotis, Nikolaos Dikaios, George A. Kastis, Sofia N. Chatziioannou, Marinos Metaxas, Nikolaos Doulamis, and Anastasios Doulamis. "A few-shot U-Net deep learning model for lung cancer lesion segmentation via PET/CT imaging." Biomedical Physics & Engineering Express 8, no. 2 (February 18, 2022): 025019. http://dx.doi.org/10.1088/2057-1976/ac53bd.
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Abstract Over the past few years, positron emission tomography/computed tomography (PET/CT) imaging for computer-aided diagnosis has received increasing attention. Supervised deep learning architectures are usually employed for the detection of abnormalities, with anatomical localization, especially in the case of CT scans. However, the main limitations of the supervised learning paradigm include (i) large amounts of data required for model training, and (ii) the assumption of fixed network weights upon training completion, implying that the performance of the model cannot be further improved after training. In order to overcome these limitations, we apply a few-shot learning (FSL) scheme. Contrary to traditional deep learning practices, in FSL the model is provided with less data during training. The model then utilizes end-user feedback after training to constantly improve its performance. We integrate FSL in a U-Net architecture for lung cancer lesion segmentation on PET/CT scans, allowing for dynamic model weight fine-tuning and resulting in an online supervised learning scheme. Constant online readjustments of the model weights according to the users’ feedback, increase the detection and classification accuracy, especially in cases where low detection performance is encountered. Our proposed method is validated on the Lung-PET-CT-DX TCIA database. PET/CT scans from 87 patients were included in the dataset and were acquired 60 minutes after intravenous 18F-FDG injection. Experimental results indicate the superiority of our approach compared to other state-of-the-art methods.
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Steed, T. C., J. M. Treiber, K. S. Patel, Z. Taich, N. S. White, M. L. Treiber, N. Farid, B. S. Carter, A. M. Dale, and C. C. Chen. "Iterative Probabilistic Voxel Labeling: Automated Segmentation for Analysis of The Cancer Imaging Archive Glioblastoma Images." American Journal of Neuroradiology 36, no. 4 (November 20, 2014): 678–85. http://dx.doi.org/10.3174/ajnr.a4171.
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Kirby, J., L. Tarbox, J. Freymann, C. Jaffe, and F. Prior. "TU-AB-BRA-03: The Cancer Imaging Archive: Supporting Radiomic and Imaging Genomic Research with Open-Access Data Sets." Medical Physics 42, no. 6Part31 (June 2015): 3587. http://dx.doi.org/10.1118/1.4925508.
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Taha, Birra, Taihui Li, Daniel Boley, Clark C. Chen, and Ju Sun. "Detection of Isocitrate Dehydrogenase Mutated Glioblastomas Through Anomaly Detection Analytics." Neurosurgery 89, no. 2 (April 22, 2021): 323–28. http://dx.doi.org/10.1093/neuros/nyab130.
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Abstract BACKGROUND The rarity of Isocitrate Dehydrogenase mutated (mIDH) glioblastomas relative to wild-type IDH glioblastomas, as well as their distinct tumor physiology, effectively render them “outliers”. Specialized tools are needed to identify these outliers. OBJECTIVE To carefully craft and apply anomaly detection methods to identify mIDH glioblastoma based on radiomic features derived from magnetic resonance imaging. METHODS T1-post gadolinium images for 188 patients and 138 patients were downloaded from The Cancer Imaging Archive's (TCIA) The Cancer Genome Atlas (TCGA) glioblastoma collection, and from the University of Minnesota Medical Center (UMMC), respectively. Anomaly detection methods were tested on glioblastoma image features for the precision of mIDH detection and compared to standard classification methods. RESULTS Using anomaly detection training methods, we were able to detect IDH mutations from features in noncontrast-enhancing regions in glioblastoma with an average precision of 75.0%, 69.9%, and 69.8% using three different models. Anomaly detection methods consistently outperformed traditional two-class classification methods from 2 unique learning models (67.9%, 67.6%). The disparity in performances could not be overcome through newer, popular models such as neural networks (67.4%). CONCLUSION We employed an anomaly detection strategy in the detection of IDH mutation in glioblastoma using preoperative T1 postcontrast imaging. We show these methods outperform traditional two-class classification in the setting of dataset imbalances inherent to IDH mutation prevalence in glioblastoma. We validate our results using an external dataset and highlight new possible avenues for radiogenomic rare event prediction in glioblastoma and beyond.
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Moyya, Priscilla Dinkar, Mythili Asaithambi, and Anandh Kilpattu Ramaniharan. "RADIOMICS BASED BREAST MALIGNANCY INDEX TO DIFFERENTIATE PATHOLOGICAL CHANGES DUE TO NEOADJUVANT CHEMOTHERAPY." Biomedical Sciences Instrumentation 57, no. 2 (April 1, 2021): 219–27. http://dx.doi.org/10.34107/yhpn9422.04219.
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The leading cause of deaths among women in the world is Breast Cancer. Neoadjuvant chemotherapy (NAC) offers effective treatment results, thus reducing tumor aggression and allowing treatment monitoring. The Dynamic Contrast Enhanced (DCE) MRI plays a vital role in assessing the treatment response due to NAC. However, quantifying the treatment response in low-grade tumours is visually challenging. Radiomics is an evolving field of medical imaging that reflects the histopathological variations in breast tissues. Integrating radiomics with breast DCE-MRI provides clinically useful measures in evaluating the NAC response. In this work, we have formulated an index called Radiomics based Breast Malignancy Index (RBMI) using texture and Haar wavelets to differentiate the radiological differences of breast tissue due to NAC. The statistically significant radiomic features extracted from 20 DCE-MR images obtained using TCIA database were used in the calculation of RBMI. Results show that, RBMI could statistically differentiate (p=0.007) the treatment response between visit-1 & 2 due to NAC with mean and standard deviation values of 334706.5949 ± 93952.5123 and 296354.9720 ± 77120.6718 respectively. Hence, RBMI seems to be a clinically adjunct measure in evaluating the treatment response of breast cancer due to NAC.
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Davatzikos, Christos, Chaitra Badve, Anahita Fathi Kazerooni, Vishnu Bashyam, Spyridon Bakas, Rivka Colen, Abhishek Mahajan, et al. "NIMG-66. AI-BASED PROGNOSTIC IMAGING BIOMARKERS FOR PRECISION NEUROONCOLOGY AND THE RESPOND CONSORTIUM." Neuro-Oncology 22, Supplement_2 (November 2020): ii162—ii163. http://dx.doi.org/10.1093/neuonc/noaa215.679.
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Abstract AI-based methods have shown great promise in a variety of biomedical research fields, including neurooncologic imaging. For example, machine learning methods have offered informative predictions of overall survival (OS) and progression-free survival (PFS), differentiation between pseudoprogression (PsP) and progressive disease (PD), and estimation of mutational status from imaging data. Despite their promise, AI, and especially the emerging deep learning (DL) methods, are challenged by several factors, including imaging heterogeneity across scanners and lack of sufficiently large and diverse training datasets, which limits their reproducibility and general acceptance. These challenges prompted the development of the ReSPOND (Radiomics Signatures for PrecisiON Diagnostics) consortium on glioblastoma, a growing effort to bring together a community of researchers sharing imaging, demographic, clinical and (currently) limited molecular data in order to address the following aims: 1) pool and harmonize data across diverse hospitals and patient populations worldwide; 2) derive robust and generalizable AI models for prediction of (initially) OS, PFS, PsP vs. PD, and recurrence; 3) test these predictive models across multiple sites. In its first phase, ReSPOND aims to pool together approximately 3,000MRI scans (from 10institutions plus TCIA), along with demographics, KPS, and (for a subset) MGMT/IDH1 status. We present initial results testing the generalization of a previously trained model of OS on 505Penn datasets to 2independent cohorts from Case Western Reserve University and University Hospitals (N=44), and Penn (N=67). The results indicate good generalization, with correlation coefficients between OS/predicted-OS between 0.25 to 0.5, depending on variable availability, which is comparable to cross-validated accuracy previously obtained from the training set itself (N=505). Additional preliminary studies evaluating prediction of future recurrence from baseline pre-operative scans in de novo patients (Penn model applied to CWR) indicated potential for guiding targeted dose escalation and supra-total resection (excellent predictions in 6/12 patients, modest in 1/12, and poor in 5/12).