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Huang, Yi-Ching, Yi-Shan Tsai, Chung-I. Li, Ren-Hao Chan, Yu-Min Yeh, Po-Chuan Chen, Meng-Ru Shen, and Peng-Chan Lin. "Adjusted CT Image-Based Radiomic Features Combined with Immune Genomic Expression Achieve Accurate Prognostic Classification and Identification of Therapeutic Targets in Stage III Colorectal Cancer." Cancers 14, no. 8 (April 8, 2022): 1895. http://dx.doi.org/10.3390/cancers14081895.
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To evaluate whether adjusted computed tomography (CT) scan image-based radiomics combined with immune genomic expression can achieve accurate stratification of cancer recurrence and identify potential therapeutic targets in stage III colorectal cancer (CRC), this cohort study enrolled 71 patients with postoperative stage III CRC. Based on preoperative CT scans, radiomic features were extracted and selected to build pixel image data using covariate-adjusted tensor classification in the high-dimension (CATCH) model. The differentially expressed RNA genes, as radiomic covariates, were identified by cancer recurrence. Predictive models were built using the pixel image and immune genomic expression factors, and the area under the curve (AUC) and F1 score were used to evaluate their performance. Significantly adjusted radiomic features were selected to predict recurrence. The association between the significantly adjusted radiomic features and immune gene expression was also investigated. Overall, 1037 radiomic features were converted into 33 × 32-pixel image data. Thirty differentially expressed genes were identified. We performed 100 iterations of 3-fold cross-validation to evaluate the performance of the CATCH model, which showed a high sensitivity of 0.66 and an F1 score of 0.69. The area under the curve (AUC) was 0.56. Overall, ten adjusted radiomic features were significantly associated with cancer recurrence in the CATCH model. All of these methods are texture-associated radiomics. Compared with non-adjusted radiomics, 7 out of 10 adjusted radiomic features influenced recurrence-free survival. The adjusted radiomic features were positively associated with PECAM1, PRDM1, AIF1, IL10, ISG20, and TLR8 expression. We provide individualized cancer therapeutic strategies based on adjusted radiomic features in recurrent stage III CRC. Adjusted CT scan image-based radiomics with immune genomic expression covariates using the CATCH model can efficiently predict cancer recurrence. The correlation between adjusted radiomic features and immune genomic expression can provide biological relevance and individualized therapeutic targets.
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Ryan, Sarah M., Tasha E. Fingerlin, Margaret Mroz, Briana Barkes, Nabeel Hamzeh, Lisa A. Maier, and Nichole E. Carlson. "Radiomic measures from chest high-resolution computed tomography associated with lung function in sarcoidosis." European Respiratory Journal 54, no. 2 (June 13, 2019): 1900371. http://dx.doi.org/10.1183/13993003.00371-2019.
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IntroductionPulmonary sarcoidosis is a rare heterogeneous lung disease of unknown aetiology, with limited treatment options. Phenotyping relies on clinical testing including visual scoring of chest radiographs. Objective radiomic measures from high-resolution computed tomography (HRCT) may provide additional information to assess disease status. As the first radiomics analysis in sarcoidosis, we investigate the potential of radiomic measures as biomarkers for sarcoidosis, by assessing 1) differences in HRCT between sarcoidosis subjects and healthy controls, 2) associations between radiomic measures and spirometry, and 3) trends between Scadding stages.MethodsRadiomic features were computed on HRCT in three anatomical planes. Linear regression compared global radiomic features between sarcoidosis subjects (n=73) and healthy controls (n=78), and identified associations with spirometry. Spatial differences in associations across the lung were investigated using functional data analysis. A subanalysis compared radiomic features between Scadding stages.ResultsGlobal radiomic measures differed significantly between sarcoidosis subjects and controls (p<0.001 for skewness, kurtosis, fractal dimension and Geary'sC), with differences in spatial radiomics most apparent in superior and lateral regions. In sarcoidosis subjects, there were significant associations between radiomic measures and spirometry, with a large association found between Geary'sCand forced vital capacity (FVC) (p=0.008). Global radiomic measures differed significantly between Scadding stages (p<0.032), albeit nonlinearly, with stage IV having more extreme radiomic values. Radiomics explained 71.1% of the variability in FVC compared with 51.4% by Scadding staging alone.ConclusionsRadiomic HRCT measures objectively differentiate disease abnormalities, associate with lung function and identify trends in Scadding stage, showing promise as quantitative biomarkers for pulmonary sarcoidosis.
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Hu, Yumin, Qiaoyou Weng, Haihong Xia, Tao Chen, Chunli Kong, Weiyue Chen, Peipei Pang, Min Xu, Chenying Lu, and Jiansong Ji. "A radiomic nomogram based on arterial phase of CT for differential diagnosis of ovarian cancer." Abdominal Radiology 46, no. 6 (June 2021): 2384–92. http://dx.doi.org/10.1007/s00261-021-03120-w.
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Abstract Purpose To develop and validate a radiomic nomogram based on arterial phase of CT to discriminate the primary ovarian cancers (POCs) and secondary ovarian cancers (SOCs). Methods A total of 110 ovarian cancer patients in our hospital were reviewed from January 2010 to December 2018. Radiomic features based on the arterial phase of CT were extracted by Artificial Intelligence Kit software (A.K. software). The least absolute shrinkage and selection operation regression (LASSO) was employed to select features and construct the radiomics score (Rad-score) for further radiomics signature calculation. Multivariable logistic regression analysis was used to develop the predicting model. The predictive nomogram model was composed of rad-score and clinical data. Nomogram discrimination and calibration were evaluated. Results Two radiomic features were selected to build the radiomics signature. The radiomics nomogram that incorporated 2 radiomics signature and 2 clinical factors (CA125 and CEA) showed good discrimination in training cohort (AUC 0.854), yielding the sensitivity of 78.8% and specificity of 90.7%, which outperformed the prediction model based on radiomics signature or clinical data alone. A visualized differential nomogram based on the radiomic score, CEA, and CA125 level was established. The calibration curve demonstrated the clinical usefulness of the proposed nomogram. Conclusion The presented nomogram, which incorporated radiomic features of arterial phase of CT with clinical features, could be useful for differentiating the primary and secondary ovarian cancers.
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Hu, Shuyi, Xiajie Lyu, Weifeng Li, Xiaohan Cui, Qiaoyu Liu, Xiaoliang Xu, Jincheng Wang, Lin Chen, Xudong Zhang, and Yin Yin. "Radiomics Analysis on Noncontrast CT for Distinguishing Hepatic Hemangioma (HH) and Hepatocellular Carcinoma (HCC)." Contrast Media & Molecular Imaging 2022 (June 25, 2022): 1–8. http://dx.doi.org/10.1155/2022/7693631.
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Background. To form a radiomic model on the basis of noncontrast computed tomography (CT) to distinguish hepatic hemangioma (HH) and hepatocellular carcinoma (HCC). Methods. In this retrospective study, a total of 110 patients were reviewed, including 72 HCC and 38 HH. We accomplished feature selection with the least absolute shrinkage and operator (LASSO) and built a radiomics signature. Another improved model (radiomics index) was established using forward conditional multivariate logistic regression. Both models were tested in an internal validation group (38 HCC and 21 HH). Results. The radiomic signature we built including 5 radiomic features demonstrated significant differences between the hepatic HH and HCC groups P < 0.05 . The improved model demonstrated a higher net benefit based on only 2 radiomic features. In the validation group, radiomics signature and radiomics index achieved great diagnostic performance with AUC values of 0.716 (95% confidence interval (CI): 0.581, 0.850) and 0.870 (95% CI: 0.782, 0.957), respectively. Conclusions. Our developed radiomics-based model can successfully distinguish HH and HCC patients, which can help clinical decision-making with lower cost.
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Sherkhane, Umesh B., Ashish Kumar Jha, Sneha Mithun, Vinay Jaiswar, Alberto Traverso, Leonard Wee, Venkatesh Rangarajan, and Andre Dekker. "PyRadGUI: A GUI based radiomics extractor software." F1000Research 12 (March 10, 2023): 259. http://dx.doi.org/10.12688/f1000research.129826.1.
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Radiomics is the method of extracting high throughput mathematical and statistical features from medical images. These features have the potential to characterize the underlying pathology of the disease that is inappreciable to a trained human eye. There are several open-source and licensed tools to extract radiomic features such as pyradiomics, LIFEx, TexRAD, and RaCat. Although pyradiomics is a widely used radiomics package by researchers, this software is not very user-friendly and can be run using a command line. We have developed and validated the GUI tool, PyRadGUI to make the radiomics software easy to operate. This software adheres to IBSI radiomic feature definition and implements the radiomic pipeline in batch processing to extract radiomic features from multiple patient’s data and stores it in a comma separated value (CSV). We validated PyRadGUI software with the existing pyradiomic pipeline.
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Lei, Chu-qian, Wei Wei, Zhen-yu Liu, Qian-Qian Xiong, Ci-Qiu Yang, Teng Zhu, Liu-Lu Zhang, Mei Yang, Jie Tian, and Kun Wang. "Radiomics analysis for pathological classification prediction in BI-RADS category 4 mammographic calcifications." Journal of Clinical Oncology 37, no. 15_suppl (May 20, 2019): e13055-e13055. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.e13055.
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e13055 Background: To establish and validate a radiomics-based imaging diagnostic model to predict Breast Imaging Reporting and Data System (BI-RADS) category 4 calcification of breast with mammographic images before biopsy and assess its value. Methods: A total of 212 BI-RADS category 4 pathology-proven mammographic calcifications without obvious mass on mammography were retrospectively enrolled (159 in primary cohort and 53 in validation cohort). All patients received ultrasound inspection and the results were available. 8286 radiomic features were extracted from each mammography images. We utilized machine learning to build a radiomic signature based on optimal features. Independent clinical factors were selected by multivariable logistic regression analysis, and we incorporated the radiomic signatures and risk clinical factors to build a radiomic nomogram. The performance of the radiomic nomogram were assessed by the area under the receiver-operating characteristic curve (AUC). Results: Six features were selected to develop the radiomic signatures based on the primary cohort. Combining with menopausal states, the individualized radiomic nomogram reached an AUC of 0.803 in the validation cohorts, and its clinical utility was confirmed by the decision curve analysis. The difference was significant between the AUC value of differentiating results of the radiomic nomogram compared with ultrasound, mammography and combined modality respectively(p < 0.05 in all three groups). Especially, for patients with MG+/US- calcifications, radiomics nomogram can be screen out benign calcifications. Conclusions: Based on mammographic radiomics, we developed a method for prediction of pathological classification in BI-RADS IV calcification, which has a certain predictive effect.
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Costa, Guido, Lara Cavinato, Chiara Masci, Francesco Fiz, Martina Sollini, Letterio Salvatore Politi, Arturo Chiti, et al. "Virtual Biopsy for Diagnosis of Chemotherapy-Associated Liver Injuries and Steatohepatitis: A Combined Radiomic and Clinical Model in Patients with Colorectal Liver Metastases." Cancers 13, no. 12 (June 20, 2021): 3077. http://dx.doi.org/10.3390/cancers13123077.
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Non-invasive diagnosis of chemotherapy-associated liver injuries (CALI) is still an unmet need. The present study aims to elucidate the contribution of radiomics to the diagnosis of sinusoidal dilatation (SinDil), nodular regenerative hyperplasia (NRH), and non-alcoholic steatohepatitis (NASH). Patients undergoing hepatectomy for colorectal metastases after chemotherapy (January 2018-February 2020) were retrospectively analyzed. Radiomic features were extracted from a standardized volume of non-tumoral liver parenchyma outlined in the portal phase of preoperative post-chemotherapy computed tomography. Seventy-eight patients were analyzed: 25 had grade 2–3 SinDil, 27 NRH, and 14 NASH. Three radiomic fingerprints independently predicted SinDil: GLRLM_f3 (OR = 12.25), NGLDM_f1 (OR = 7.77), and GLZLM_f2 (OR = 0.53). Combining clinical, laboratory, and radiomic data, the predictive model had accuracy = 82%, sensitivity = 64%, and specificity = 91% (AUC = 0.87 vs. AUC = 0.77 of the model without radiomics). Three radiomic parameters predicted NRH: conventional_HUQ2 (OR = 0.76), GLZLM_f2 (OR = 0.05), and GLZLM_f3 (OR = 7.97). The combined clinical/laboratory/radiomic model had accuracy = 85%, sensitivity = 81%, and specificity = 86% (AUC = 0.91 vs. AUC = 0.85 without radiomics). NASH was predicted by conventional_HUQ2 (OR = 0.79) with accuracy = 91%, sensitivity = 86%, and specificity = 92% (AUC = 0.93 vs. AUC = 0.83 without radiomics). In the validation set, accuracy was 72%, 71%, and 91% for SinDil, NRH, and NASH. Radiomic analysis of liver parenchyma may provide a signature that, in combination with clinical and laboratory data, improves the diagnosis of CALI.
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Yin, Yunchao, Derya Yakar, Rudi A. J. O. Dierckx, Kim B. Mouridsen, Thomas C. Kwee, and Robbert J. de Haas. "Combining Hepatic and Splenic CT Radiomic Features Improves Radiomic Analysis Performance for Liver Fibrosis Staging." Diagnostics 12, no. 2 (February 21, 2022): 550. http://dx.doi.org/10.3390/diagnostics12020550.
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Background: The exact focus of computed tomography (CT)-based artificial intelligence techniques when staging liver fibrosis is still not exactly known. This study aimed to determine both the added value of splenic information to hepatic information, and the correlation between important radiomic features and information exploited by deep learning models for liver fibrosis staging by CT-based radiomics. Methods: The study design is retrospective. Radiomic features were extracted from both liver and spleen on portal venous phase CT images of 252 consecutive patients with histologically proven liver fibrosis stages between 2006 and 2018. The radiomics analyses for liver fibrosis staging were done by hepatic and hepatic–splenic features, respectively. The most predictive radiomic features were automatically selected by machine learning models. Results: When using splenic–hepatic features in the CT-based radiomics analysis, the average accuracy rates for significant fibrosis, advanced fibrosis, and cirrhosis were 88%, 82%, and 86%, and area under the receiver operating characteristic curves (AUCs) were 0.92, 0.81, and 0.85. The AUC of hepatic–splenic-based radiomics analysis with the ensemble classifier was 7% larger than that of hepatic-based analysis (p < 0.05). The most important features selected by machine learning models included both hepatic and splenic features, and they were consistent with the location maps indicating the focus of deep learning when predicting liver fibrosis stage. Conclusions: Adding CT-based splenic radiomic features to hepatic radiomic features increases radiomics analysis performance for liver fibrosis staging. The most important features of the radiomics analysis were consistent with the information exploited by deep learning.
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Gill, Andrew B., Leonardo Rundo, Jonathan C. M. Wan, Doreen Lau, Jeries P. Zawaideh, Ramona Woitek, Fulvio Zaccagna, et al. "Correlating Radiomic Features of Heterogeneity on CT with Circulating Tumor DNA in Metastatic Melanoma." Cancers 12, no. 12 (November 24, 2020): 3493. http://dx.doi.org/10.3390/cancers12123493.
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Clinical imaging methods, such as computed tomography (CT), are used for routine tumor response monitoring. Imaging can also reveal intratumoral, intermetastatic, and interpatient heterogeneity, which can be quantified using radiomics. Circulating tumor DNA (ctDNA) in the plasma is a sensitive and specific biomarker for response monitoring. Here we evaluated the interrelationship between circulating tumor DNA mutant allele fraction (ctDNAmaf), obtained by targeted amplicon sequencing and shallow whole genome sequencing, and radiomic measurements of CT heterogeneity in patients with stage IV melanoma. ctDNAmaf and radiomic observations were obtained from 15 patients with a total of 70 CT examinations acquired as part of a prospective trial. 26 of 39 radiomic features showed a significant relationship with log(ctDNAmaf). Principal component analysis was used to define a radiomics signature that predicted ctDNAmaf independent of lesion volume. This radiomics signature and serum lactate dehydrogenase were independent predictors of ctDNAmaf. Together, these results suggest that radiomic features and ctDNAmaf may serve as complementary clinical tools for treatment monitoring.
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Baine, Michael, Justin Burr, Qian Du, Chi Zhang, Xiaoying Liang, Luke Krajewski, Laura Zima, Gerard Rux, Chi Zhang, and Dandan Zheng. "The Potential Use of Radiomics with Pre-Radiation Therapy MR Imaging in Predicting Risk of Pseudoprogression in Glioblastoma Patients." Journal of Imaging 7, no. 2 (January 28, 2021): 17. http://dx.doi.org/10.3390/jimaging7020017.
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Glioblastoma (GBM) is the most common adult glioma. Differentiating post-treatment effects such as pseudoprogression from true progression is paramount for treatment. Radiomics has been shown to predict overall survival and MGMT (methylguanine-DNA methyltransferase) promoter status in those with GBM. A potential application of radiomics is predicting pseudoprogression on pre-radiotherapy (RT) scans for patients with GBM. A retrospective review was performed with radiomic data analyzed using pre-RT MRI scans. Pseudoprogression was defined as post-treatment findings on imaging that resolved with steroids or spontaneously on subsequent imaging. Of the 72 patients identified for the study, 35 were able to be assessed for pseudoprogression, and 8 (22.9%) had pseudoprogression. A total of 841 radiomic features were examined along with clinical features. Receiver operating characteristic (ROC) analyses were performed to determine the AUC (area under ROC curve) of models of clinical features, radiomic features, and combining clinical and radiomic features. Two radiomic features were identified to be the optimal model combination. The ROC analysis found that the predictive ability of this combination was higher than using clinical features alone (mean AUC: 0.82 vs. 0.62). Additionally, combining the radiomic features with clinical factors did not improve predictive ability. Our results indicate that radiomics is potentially capable of predicting future development of pseudoprogression in patients with GBM using pre-RT MRIs.
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Mes, Steven W., Floris H. P. van Velden, Boris Peltenburg, Carel F. W. Peeters, Dennis E. te Beest, Mark A. van de Wiel, Joost Mekke, et al. "Outcome prediction of head and neck squamous cell carcinoma by MRI radiomic signatures." European Radiology 30, no. 11 (June 4, 2020): 6311–21. http://dx.doi.org/10.1007/s00330-020-06962-y.
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Abstract Objectives Head and neck squamous cell carcinoma (HNSCC) shows a remarkable heterogeneity between tumors, which may be captured by a variety of quantitative features extracted from diagnostic images, termed radiomics. The aim of this study was to develop and validate MRI-based radiomic prognostic models in oral and oropharyngeal cancer. Materials and Methods Native T1-weighted images of four independent, retrospective (2005–2013), patient cohorts (n = 102, n = 76, n = 89, and n = 56) were used to delineate primary tumors, and to extract 545 quantitative features from. Subsequently, redundancy filtering and factor analysis were performed to handle collinearity in the data. Next, radiomic prognostic models were trained and validated to predict overall survival (OS) and relapse-free survival (RFS). Radiomic features were compared to and combined with prognostic models based on standard clinical parameters. Performance was assessed by integrated area under the curve (iAUC). Results In oral cancer, the radiomic model showed an iAUC of 0.69 (OS) and 0.70 (RFS) in the validation cohort, whereas the iAUC in the oropharyngeal cancer validation cohort was 0.71 (OS) and 0.74 (RFS). By integration of radiomic and clinical variables, the most accurate models were defined (iAUC oral cavity, 0.72 (OS) and 0.74 (RFS); iAUC oropharynx, 0.81 (OS) and 0.78 (RFS)), and these combined models outperformed prognostic models based on standard clinical variables only (p < 0.001). Conclusions MRI radiomics is feasible in HNSCC despite the known variability in MRI vendors and acquisition protocols, and radiomic features added information to prognostic models based on clinical parameters. Key Points • MRI radiomics can predict overall survival and relapse-free survival in oral and HPV-negative oropharyngeal cancer. • MRI radiomics provides additional prognostic information to known clinical variables, with the best performance of the combined models. • Variation in MRI vendors and acquisition protocols did not influence performance of radiomic prognostic models.
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Cinarer, Gokalp, and Bulent Gursel Emiroglu. "Statistical analysis of radiomic features in differentiation of glioma grades." New Trends and Issues Proceedings on Advances in Pure and Applied Sciences, no. 12 (April 30, 2020): 68–79. http://dx.doi.org/10.18844/gjpaas.v0i12.4988.
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Radiomics is an important quantitative feature extraction tool used in many areas such as image processing and computer-aided diagnosis. In this study, the discriminability of brain cancer tumour grades (Grade II and Grade III) with radiomic features were analysed statistically. The data set consists of 121 patients, 77 patients with Grade II tumours and 44 patients with Grade III tumours. A total of 107 radiomic features were extracted, including three groups of radiomic features such as morphological, first-order and texture. Relationships between the characteristics of each group were tested by Spearman’s correlation analysis. Differences between Grade II and Grade III tumour categories were analysed with Mann–Whitney U test. According to the results, it was seen that radiomic features can be used to differentiate the features of tumour levels evaluated in the same category. These results show that by employing radiomic features brain cancer grade detection can help machine learning technologies and radiological analysis.
Keywords: Radiomics, glioma, image processing.
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Li, Wenfei, Huanlei Zhang, Lei Ren, Ying Zou, Fengyue Tian, Xiaodong Ji, Qing Li, Wei Wang, Guolin Ma, and Shuang Xia. "Radiomics of dual-energy computed tomography for predicting progression-free survival in patients with early glottic cancer." Future Oncology 18, no. 15 (May 2022): 1873–84. http://dx.doi.org/10.2217/fon-2021-1125.
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Aim: This study aimed to predict progression-free survival (PFS) in patients with early glottic cancer using radiomic features on dual-energy computed tomography iodine maps. Methods: Radiomic features were extracted from arterial and venous phase iodine maps, and radiomic risk scores were determined by univariate Cox proportional hazards regression analysis and least absolute shrinkage and selection operator regression with tenfold cross-validation. The Kaplan–Meier method was used to evaluate the association between radiomic risk scores and PFS. Results: Patients were stratified into low-risk and high-risk groups using radiomics, the PFS corresponding rates with statistical significance between the two groups. The high-risk group showed better survival, benefiting from laryngectomy. Conclusion: Radiomics could provide a promising biomarker for predicting the PFS of early glottic cancer patients.
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Chen, Li, Yi Ouyang, Shuang Liu, Jie Lin, Changhuan Chen, Caixia Zheng, Jianbo Lin, Zhijian Hu, and Moliang Qiu. "Radiomics Analysis of Lymph Nodes with Esophageal Squamous Cell Carcinoma Based on Deep Learning." Journal of Oncology 2022 (September 13, 2022): 1–11. http://dx.doi.org/10.1155/2022/8534262.
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Purpose. To assess the role of multiple radiomic features of lymph nodes in the preoperative prediction of lymph node metastasis (LNM) in patients with esophageal squamous cell carcinoma (ESCC). Methods. Three hundred eight patients with pathologically confirmed ESCC were retrospectively enrolled (training cohort, n = 216; test cohort, n = 92). We extracted 207 handcrafted radiomic features and 1000 deep radiomic features of lymph nodes from their computed tomography (CT) images. The t-test and least absolute shrinkage and selection operator (LASSO) were used to reduce the dimensions and select key features. Handcrafted radiomics, deep radiomics, and clinical features were combined to construct models. Models I (handcrafted radiomic features), II (Model I plus deep radiomic features), and III (Model II plus clinical features) were built using three machine learning methods: support vector machine (SVM), adaptive boosting (AdaBoost), and random forest (RF). The best model was compared with the results of two radiologists, and its performance was evaluated in terms of sensitivity, specificity, accuracy, area under the curve (AUC), and receiver operating characteristic (ROC) curve analysis. Results. No significant differences were observed between cohorts. Ten handcrafted and 12 deep radiomic features were selected from the extracted features ( p < 0.05 ). Model III could discriminate between patients with and without LNM better than the diagnostic results of the two radiologists. Conclusion. The combination of handcrafted radiomic features, deep radiomic features, and clinical features could be used clinically to assess lymph node status in patients with ESCC.
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Li, Yingping, Samy Ammari, Corinne Balleyguier, Nathalie Lassau, and Emilie Chouzenoux. "Impact of Preprocessing and Harmonization Methods on the Removal of Scanner Effects in Brain MRI Radiomic Features." Cancers 13, no. 12 (June 15, 2021): 3000. http://dx.doi.org/10.3390/cancers13123000.
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In brain MRI radiomics studies, the non-biological variations introduced by different image acquisition settings, namely scanner effects, affect the reliability and reproducibility of the radiomics results. This paper assesses how the preprocessing methods (including N4 bias field correction and image resampling) and the harmonization methods (either the six intensity normalization methods working on brain MRI images or the ComBat method working on radiomic features) help to remove the scanner effects and improve the radiomic feature reproducibility in brain MRI radiomics. The analyses were based on in vitro datasets (homogeneous and heterogeneous phantom data) and in vivo datasets (brain MRI images collected from healthy volunteers and clinical patients with brain tumors). The results show that the ComBat method is essential and vital to remove scanner effects in brain MRI radiomic studies. Moreover, the intensity normalization methods, while not able to remove scanner effects at the radiomic feature level, still yield more comparable MRI images and improve the robustness of the harmonized features to the choice among ComBat implementations.
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Anile, Giuseppe, Andrea Bettinelli, Marta Paiusco, Giorgio De Conti, Chiara Gottardi, Pierfranco Conte, and Maria Grazia Ghi. "Radiomic features in recurrent/metastatic platinum refractory head and neck squamous cell carcinoma treated with immunotherapy." Journal of Clinical Oncology 39, no. 15_suppl (May 20, 2021): e18000-e18000. http://dx.doi.org/10.1200/jco.2021.39.15_suppl.e18000.
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e18000 Background: Radiomics is the computerized extraction of quantitative features from medical images, beyond the level of detail accessible to an unaided human eye. Several studies on radiomic analysis have been carried out to identify predictive, prognostic and diagnostic biomarkers for diverse tumor types including HNSCC. Radiomic features proved to be effective in predicting outcomes in patients with locally advanced HNSCC. The aim of the present study was to dentify predictive and prognostic radiomic features in platinum-refractory HNSCC patients with recurrent and/or metastatic (R/M) disease treated with anti PD-1 monotherapy. Methods: We retrospectively reviewed the data of 38 patients treated with Nivolumab at our Institution between January 2018 and March 2020. Nasopharyngeal carcinomas were not eligible. CT radiomic textural features were extracted from regions of interests (ROIs) manually delineated around tumor volumes. Minimum Redundancy Maximum Relevance (mRMR) algorithm was the method of choice to rank radiomic features based on three outcomes: overall response rate (ORR); disease progression (PD) and overall survival (OS). Logistic regression was employed to build predictive/prognostic models. Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) was chosen as the metric to assess model performances. Results: Data from 29 out of 38 patients were ultimately analyzed. Nine patients were excluded due to nasopharynx as primary tumor site and/or inadequate CT scan imaging. A total of 57 ROIs were extracted and analyzed. We obtained 9 models: 3 radiomic models, 3 clinical models and 3 combined models (radiomic plus clinical) for each outcome of interest (ORR, PD, OS). In the radiomic models 2 features were identified as predictor for ORR (AUC 0.69), 1 radiomic feature was predictive for PD (AUC 0.83) and 1 predictive for OS (AUC 0.72). In the clinical models, 3 clinical characteristics were identified for both ORR (AUC 0.91) and PD (AUC 0.73) and 2 clinical features were found to be predictive for OS (AUC 0.91). The combined model identified 3 features (2 radiomics and 1 clinical) predictive for ORR (AUC 0.76). No clinical characteristics were found in the combined model for PD. No radiomics features have been shown to be related to OS. Conclusions: This is an explorative study to test the power of radiomics and the potential value of combining radiomics and clinical data as a prognostic and predictive instrument. In this small sample size, the quantitative analysis of CT images seems to be an interesting tool which has to be further explored as predictor of outcome in a larger series of patients.[Table: see text]
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Gangil, Tarun, Krishna Sharan, B. Dinesh Rao, Krishnamoorthy Palanisamy, Biswaroop Chakrabarti, and Rajagopal Kadavigere. "Utility of adding Radiomics to clinical features in predicting the outcomes of radiotherapy for head and neck cancer using machine learning." PLOS ONE 17, no. 12 (December 15, 2022): e0277168. http://dx.doi.org/10.1371/journal.pone.0277168.
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Background Radiomics involves the extraction of quantitative information from annotated Computed-Tomography (CT) images, and has been used to predict outcomes in Head and Neck Squamous Cell Carcinoma (HNSCC). Subjecting combined Radiomics and Clinical features to Machine Learning (ML) could offer better predictions of clinical outcomes. This study is a comparative performance analysis of ML models with Clinical, Radiomics, and Clinico-Radiomic datasets for predicting four outcomes of HNSCC treated with Curative Radiation Therapy (RT): Distant Metastases, Locoregional Recurrence, New Primary, and Residual Disease. Methodology The study used retrospective data of 311 HNSCC patients treated with radiotherapy between 2013–2018 at our centre. Binary prediction models were developed for the four outcomes with Clinical-only, Clinico-Radiomic, and Radiomics-only datasets, using three different ML classification algorithms namely, Random Forest (RF), Kernel Support Vector Machine (KSVM), and XGBoost. The best-performing ML algorithms of the three dataset groups was then compared. Results The Clinico-Radiomic dataset using KSVM classifier provided the best prediction. Predicted mean testing accuracy for Distant Metastases, Locoregional Recurrence, New Primary, and Residual Disease was 97%, 72%, 99%, and 96%, respectively. The mean area under the receiver operating curve (AUC) was calculated and displayed for all the models using three dataset groups. Conclusion Clinico-Radiomic dataset improved the predictive ability of ML models over clinical features alone, while models built using Radiomics performed poorly. Radiomics data could therefore effectively supplement clinical data in predicting outcomes.
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Bogani, Giorgio, Valentina Chiappa, Salvatore Lopez, Christian Salvatore, Matteo Interlenghi, Ottavia D’Oria, Andrea Giannini, et al. "Radiomics and Molecular Classification in Endometrial Cancer (The ROME Study): A Step Forward to a Simplified Precision Medicine." Healthcare 10, no. 12 (December 7, 2022): 2464. http://dx.doi.org/10.3390/healthcare10122464.
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Molecular/genomic profiling is the most accurate method to assess prognosis of endometrial cancer patients. Radiomic profiling allows for the extraction of mineable high-dimensional data from clinical radiological images, thus providing noteworthy information regarding tumor tissues. Interestingly, the adoption of radiomics shows important results for screening, diagnosis and prognosis, across various radiological systems and oncologic specialties. The central hypothesis of the prospective trial is that combining radiomic features with molecular features might allow for the identification of various classes of risks for endometrial cancer, e.g., predicting unfavorable molecular/genomic profiling. The rationale for the proposed research is that once validated, radiomics applied to ultrasonographic images would be an effective, innovative and inexpensive method for tailoring operative and postoperative treatment modalities in endometrial cancer. Patients with newly diagnosed endometrial cancer will have ultrasonographic evaluation and radiomic analysis of the ultrasonographic images. We will correlate radiomic features with molecular/genomic profiling to classify prognosis.
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Bologna, Marco, Valentina Corino, Giuseppina Calareso, Chiara Tenconi, Salvatore Alfieri, Nicola Alessandro Iacovelli, Anna Cavallo, et al. "Baseline MRI-Radiomics Can Predict Overall Survival in Non-Endemic EBV-Related Nasopharyngeal Carcinoma Patients." Cancers 12, no. 10 (October 13, 2020): 2958. http://dx.doi.org/10.3390/cancers12102958.
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Advanced stage nasopharyngeal cancer (NPC) shows highly variable treatment outcomes, suggesting the need for independent prognostic factors. This study aims at developing a magnetic resonance imaging (MRI)-based radiomic signature as a prognostic marker for different clinical endpoints in NPC patients from non-endemic areas. A total 136 patients with advanced NPC and available MRI imaging (T1-weighted and T2-weighted) were selected. For each patient, 2144 radiomic features were extracted from the main tumor and largest lymph node. A multivariate Cox regression model was trained on a subset of features to obtain a radiomic signature for overall survival (OS), which was also applied for the prognosis of other clinical endpoints. Validation was performed using 10-fold cross-validation. The added prognostic value of the radiomic features to clinical features and volume was also evaluated. The radiomics-based signature had good prognostic power for OS and loco-regional recurrence-free survival (LRFS), with C-index of 0.68 and 0.72, respectively. In all the cases, the addition of radiomics to clinical features improved the prognostic performance. Radiomic features can provide independent prognostic information in NPC patients from non-endemic areas.
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Lisson, Catharina Silvia, Christoph Gerhard Lisson, Sherin Achilles, Marc Fabian Mezger, Daniel Wolf, Stefan Andreas Schmidt, Wolfgang M. Thaiss, et al. "Longitudinal CT Imaging to Explore the Predictive Power of 3D Radiomic Tumour Heterogeneity in Precise Imaging of Mantle Cell Lymphoma (MCL)." Cancers 14, no. 2 (January 13, 2022): 393. http://dx.doi.org/10.3390/cancers14020393.
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The study’s primary aim is to evaluate the predictive performance of CT-derived 3D radiomics for MCL risk stratification. The secondary objective is to search for radiomic features associated with sustained remission. Included were 70 patients: 31 MCL patients and 39 control subjects with normal axillary lymph nodes followed over five years. Radiomic analysis of all targets (n = 745) was performed and features selected using the Mann Whitney U test; the discriminative power of identifying “high-risk MCL” was evaluated by receiver operating characteristics (ROC). The four radiomic features, “Uniformity”, “Entropy”, “Skewness” and “Difference Entropy” showed predictive significance for relapse (p < 0.05)—in contrast to the routine size measurements, which showed no relevant difference. The best prognostication for relapse achieved the feature “Uniformity” (AUC-ROC-curve 0.87; optimal cut-off ≤0.0159 to predict relapse with 87% sensitivity, 65% specificity, 69% accuracy). Several radiomic features, including the parameter “Short Axis,” were associated with sustained remission. CT-derived 3D radiomics improves the predictive estimation of MCL patients; in combination with the ability to identify potential radiomic features that are characteristic for sustained remission, it may assist physicians in the clinical management of MCL.
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Li, Chen, Jingyong Xu, Yuan Liu, Mingxiao Wu, Weide Dai, Jinghai Song, and Hanzhang Wang. "Kupffer Phase Radiomics Signature in Sonazoid-Enhanced Ultrasound is an Independent and Effective Predictor of the Pathologic Grade of Hepatocellular Carcinoma." Journal of Oncology 2022 (June 27, 2022): 1–7. http://dx.doi.org/10.1155/2022/6123242.
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We conduct this study to investigate the value of Kupffer phase radiomics signature of Sonazoid-enhanced ultrasound images (SEUS) for the preoperative prediction of hepatocellular carcinoma (HCC) grade. From November 2019 to October 2021, 68 pathologically confirmed HCC nodules from 54 patients were included. Quantitative radiomic features were extracted from grayscale images and arterial and Kupffer phases of SEUS of HCC lesions. Univariate logistic regression and the maximum relevance minimum redundancy (MRMR) method were applied to select radiomic features best corresponding to pathological results. Prediction radiomic signature was calculated using each of the image types. A predictive model was validated using internal leave-one-out cross validation (LOOCV). For discrimination between poorly differentiated HCC (p-HCC) and well-differentiated HCC/moderately differentiated HCC (w/m-HCC), the Kupffer phase radiomic score (KPRS) achieved an excellent area under the curve (AUC = 0.937), significantly higher than the other two radiomic signatures. KPRS was the best radiomic score based on the highest AUC (AUC = 0.878), which is prior to gray and arterial RS for differentiation between w-HCC and m/p-HCC. Univariate and multivariate analysis incorporating all radiomic signatures and serological variables showed that KPRS was the only independent predictor in both predictions of HCC lesions (p-HCC vs. w/m-HCC, log OR 15.869, P < 0.001 , m/p-HCC vs. w-HCC, log OR 12.520, P < 0.05 ). We conclude that radiomics signature based on the Kupffer phase imaging may be useful for identifying the histological grade of HCC. The Kupffer phase radiomic signature may be an independent and effective predictor in discriminating w-HCC and p-HCC.
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Fournier, Laure, Lena Costaridou, Luc Bidaut, Nicolas Michoux, Frederic E. Lecouvet, Lioe-Fee de Geus-Oei, Ronald Boellaard, et al. "Incorporating radiomics into clinical trials: expert consensus endorsed by the European Society of Radiology on considerations for data-driven compared to biologically driven quantitative biomarkers." European Radiology 31, no. 8 (January 25, 2021): 6001–12. http://dx.doi.org/10.1007/s00330-020-07598-8.
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Abstract Existing quantitative imaging biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials. Key Points • Data-driven processes like radiomics risk false discoveries due to high-dimensionality of the dataset compared to sample size, making adequate diversity of the data, cross-validation and external validation essential to mitigate the risks of spurious associations and overfitting. • Use of radiomic signatures within clinical trials requires multistep standardisation of image acquisition, image analysis and data mining processes. • Biological correlation may be established after clinical validation but is not mandatory.
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Fan, Yanghua, Renzhi Wang, and Ming Feng. "NIMG-22. DEVELOPMENT AND VALIDATION OF A RADIOMIC MODEL FOR RWDD3 EXPRESSION PREDICTION IN PATIENTS WITH ACROMEGALY." Neuro-Oncology 21, Supplement_6 (November 2019): vi166. http://dx.doi.org/10.1093/neuonc/noz175.694.
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Abstract BACKGROUND The expression of RWDD3 is closely related to the prognosis of acromegaly. Therefore, this study aimed to investigate a radiomics method based on MRI to noninvasively evaluate RWDD3 expression in acromegaly. MATERIAL AND METHODS 132 patients with acromegaly were enrolled and divided into primary (n=88) and validation cohorts (n=44) according. The expression of RWDD3 was determined by immunohistochemistry. Radiomic features were extracted from the MR images and determined using the ‘Elastic Net’ feature selection algorithm. A radiomic signature was built using a support vector machine. Subsequently, multivariable logistic regression analysis was used to select the most informative clinical features, and a radiomic model, incorporating the radiomic signature and selected clinical features, was constructed and used as the final predictive model. The performance of this radiomic model was validated using receiver operating characteristics analysis, and its calibration, discriminating ability, and clinical usefulness were assessed. RESULTS The radiomic signature, which was constructed with radiomic features selected using the primary cohort, showed a favorable discriminatory ability in the validation cohort. The radiomic model incorporating the radiomic signature and three selected clinical features showed good discrimination abilities and calibration, with an area under the curve (AUC) of 0.89 for the primary cohort and 0.84 for the validation cohort. The radiomic model better estimated the treatment responses of patients with acromegaly than did the clinical features. Decision curve analysis showed the radiomic model was clinically useful. CONCLUSION This radiomic model could aid neurosurgeons in the prediction of RWDD3 expression in patients with acromegaly, and could contribute to predicting of patient prognoses.
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Avesani, Giacomo, Huong Elena Tran, Giulio Cammarata, Francesca Botta, Sara Raimondi, Luca Russo, Salvatore Persiani, et al. "CT-Based Radiomics and Deep Learning for BRCA Mutation and Progression-Free Survival Prediction in Ovarian Cancer Using a Multicentric Dataset." Cancers 14, no. 11 (May 31, 2022): 2739. http://dx.doi.org/10.3390/cancers14112739.
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Purpose: Build predictive radiomic models for early relapse and BRCA mutation based on a multicentric database of high-grade serous ovarian cancer (HGSOC) and validate them in a test set coming from different institutions. Methods: Preoperative CTs of patients with HGSOC treated at four referral centers were retrospectively acquired and manually segmented. Hand-crafted features and deep radiomics features were extracted respectively by dedicated software (MODDICOM) and a dedicated convolutional neural network (CNN). Features were selected with and without prior harmonization (ComBat harmonization), and models were built using different machine learning algorithms, including clinical variables. Results: We included 218 patients. Radiomic models showed low performance in predicting both BRCA mutation (AUC in test set between 0.46 and 0.59) and 1-year relapse (AUC in test set between 0.46 and 0.56); deep learning models demonstrated similar results (AUC in the test of 0.48 for BRCA and 0.50 for relapse). The inclusion of clinical variables improved the performance of the radiomic models to predict BRCA mutation (AUC in the test set of 0.74). Conclusions: In our multicentric dataset, representative of a real-life clinical scenario, we could not find a good radiomic predicting model for PFS and BRCA mutational status, with both traditional radiomics and deep learning, but the combination of clinical and radiomic models improved model performance for the prediction of BRCA mutation. These findings highlight the need for standardization through the whole radiomic pipelines and robust multicentric external validations of results.
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Chaddad, Ahmad, Jiali Li, Qizong Lu, Yujie Li, Idowu Paul Okuwobi, Camel Tanougast, Christian Desrosiers, and Tamim Niazi. "Can Autism Be Diagnosed with Artificial Intelligence? A Narrative Review." Diagnostics 11, no. 11 (November 3, 2021): 2032. http://dx.doi.org/10.3390/diagnostics11112032.
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Radiomics with deep learning models have become popular in computer-aided diagnosis and have outperformed human experts on many clinical tasks. Specifically, radiomic models based on artificial intelligence (AI) are using medical data (i.e., images, molecular data, clinical variables, etc.) for predicting clinical tasks such as autism spectrum disorder (ASD). In this review, we summarized and discussed the radiomic techniques used for ASD analysis. Currently, the limited radiomic work of ASD is related to the variation of morphological features of brain thickness that is different from texture analysis. These techniques are based on imaging shape features that can be used with predictive models for predicting ASD. This review explores the progress of ASD-based radiomics with a brief description of ASD and the current non-invasive technique used to classify between ASD and healthy control (HC) subjects. With AI, new radiomic models using the deep learning techniques will be also described. To consider the texture analysis with deep CNNs, more investigations are suggested to be integrated with additional validation steps on various MRI sites.
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Dulhanty, Chris, Linda Wang, Maria Cheng, Hayden Gunraj, Farzad Khalvati, Masoom A. Haider, and Alexander Wong. "Radiomics Driven Diffusion Weighted Imaging Sensing Strategies for Zone-Level Prostate Cancer Sensing." Sensors 20, no. 5 (March 10, 2020): 1539. http://dx.doi.org/10.3390/s20051539.
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Prostate cancer is the most commonly diagnosed cancer in North American men; however, prognosis is relatively good given early diagnosis. This motivates the need for fast and reliable prostate cancer sensing. Diffusion weighted imaging (DWI) has gained traction in recent years as a fast non-invasive approach to cancer sensing. The most commonly used DWI sensing modality currently is apparent diffusion coefficient (ADC) imaging, with the recently introduced computed high-b value diffusion weighted imaging (CHB-DWI) showing considerable promise for cancer sensing. In this study, we investigate the efficacy of ADC and CHB-DWI sensing modalities when applied to zone-level prostate cancer sensing by introducing several radiomics driven zone-level prostate cancer sensing strategies geared around hand-engineered radiomic sequences from DWI sensing (which we term as Zone-X sensing strategies). Furthermore, we also propose Zone-DR, a discovery radiomics approach based on zone-level deep radiomic sequencer discovery that discover radiomic sequences directly for radiomics driven sensing. Experimental results using 12,466 pathology-verified zones obtained through the different DWI sensing modalities of 101 patients showed that: (i) the introduced Zone-X and Zone-DR radiomics driven sensing strategies significantly outperformed the traditional clinical heuristics driven strategy in terms of AUC, (ii) the introduced Zone-DR and Zone-SVM strategies achieved the highest sensitivity and specificity, respectively for ADC amongst the tested radiomics driven strategies, (iii) the introduced Zone-DR and Zone-LR strategies achieved the highest sensitivities for CHB-DWI amongst the tested radiomics driven strategies, and (iv) the introduced Zone-DR, Zone-LR, and Zone-SVM strategies achieved the highest specificities for CHB-DWI amongst the tested radiomics driven strategies. Furthermore, the results showed that the trade-off between sensitivity and specificity can be optimized based on the particular clinical scenario we wish to employ radiomic driven DWI prostate cancer sensing strategies for, such as clinical screening versus surgical planning. Finally, we investigate the critical regions within sensing data that led to a given radiomic sequence generated by a Zone-DR sequencer using an explainability method to get a deeper understanding on the biomarkers important for zone-level cancer sensing.
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Xie, Chen-Yi, Yi-Huai Hu, Joshua Wing-Kei Ho, Lu-Jun Han, Hong Yang, Jing Wen, Ka-On Lam, et al. "Using Genomics Feature Selection Method in Radiomics Pipeline Improves Prognostication Performance in Locally Advanced Esophageal Squamous Cell Carcinoma—A Pilot Study." Cancers 13, no. 9 (April 29, 2021): 2145. http://dx.doi.org/10.3390/cancers13092145.
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Purpose: To evaluate the prognostic value of baseline and restaging CT-based radiomics with features associated with gene expression in esophageal squamous cell carcinoma (ESCC) patients receiving neoadjuvant chemoradiation (nCRT) plus surgery. Methods: We enrolled 106 ESCC patients receiving nCRT from two institutions. Gene expression profiles of 28 patients in the training set were used to detect differentially expressed (DE) genes between patients with and without relapse. Radiomic features that were correlated to DE genes were selected, followed by additional machine learning selection. A radiomic nomogram for disease-free survival (DFS) prediction incorporating the radiomic signature and prognostic clinical characteristics was established for DFS estimation and validated. Results: The radiomic signature with DE genes feature selection achieved better performance for DFS prediction than without. The nomogram incorporating the radiomic signature and lymph nodal status significantly stratified patients into high and low-risk groups for DFS (p < 0.001). The areas under the curve (AUCs) for predicting 5-year DFS were 0.912 in the training set, 0.852 in the internal test set, 0.769 in the external test set. Conclusions: Genomics association was useful for radiomic feature selection. The established radiomic signature was prognostic for DFS. The radiomic nomogram could provide a valuable prediction for individualized long-term survival.
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Parr, Elsa, Qian Du, Chi Zhang, Chi Lin, Ahsan Kamal, Josiah McAlister, Xiaoying Liang, et al. "Radiomics-Based Outcome Prediction for Pancreatic Cancer Following Stereotactic Body Radiotherapy." Cancers 12, no. 4 (April 24, 2020): 1051. http://dx.doi.org/10.3390/cancers12041051.
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(1) Background: Radiomics use high-throughput mining of medical imaging data to extract unique information and predict tumor behavior. Currently available clinical prediction models poorly predict treatment outcomes in pancreatic adenocarcinoma. Therefore, we used radiomic features of primary pancreatic tumors to develop outcome prediction models and compared them to traditional clinical models. (2) Methods: We extracted and analyzed radiomic data from pre-radiation contrast-enhanced CTs of 74 pancreatic cancer patients undergoing stereotactic body radiotherapy. A panel of over 800 radiomic features was screened to create overall survival and local-regional recurrence prediction models, which were compared to clinical prediction models and models combining radiomic and clinical information. (3) Results: A 6-feature radiomic signature was identified that achieved better overall survival prediction performance than the clinical model (mean concordance index: 0.66 vs. 0.54 on resampled cross-validation test sets), and the combined model improved the performance slightly further to 0.68. Similarly, a 7-feature radiomic signature better predicted recurrence than the clinical model (mean AUC of 0.78 vs. 0.66). (4) Conclusion: Overall survival and recurrence can be better predicted with models based on radiomic features than with those based on clinical features for pancreatic cancer.
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Bera, Kaustav, Vamsidhar Velcheti, and Anant Madabhushi. "Novel Quantitative Imaging for Predicting Response to Therapy: Techniques and Clinical Applications." American Society of Clinical Oncology Educational Book, no. 38 (May 2018): 1008–18. http://dx.doi.org/10.1200/edbk_199747.
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The current standard of Response Evaluation Criteria in Solid Tumors (RECIST)–based tumor response evaluation is limited in its ability to accurately monitor treatment response. Radiomics, an approach involving computerized extraction of several quantitative imaging features, has shown promise in predicting as well as monitoring response to therapy. In this article, we provide a brief overview of radiomic approaches and the various analytical methods and techniques, specifically in the context of predicting and monitoring treatment response for non–small cell lung cancer (NSCLC). We briefly summarize some of the various types of radiomic features, including tumor shape and textural patterns, both within the tumor and within the adjacent tumor microenvironment. Additionally, we also discuss work in delta-radiomics or change in radiomic features (e.g., texture within the nodule) across longitudinally interspersed images in time for monitoring changes in therapy. We discuss the utility of these approaches for NSCLC, specifically the role of radiomics as a prognostic marker for treatment effectiveness and early therapy response, including chemoradiation, immunotherapy, and trimodality therapy.
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Guglielmo, Priscilla, Francesca Marturano, Andrea Bettinelli, Michele Gregianin, Marta Paiusco, and Laura Evangelista. "Additional Value of PET Radiomic Features for the Initial Staging of Prostate Cancer: A Systematic Review from the Literature." Cancers 13, no. 23 (November 30, 2021): 6026. http://dx.doi.org/10.3390/cancers13236026.
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We performed a systematic review of the literature to provide an overview of the application of PET radiomics for the prediction of the initial staging of prostate cancer (PCa), and to discuss the additional value of radiomic features over clinical data. The most relevant databases and web sources were interrogated by using the query “prostate AND radiomic* AND PET”. English-language original articles published before July 2021 were considered. A total of 28 studies were screened for eligibility and 6 of them met the inclusion criteria and were, therefore, included for further analysis. All studies were based on human patients. The average number of patients included in the studies was 72 (range 52–101), and the average number of high-order features calculated per study was 167 (range 50–480). The radiotracers used were [68Ga]Ga-PSMA-11 (in four out of six studies), [18F]DCFPyL (one out of six studies), and [11C]Choline (one out of six studies). Considering the imaging modality, three out of six studies used a PET/CT scanner and the other half a PET/MRI tomograph. Heterogeneous results were reported regarding radiomic methods (e.g., segmentation modality) and considered features. The studies reported several predictive markers including first-, second-, and high-order features, such as “kurtosis”, “grey-level uniformity”, and “HLL wavelet mean”, respectively, as well as PET-based metabolic parameters. The strengths and weaknesses of PET radiomics in this setting of disease will be largely discussed and a critical analysis of the available data will be reported. In our review, radiomic analysis proved to add useful information for lesion detection and the prediction of tumor grading of prostatic lesions, even when they were missed at visual qualitative assessment due to their small size; furthermore, PET radiomics could play a synergistic role with the mpMRI radiomic features in lesion evaluation. The most common limitations of the studies were the small sample size, retrospective design, lack of validation on external datasets, and unavailability of univocal cut-off values for the selected radiomic features.
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Marzi, Chiara, Daniela Marfisi, Andrea Barucci, Jacopo Del Del Meglio, Alessio Lilli, Claudio Vignali, Mario Mascalchi, et al. "Collinearity and Dimensionality Reduction in Radiomics: Effect of Preprocessing Parameters in Hypertrophic Cardiomyopathy Magnetic Resonance T1 and T2 Mapping." Bioengineering 10, no. 1 (January 6, 2023): 80. http://dx.doi.org/10.3390/bioengineering10010080.
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Radiomics and artificial intelligence have the potential to become a valuable tool in clinical applications. Frequently, radiomic analyses through machine learning methods present issues caused by high dimensionality and multicollinearity, and redundant radiomic features are usually removed based on correlation analysis. We assessed the effect of preprocessing—in terms of voxel size resampling, discretization, and filtering—on correlation-based dimensionality reduction in radiomic features from cardiac T1 and T2 maps of patients with hypertrophic cardiomyopathy. For different combinations of preprocessing parameters, we performed a dimensionality reduction of radiomic features based on either Pearson’s or Spearman’s correlation coefficient, followed by the computation of the stability index. With varying resampling voxel size and discretization bin width, for both T1 and T2 maps, Pearson’s and Spearman’s dimensionality reduction produced a slightly different percentage of remaining radiomic features, with a relatively high stability index. For different filters, the remaining features’ stability was instead relatively low. Overall, the percentage of eliminated radiomic features through correlation-based dimensionality reduction was more dependent on resampling voxel size and discretization bin width for textural features than for shape or first-order features. Notably, correlation-based dimensionality reduction was less sensitive to preprocessing when considering radiomic features from T2 compared with T1 maps.
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Kazerooni, Anahita Fathi, Sherjeel Arif, Debanjan Haldar, Rachel Madhogarhia, Ariana Familiar, Sina Bagheri, Hannah Anderson, et al. "IMG-15. Radiomic Profiling of Pediatric Low-Grade Glioma Improves Risk Stratification Beyond Clinical Measures." Neuro-Oncology 24, Supplement_1 (June 1, 2022): i80. http://dx.doi.org/10.1093/neuonc/noac079.291.
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Abstract PURPOSE: Treatment response is heterogeneous among patients with pediatric low-grade glioma (pLGG), the most frequent childhood brain tumor. Upfront prediction of progression-free survival (PFS) may facilitate more personalized treatment planning and improve outcomes for the pLGG patients. In this work, we explored the additive value of radiomics to clinical measures for prediction of PFS in pLGGs. We further sought associations between the derived risk groups and underlying alterations in key genomic and transcriptomic variables. METHODS: Quantitative radiomic features were extracted from pre-operative multi-parametric MRI scans (T1, T1-post, T2, T2-FLAIR) of 96 patients with newly diagnosed pLGG (median age, 8.59, range, 0.35-18.87 years; median PFS, 25.23, range, 3.03-124.83 months). Multivariate Cox proportional hazard’s (Cox-PH) regression models were fitted using 5-fold cross-validation on a training cohort of 68 subjects and tested on 28 patients. Three models were generated using (1) only clinical variables (age, sex, and extent of tumor resection), (2) radiomic features, and (3) clinical and radiomic variables. The dimensionality of radiomic features in Cox-PH models was reduced by applying Elastic Net regularization penalty to identify a subset of variables that are most predictive of PFS. The patients were then stratified into three groups of high, medium, and low-risk based on model predictions. RESULTS: Cox-PH modeling resulted in a concordance index (c-index) of 0.55 for clinical data, 0.65 for radiomics, and 0.73 for a combination of clinical and radiomic variables, highlighting the additive value of radiomics to the readily available clinical information in prediction of PFS. Radiogenomic assessments revealed significant differences in expression of BRAF, NF1, TSC1, ALK (p<0.01), and RB1 (p<0.05) genes in the high-risk group, compared to the medium and low-risk groups. CONCLUSION: Our results demonstrate the value of integrating radiomics with clinical measures to improve risk assessment of patients with pLGG through improved pretreatment prediction of PFS.
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Abdurixiti, Meilinuer, Mayila Nijiati, Rongfang Shen, Qiu Ya, Naibijiang Abuduxiku, and Mayidili Nijiati. "Current progress and quality of radiomic studies for predicting EGFR mutation in patients with non-small cell lung cancer using PET/CT images: a systematic review." British Journal of Radiology 94, no. 1122 (June 1, 2021): 20201272. http://dx.doi.org/10.1259/bjr.20201272.
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Objectives: To assess the methodological quality of radiomic studies based on positron emission tomography/computed tomography (PET/CT) images predicting epidermal growth factor receptor (EGFR) mutation status in patients with non-small cell lung cancer (NSCLC). Methods: We systematically searched for eligible studies in the PubMed and Web of Science datasets using the terms “radiomics”, “PET/CT”, “NSCLC”, and “EGFR”. The included studies were screened by two reviewers independently. The quality of the radiomic workflow of studies was assessed using the Radiomics Quality Score (RQS). Interclass correlation coefficient (ICC) was used to determine inter rater agreement for the RQS. An overview of the methodologies used in steps of the radiomics workflow and current results are presented. Results: Six studies were included with sample sizes of 973 ranging from 115 to 248 patients. Methodologies in the radiomic workflow varied greatly. The first-order statistics were the most reproducible features. The RQS scores varied from 13.9 to 47.2%. All studies were scored below 50% due to defects on multiple segmentations, phantom study on all scanners, imaging at multiple time points, cut-off analyses, calibration statistics, prospective study, potential clinical utility, and cost-effectiveness analysis. The ICC results for majority of RQS items were excellent. The ICC for summed RQS was 0.986 [95% confidence interval (CI): 0.898–0.998]. Conclusions: The PET/CT-based radiomics signature could serve as a diagnostic indicator of EGFR mutation status in NSCLC patients. However, the current conclusions should be interpreted with care due to the suboptimal quality of the studies. Consensus for standardization of PET/CT-based radiomic workflow for EGFR mutation status in NSCLC patients is warranted to further improve research. Advances in knowledge: Radiomics can offer clinicians better insight into the prediction of EGFR mutation status in NSCLC patients, whereas the quality of relative studies should be improved before application to the clinical setting.
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Harrison, Rebecca, Bryce Wei Quan Tan, Hong Qi Tan, Lloyd Tan, Mei Chin Lim, Clement Yong, John Kuo, and Shelli Kesler. "NIMG-32. THE PREDICTIVE CAPACITY OF PRE-OPERATIVE IMAGING ANALYSIS IN DIFFUSE GLIOMA: A COMPARISON OF CONNECTOMICS, RADIOMICS, AND CLINICAL PREDICTIVE MODELS." Neuro-Oncology 22, Supplement_2 (November 2020): ii154—ii155. http://dx.doi.org/10.1093/neuonc/noaa215.645.
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Abstract BACKGROUND Radiomics and connectome analysis are distinct and non-invasive methods of deriving biologic information from MRI. Radiomics analyzes features intrinsic to the tumor, and connectomics incorporates data regarding the tumor and surrounding neural circuitry. In this study we used both techniques to predict glioma survival. METHODS We retrospectively identified 305 adult patients with histopathologically confirmed WHO grade II–IV gliomas who had presurgical, 3D, T1-weighted brain MRI. Available clinical variables included tumor lobe, hemisphere, multifocal nature grade, histology extent of surgical resection, patient age gender. For connectomics, we calculated nodal efficiencies, network size and degree for all pairs of 33 voxel cubes spanning the entire gray matter volume using similarity-based extraction and graph theory. Radiomic features were extracted using Pyradiomics and subjected to patient-level and population-level clustering (N=172). These clusters were then used to construct a multi-regional spatial interaction matrix for model building. Cox proportional hazards models were fit for clinical variables alone, connectomics alone, radiomics alone, connectomics+clinical and radiomics+clinical. We implemented 10-folds cross-validation and examined the mean area under the curve (AUC) across validation loops. RESULTS Median survival time was 134.2 months. The mean AUC for the clinical model was 0.79 +/- 0.01, the connectome model was 0.88 +/- 0.01, the combined connectome + clinical model was 0.93 +/- 0.01, the radiomic model was 0.64 +/- 0.05 and the radiomics+clinical model was 0.89+/-0.03. Radiomic analysis of the entire dataset as well as comparisons of radiomic+connectomics +/- clinical models are pending. CONCLUSIONS The combination of clinical variables and connectome analysis provided a more robust predictive model than other models. This suggests that connectome analysis incorporates valuable clinically-predictive information which can augment our capacity for prognostication of patients with diffuse glioma. These methods warrant further evaluation in larger prospective study of patients with diffuse glioma.
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Chilaca-Rosas, Maria-Fatima, Melissa Garcia-Lezama, Sergio Moreno-Jimenez, and Ernesto Roldan-Valadez. "Diagnostic Performance of Selected MRI-Derived Radiomics Able to Discriminate Progression-Free and Overall Survival in Patients with Midline Glioma and the H3F3AK27M Mutation." Diagnostics 13, no. 5 (February 23, 2023): 849. http://dx.doi.org/10.3390/diagnostics13050849.
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Background: Radiomics refers to a recent area of knowledge that studies features extracted from different imaging techniques and subsequently transformed into high-dimensional data that can be associated with biological events. Diffuse midline gliomas (DMG) are one of the most devastating types of cancer, with a median survival of approximately 11 months after diagnosis and 4–5 months after radiological and clinical progression. Methods: A retrospective study. From a database of 91 patients with DMG, only 12 had the H3.3K27M mutation and brain MRI DICOM files available. Radiomic features were extracted from MRI T1 and T2 sequences using LIFEx software. Statistical analysis included normal distribution tests and the Mann–Whitney U test, ROC analysis, and calculation of cut-off values. Results: A total of 5760 radiomic values were included in the analyses. AUROC demonstrated 13 radiomics with statistical significance for progression-free survival (PFS) and overall survival (OS). Diagnostic performance tests showed nine radiomics with specificity for PFS above 90% and one with a sensitivity of 97.2%. For OS, 3 out of 4 radiomics demonstrated between 80 and 90% sensitivity. Conclusions: Several radiomic features demonstrated statistical significance and have the potential to further aid DMG diagnostic assessment non-invasively. The most significant radiomics were first- and second-order features with GLCM texture profile, GLZLM_GLNU, and NGLDM_Contrast.
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Zhu, Yitan, Abdallah S. R. Mohamed, Stephen Y. Lai, Shengjie Yang, Aasheesh Kanwar, Lin Wei, Mona Kamal, et al. "Imaging-Genomic Study of Head and Neck Squamous Cell Carcinoma: Associations Between Radiomic Phenotypes and Genomic Mechanisms via Integration of The Cancer Genome Atlas and The Cancer Imaging Archive." JCO Clinical Cancer Informatics, no. 3 (December 2019): 1–9. http://dx.doi.org/10.1200/cci.18.00073.
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Purpose Recent data suggest that imaging radiomic features of a tumor could be indicative of important genomic biomarkers. Understanding the relationship between radiomic and genomic features is important for basic cancer research and future patient care. We performed a comprehensive study to discover the imaginggenomic associations in head and neck squamous cell carcinoma (HNSCC) and explore the potential of predicting tumor genomic alternations using radiomic features. Methods Our retrospective study integrated whole-genome multiomics data from The Cancer Genome Atlas with matched computed tomography imaging data from The Cancer Imaging Archive for the same set of 126 patients with HNSCC. Linear regression and gene set enrichment analysis were used to identify statistically significant associations between radiomic imaging and genomic features. Random forest classifier was used to predict the status of two key HNSCC molecular biomarkers, human papillomavirus and disruptive TP53 mutation, on the basis of radiomic features. Results Widespread and statistically significant associations were discovered between genomic features (including microRNA expression, somatic mutations, and transcriptional activity, copy number variations, and promoter region DNA methylation changes of pathways) and radiomic features characterizing the size, shape, and texture of tumor. Prediction of human papillomavirus and TP53 mutation status using radiomic features achieved areas under the receiver operating characteristic curve of 0.71 and 0.641, respectively. Conclusion Our exploratory study suggests that radiomic features are associated with genomic characteristics at multiple molecular layers in HNSCC and provides justification for continued development of radiomics as biomarkers for relevant genomic alterations in HNSCC.
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Losnegård, Are, Lars A. R. Reisæter, Ole J. Halvorsen, Jakub Jurek, Jörg Assmus, Jarle B. Arnes, Alfred Honoré, et al. "Magnetic resonance radiomics for prediction of extraprostatic extension in non-favorable intermediate- and high-risk prostate cancer patients." Acta Radiologica 61, no. 11 (February 28, 2020): 1570–79. http://dx.doi.org/10.1177/0284185120905066.
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Background To investigate whether magnetic resonance (MR) radiomic features combined with machine learning may aid in predicting extraprostatic extension (EPE) in high- and non-favorable intermediate-risk patients with prostate cancer. Purpose To investigate the diagnostic performance of radiomics to detect EPE. Material and Methods MR radiomic features were extracted from 228 patients, of whom 86 were diagnosed with EPE, using prostate and lesion segmentations. Prediction models were built using Random Forest. Further, EPE was also predicted using a clinical nomogram and routine radiological interpretation and diagnostic performance was assessed for individual and combined models. Results The MR radiomic model with features extracted from the manually delineated lesions performed best among the radiomic models with an area under the curve (AUC) of 0.74. Radiology interpretation yielded an AUC of 0.75 and the clinical nomogram (MSKCC) an AUC of 0.67. A combination of the three prediction models gave the highest AUC of 0.79. Conclusion Radiomic analysis combined with radiology interpretation aid the MSKCC nomogram in predicting EPE in high- and non-favorable intermediate-risk patients.
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Filippov, Aleksandr, Lawrence Shaktah, Chi Wah Wong, Kimberley-Jane Bonjoc, Bassam Shaktah, Russell C. Rockne, Christine Brown, Ammar Chaudhry, and Behnam Badie. "NIMG-85. RADIOMIC FEATURES PREDICTIVE OF RESPONSE IN HGG-TARGETING CAR-T THERAPY." Neuro-Oncology 24, Supplement_7 (November 1, 2022): vii185. http://dx.doi.org/10.1093/neuonc/noac209.703.
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Abstract SIGNIFICANCE Radiomics may improve precision medicine in CAR-T (Chimeric Antigen Receptor T Cell) therapy patient selection. BACKGROUND High-Grade Glioma (HGG) is a heterogenous primary CNS neoplasm with a high recurrence rate and poor outcomes. Many studies are exploring CAR T cells to combat HGG. Radiomic models have shown value in identifying biomarkers predictive of tumor genetics, response, and patient prognosis. In this study, we explore radiomic features derived from four clinical sequences and volumes of edema and enhancing tumor to predict treatment response to the first three doses of CAR-T therapy. METHODS In this IRB-approved phase 1 clinical trial (IRB 13384), patients underwent surgical resection of the tumor and received CAR-T cell therapy. Of the 82 patients accrued, 59 (20 females, median age = 49) completed three cycles of therapy and had 3T field strength MRI scans with minimal imaging artifacts. T1 weighted pre-contrast, T1 weighted post-contrast, T2 weighted, and T2 FLuid Attenuated Inversion Recovery sequences were used to generate 3D and 2D radiomics features. In total 28,541 radiomic features were generated per patient using the images prior to CAR-T administration. Each patient’s response to treatment after three cycles was determined to be either stable disease (29 patients) or progression. The radiomic feature set dimensionality was reduced using Maximum Relevance Minimum Redundancy. 10-fold cross-validation XGBoost was used to determine radiomic features predictive of treatment response with a randomized grid search for hyperparameter tuning. RESULTS Six radiomic features (four shape-based), had high SHapley Additive exPlanations (SHAP)-based importance feature predictive of RANO response with an AUC >0.73. CONCLUSION Despite the limited study size, such imaging-based radiomic models can serve as a potential basis for optimizing clinical trial design through more precise patient screening and providing potential predictive imaging biomarkers of whether a patient will respond to CAR-T cell therapy in HGGs.
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Dovrou, Aikaterini, Ekaterini Bei, Stelios Sfakianakis, Kostas Marias, Nickolas Papanikolaou, and Michalis Zervakis. "Synergies of Radiomics and Transcriptomics in Lung Cancer Diagnosis: A Pilot Study." Diagnostics 13, no. 4 (February 15, 2023): 738. http://dx.doi.org/10.3390/diagnostics13040738.
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Radiotranscriptomics is an emerging field that aims to investigate the relationships between the radiomic features extracted from medical images and gene expression profiles that contribute in the diagnosis, treatment planning, and prognosis of cancer. This study proposes a methodological framework for the investigation of these associations with application on non-small-cell lung cancer (NSCLC). Six publicly available NSCLC datasets with transcriptomics data were used to derive and validate a transcriptomic signature for its ability to differentiate between cancer and non-malignant lung tissue. A publicly available dataset of 24 NSCLC-diagnosed patients, with both transcriptomic and imaging data, was used for the joint radiotranscriptomic analysis. For each patient, 749 Computed Tomography (CT) radiomic features were extracted and the corresponding transcriptomics data were provided through DNA microarrays. The radiomic features were clustered using the iterative K-means algorithm resulting in 77 homogeneous clusters, represented by meta-radiomic features. The most significant differentially expressed genes (DEGs) were selected by performing Significance Analysis of Microarrays (SAM) and 2-fold change. The interactions among the CT imaging features and the selected DEGs were investigated using SAM and a Spearman rank correlation test with a False Discovery Rate (FDR) of 5%, leading to the extraction of 73 DEGs significantly correlated with radiomic features. These genes were used to produce predictive models of the meta-radiomics features, defined as p-metaomics features, by performing Lasso regression. Of the 77 meta-radiomic features, 51 can be modeled in terms of the transcriptomic signature. These significant radiotranscriptomics relationships form a reliable basis to biologically justify the radiomics features extracted from anatomic imaging modalities. Thus, the biological value of these radiomic features was justified via enrichment analysis on their transcriptomics-based regression models, revealing closely associated biological processes and pathways. Overall, the proposed methodological framework provides joint radiotranscriptomics markers and models to support the connection and complementarities between the transcriptome and the phenotype in cancer, as demonstrated in the case of NSCLC.
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Smyczynska, Urszula, Szymon Grabia, Zuzanna Nowicka, Anna Papis-Ubych, Robert Bibik, Tomasz Latusek, Tomasz Rutkowski, Jacek Fijuth, Wojciech Fendler, and Bartlomiej Tomasik. "Prediction of Radiation-Induced Hypothyroidism Using Radiomic Data Analysis Does Not Show Superiority over Standard Normal Tissue Complication Models." Cancers 13, no. 21 (November 8, 2021): 5584. http://dx.doi.org/10.3390/cancers13215584.
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State-of-art normal tissue complication probability (NTCP) models do not take into account more complex individual anatomical variations, which can be objectively quantitated and compared in radiomic analysis. The goal of this project was development of radiomic NTCP model for radiation-induced hypothyroidism (RIHT) using imaging biomarkers (radiomics). We gathered CT images and clinical data from 98 patients, who underwent intensity-modulated radiation therapy (IMRT) for head and neck cancers with a planned total dose of 70.0 Gy (33–35 fractions). During the 28-month (median) follow-up 27 patients (28%) developed RIHT. For each patient, we extracted 1316 radiomic features from original and transformed images using manually contoured thyroid masks. Creating models based on clinical, radiomic features or a combination thereof, we considered 3 variants of data preprocessing. Based on their performance metrics (sensitivity, specificity), we picked best models for each variant ((0.8, 0.96), (0.9, 0.93), (0.9, 0.89) variant-wise) and compared them with external NTCP models ((0.82, 0.88), (0.82, 0.88), (0.76, 0.91)). We showed that radiomic-based models did not outperform state-of-art NTCP models (p > 0.05). The potential benefit of radiomic-based approach is that it is dose-independent, and models can be used prior to treatment planning allowing faster selection of susceptible population.
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Li, Xiaoyue, Han Chen, Feipeng Zhao, Yun Zheng, Haowen Pang, and Li Xiang. "Development of a Radiotherapy Localisation Computed Tomography-Based Radiomic Model for Predicting Survival in Patients With Nasopharyngeal Carcinoma Treated With Intensity-Modulated Radiotherapy Following Induction Chemotherapy." Cancer Control 29 (January 2022): 107327482210768. http://dx.doi.org/10.1177/10732748221076820.
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Background Our purpose is to develop a model combining radiomic features of radiotherapy localisation computed tomography and clinical characteristics that can be used to estimate overall survival in patients with nasopharyngeal carcinoma treated with intensity-modulated radiotherapy following induction chemotherapy. Methods We recruited 145 patients with pathologically confirmed nasopharyngeal carcinoma between February 2012 and April 2015. In total, 851 radiomic features were extracted from radiotherapy localisation computed tomography images for the gross tumour volume of the nasopharynx and the gross tumour volume of neck metastatic lymph nodes. The least absolute shrinkage and selection operator algorithm was applied to select radiomics features, build the model and calculate the Rad-score. The patients were divided into high- and low-risk groups based on their Rad-scores. A nomogram for estimating overall survival based on both radiomic and clinical features was generated using multivariate Cox regression hazard models. Prediction reliability was evaluated using Harrell’s concordance index. Results In total, seven radiomic features and one clinical characteristic were extracted for survival analysis, and the combination of radiomic and clinical features was a better predictor of overall survival (concordance index = .849 [confidence interval: .782-.916]) than radiomic features (concordance index = .793 [confidence interval: .697-.890]) or clinical characteristics (concordance index = .661 [confidence interval: .673-.849]) alone. Conclusion Our results show that a nomogram combining radiomic features of radiotherapy localisation computed tomography and clinical characteristics can predict overall survival in patients with nasopharyngeal carcinoma treated with intensity-modulated radiotherapy following induction chemotherapy more effectively than radiomic features or clinical characteristics alone.
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Ho, Lok-Man, Sai-Kit Lam, Jiang Zhang, Chi-Leung Chiang, Albert Chi-Yan Chan, and Jing Cai. "Association of Multi-Phasic MR-Based Radiomic and Dosimetric Features with Treatment Response in Unresectable Hepatocellular Carcinoma Patients following Novel Sequential TACE-SBRT-Immunotherapy." Cancers 15, no. 4 (February 9, 2023): 1105. http://dx.doi.org/10.3390/cancers15041105.
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This study aims to investigate the association of pre-treatment multi-phasic MR-based radiomics and dosimetric features with treatment response to a novel sequential trans-arterial chemoembolization (TACE) plus stereotactic body radiotherapy (SBRT) plus immunotherapy regimen in unresectable Hepatocellular Carcinoma (HCC) sub-population. Twenty-six patients with unresectable HCC were retrospectively analyzed. Radiomic features were extracted from 42 lesions on arterial phase (AP) and portal-venous phase (PVP) MR images. Delta-phase (DeltaP) radiomic features were calculated as AP-to-PVP ratio. Dosimetric data of the tumor was extracted from dose-volume-histograms. A two-sided independent Mann–Whitney U test was used to assess the clinical association of each feature, and the classification performance of each significant independent feature was assessed using logistic regression. For the 3-month timepoint, four DeltaP-derived radiomics that characterize the temporal change in intratumoral randomness and uniformity were the only contributors to the treatment response association (p-value = 0.038–0.063, AUC = 0.690–0.766). For the 6-month timepoint, DeltaP-derived radiomic features (n = 4) maintained strong clinical associations with the treatment response (p-value = 0.047–0.070, AUC = 0.699–0.788), additional AP-derived radiomic features (n = 4) that reflect baseline tumoral arterial-enhanced signal pattern and tumor morphology (n = 1) that denotes initial tumor burden were shown to have strong associations with treatment response (p-value = 0.028–0.074, AUC = 0.719–0.773). This pilot study successfully demonstrated associations of pre-treatment multi-phasic MR-based radiomics with tumor response to the novel treatment regimen.
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Pasello, Giulia, Alessandra Ferro, Elena Scagliori, Gisella Gennaro, Matilde Costa, Matteo Sepulcri, Marco Schiavon, et al. "Exploratory radiomic analysis of stage III non-small cell lung cancer CT images: Correlation with clinical-pathological characteristics." Journal of Clinical Oncology 40, no. 16_suppl (June 1, 2022): e20574-e20574. http://dx.doi.org/10.1200/jco.2022.40.16_suppl.e20574.
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e20574 Background: Recent evidences have suggested potential applications of radiomics in early diagnosis, prognostic stratification and treatment outcome prediction of Non-Small Cell Lung Cancer (NSCLC) patients. The purpose of this study is to evaluate the ability of radiomic analysis to discriminate between different clinical-pathological conditions in patients with stage III NSCLC. Methods: Baseline CT studies from 59 patients with stage III NSCLC referred to our Institution from 2010 and 2020 were retrospectively reviewed, and the segmentation of the main lung lesion and the extraction of 517 radiomic features performed using a commercial software. The number of features was reduced to 46 by means of principal component analysis applied using the R package “RadAR” (Radiomics Analysis with R). The Kruskal-Wallis test was applied to all the radiomic features in order to evaluate which of them can discriminate between 7 clinical dichotomous characteristics: tumor stage, type, presence of mutation, treatment response, relapse free survival (RFS), smoking habit, patient outcome. P < 0.05 means that there is a statistically significant difference between the two subgroups. Results: The median age at diagnosis was 69 years (range 43-83). Most patients were males (40/59 = 67.8%) and heavy smokers (36/59 = 61.0%). Adenocarcinoma was the most common histology (41/59 = 70.7%), while cases were almost equally splitted between stage IIIA (45.8%) and stage IIIB or IIIC (54.2%). Most selected radiomic features (29/46 = 63.0%) showed a statistically significant difference between patients with and without mutations. Ten (10/46 = 21.7%) radiomic features were associated with patient sex. Seven features (7/45 = 15.2%) were “sensitive” to the tumor clinical stage (stage IIIA vs. stage IIIB+IIIC), 4 (4/46 = 8.7%) to the histological type, and 2 (2/46 = 4.3%) to the patient outcome. None of the selected radiomic features was able to discriminate between responder and non-responder patients, current/previous smokers and never smokers, and patients with RFS lower than 12 months versus RFS equal or higher than 12 months. Conclusions: This preliminary analysis showed that radiomics has the potential of identifying mathematical features associated with clinical and histopathological characteristics in stage III NSCLC patients, which might feed multiparametric predictive models. Larger datasets and further analysis are necessary in order to confirm initial results.
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Pasello, Giulia, Alessandra Ferro, Elena Scagliori, Gisella Gennaro, Matilde Costa, Matteo Sepulcri, Marco Schiavon, et al. "Exploratory radiomic analysis of stage III non-small cell lung cancer CT images: Correlation with clinical-pathological characteristics." Journal of Clinical Oncology 40, no. 16_suppl (June 1, 2022): e20574-e20574. http://dx.doi.org/10.1200/jco.2022.40.16_suppl.e20574.
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e20574 Background: Recent evidences have suggested potential applications of radiomics in early diagnosis, prognostic stratification and treatment outcome prediction of Non-Small Cell Lung Cancer (NSCLC) patients. The purpose of this study is to evaluate the ability of radiomic analysis to discriminate between different clinical-pathological conditions in patients with stage III NSCLC. Methods: Baseline CT studies from 59 patients with stage III NSCLC referred to our Institution from 2010 and 2020 were retrospectively reviewed, and the segmentation of the main lung lesion and the extraction of 517 radiomic features performed using a commercial software. The number of features was reduced to 46 by means of principal component analysis applied using the R package “RadAR” (Radiomics Analysis with R). The Kruskal-Wallis test was applied to all the radiomic features in order to evaluate which of them can discriminate between 7 clinical dichotomous characteristics: tumor stage, type, presence of mutation, treatment response, relapse free survival (RFS), smoking habit, patient outcome. P < 0.05 means that there is a statistically significant difference between the two subgroups. Results: The median age at diagnosis was 69 years (range 43-83). Most patients were males (40/59 = 67.8%) and heavy smokers (36/59 = 61.0%). Adenocarcinoma was the most common histology (41/59 = 70.7%), while cases were almost equally splitted between stage IIIA (45.8%) and stage IIIB or IIIC (54.2%). Most selected radiomic features (29/46 = 63.0%) showed a statistically significant difference between patients with and without mutations. Ten (10/46 = 21.7%) radiomic features were associated with patient sex. Seven features (7/45 = 15.2%) were “sensitive” to the tumor clinical stage (stage IIIA vs. stage IIIB+IIIC), 4 (4/46 = 8.7%) to the histological type, and 2 (2/46 = 4.3%) to the patient outcome. None of the selected radiomic features was able to discriminate between responder and non-responder patients, current/previous smokers and never smokers, and patients with RFS lower than 12 months versus RFS equal or higher than 12 months. Conclusions: This preliminary analysis showed that radiomics has the potential of identifying mathematical features associated with clinical and histopathological characteristics in stage III NSCLC patients, which might feed multiparametric predictive models. Larger datasets and further analysis are necessary in order to confirm initial results.
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Kalasauskas, Darius, Michael Kosterhon, Naureen Keric, Oliver Korczynski, Andrea Kronfeld, Florian Ringel, Ahmed Othman, and Marc A. Brockmann. "Beyond Glioma: The Utility of Radiomic Analysis for Non-Glial Intracranial Tumors." Cancers 14, no. 3 (February 7, 2022): 836. http://dx.doi.org/10.3390/cancers14030836.
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The field of radiomics is rapidly expanding and gaining a valuable role in neuro-oncology. The possibilities related to the use of radiomic analysis, such as distinguishing types of malignancies, predicting tumor grade, determining the presence of particular molecular markers, consistency, therapy response, and prognosis, can considerably influence decision-making in medicine in the near future. Even though the main focus of radiomic analyses has been on glial CNS tumors, studies on other intracranial tumors have shown encouraging results. Therefore, as the main focus of this review, we performed an analysis of publications on PubMed and Web of Science databases, focusing on radiomics in CNS metastases, lymphoma, meningioma, medulloblastoma, and pituitary tumors.
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Shaheen, Asma, Syed Talha Bukhari, Maria Nadeem, Stefano Burigat, Ulas Bagci, and Hassan Mohy-ud-Din. "Overall Survival Prediction of Glioma Patients With Multiregional Radiomics." Frontiers in Neuroscience 16 (July 7, 2022). http://dx.doi.org/10.3389/fnins.2022.911065.
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Radiomics-guided prediction of overall survival (OS) in brain gliomas is seen as a significant problem in Neuro-oncology. The ultimate goal is to develop a robust MRI-based approach (i.e., a radiomics model) that can accurately classify a novel subject as a short-term survivor, a medium-term survivor, or a long-term survivor. The BraTS 2020 challenge provides radiological imaging and clinical data (178 subjects) to develop and validate radiomics-based methods for OS classification in brain gliomas. In this study, we empirically evaluated the efficacy of four multiregional radiomic models, for OS classification, and quantified the robustness of predictions to variations in automatic segmentation of brain tumor volume. More specifically, we evaluated four radiomic models, namely, the Whole Tumor (WT) radiomics model, the 3-subregions radiomics model, the 6-subregions radiomics model, and the 21-subregions radiomics model. The 3-subregions radiomics model is based on a physiological segmentation of whole tumor volume (WT) into three non-overlapping subregions. The 6-subregions and 21-subregions radiomic models are based on an anatomical segmentation of the brain tumor into 6 and 21 anatomical regions, respectively. Moreover, we employed six segmentation schemes – five CNNs and one STAPLE-fusion method – to quantify the robustness of radiomic models. Our experiments revealed that the 3-subregions radiomics model had the best predictive performance (mean AUC = 0.73) but poor robustness (RSD = 1.99) and the 6-subregions and 21-subregions radiomics models were more robust (RSD 1.39) with lower predictive performance (mean AUC 0.71). The poor robustness of the 3-subregions radiomics model was associated with highly variable and inferior segmentation of tumor core and active tumor subregions as quantified by the Hausdorff distance metric (4.4−6.5mm) across six segmentation schemes. Failure analysis revealed that the WT radiomics model, the 6-subregions radiomics model, and the 21-subregions radiomics model failed for the same subjects which is attributed to the common requirement of accurate segmentation of the WT volume. Moreover, short-term survivors were largely misclassified by the radiomic models and had large segmentation errors (average Hausdorff distance of 7.09mm). Lastly, we concluded that while STAPLE-fusion can reduce segmentation errors, it is not a solution to learning accurate and robust radiomic models.
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Yang, Min, Qiqi Cao, Zhihan Xu, Yingqian Ge, Shujiao Li, Fuhua Yan, and Wenjie Yang. "Development and Validation of a Machine Learning-Based Radiomics Model on Cardiac Computed Tomography of Epicardial Adipose Tissue in Predicting Characteristics and Recurrence of Atrial Fibrillation." Frontiers in Cardiovascular Medicine 9 (March 3, 2022). http://dx.doi.org/10.3389/fcvm.2022.813085.
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PurposeThis study aimed to evaluate the feasibility of differentiating the atrial fibrillation (AF) subtype and preliminary explore the prognostic value of AF recurrence after ablation using radiomics models based on epicardial adipose tissue around the left atrium (LA-EAT) of cardiac CT images.MethodThe cardiac CT images of 314 patients were collected wherein 251 and 63 cases were randomly enrolled in the training and validation cohorts, respectively. Mutual information and the random forest algorithm were used to screen for the radiomic features and construct the radiomics signature. Radiomics models reflecting the features of LA-EAT were built to differentiate the AF subtype, and the multivariable logistic regression model was adopted to integrate the radiomics signature and volume information. The same methodology and algorithm were applied to the radiomic features to explore the ability for predicting AF recurrence.ResultsThe predictive model constructed by integrating the radiomic features and volume information using a radiomics nomogram showed the best ability in differentiating AF subtype in the training [AUC, 0.915; 95% confidence interval (CI), 0.880–0.951] and validation (AUC, 0.853; 95% CI, 0.755–0.951) cohorts. The radiomic features have shown convincible predictive ability of AF recurrence in both training (AUC, 0.808; 95% CI, 0.750–0.866) and validation (AUC, 0.793; 95% CI, 0.654–0.931) cohorts.ConclusionsThe LA-EAT radiomic signatures are a promising tool in the differentiation of AF subtype and prediction of AF recurrence, which may have clinical implications in the early diagnosis of AF subtype and disease management.
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Xie, Qianrong, Yue Chen, Yimei Hu, Fanwei Zeng, Pingxi Wang, Lin Xu, Jianhong Wu, et al. "Development and validation of a machine learning-derived radiomics model for diagnosis of osteoporosis and osteopenia using quantitative computed tomography." BMC Medical Imaging 22, no. 1 (August 8, 2022). http://dx.doi.org/10.1186/s12880-022-00868-5.
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Abstract Background To develop and validate a quantitative computed tomography (QCT) based radiomics model for discriminating osteoporosis and osteopenia. Methods A total of 635 patients underwent QCT were retrospectively included from November 2016 to November 2019. The patients with osteopenia or osteoporosis (N = 590) were divided into a training cohort (N = 414) and a test cohort (N = 176). Radiomics features were extracted from the QCT images of the third lumbar vertebra. Minimum redundancy and maximum relevance and least absolute shrinkage and selection operator were used for data dimensional reduction, features selection and radiomics model building. Multivariable logistic regression was applied to construct the combined clinical-radiomic model that incorporated radiomics signatures and clinical characteristics. The performance of the combined clinical-radiomic model was evaluated by the area under the curve of receiver operator characteristic curve (ROC–AUC), accuracy, specificity, sensitivity, positive predictive value, and negative predictive value. Results The patients with osteopenia or osteoporosis were randomly divided into training and test cohort with a ratio of 7:3. Six more predictive radiomics signatures, age, alkaline phosphatase and homocysteine were selected to construct the combined clinical-radiomic model for diagnosis of osteoporosis and osteopenia. The AUC of the combined clinical-radiomic model was 0.96 (95% confidence interval (CI), 0.95 to 0.98) in the training cohort and 0.96 (95% CI 0.92 to 1.00) in the test cohort, which were superior to the clinical model alone (training-AUC = 0.81, test-AUC = 0.79). The calibration curve demonstrated that the radiomics nomogram had good agreement between prediction and observation and decision curve analysis confirmed clinically useful. Conclusions The combined clinical-radiomic model that incorporates the radiomics score and clinical risk factors, can serve as a reliable and powerful tool for discriminating osteoporosis and osteopenia.
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Cho, Daniel, David Clausi, and Alexander Wong. "Dermal Radiomics for Melanoma Screening." Vision Letters 1, no. 1 (October 31, 2015). http://dx.doi.org/10.15353/vsnl.v1i1.58.
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<p>Radiomics has shown considerable promise as a new, emerging<br />approach to computer-aided cancer screening. However, the idea<br />of adopting radiomics for melanoma screening has not been previously<br />explored, with clinical screening relying solely on visual assessment<br />of skin lesion, and thus suffers from low sensitivity and<br />specificity. In this study, a dermal radiomics framework is proposed<br />for computer-aided screening of melanoma, with the aim of improving<br />screening accuracy. A radiomic sequencer is designed to<br />generate radiomic sequences consisting of 367 dermal radiomic<br />features based on extracted physiological biomarkers from dermatological<br />imaging data. The extracted dermal radiomic sequences<br />were then employed to classify benign and malignant melanoma<br />via non-linear random forest classification, and showed superior<br />results in terms of sensitivity, specificity and accuracy when compared<br />to the-state-of-the-art feature models for melanoma classification.</p>
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Yang, Yan, WeiJie Fan, Tao Gu, Li Yu, HaiLing Chen, YangFan Lv, Huan Liu, GuangXian Wang, and Dong Zhang. "Radiomic Features of Multi-ROI and Multi-Phase MRI for the Prediction of Microvascular Invasion in Solitary Hepatocellular Carcinoma." Frontiers in Oncology 11 (October 7, 2021). http://dx.doi.org/10.3389/fonc.2021.756216.
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ObjectivesTo develop and validate an MR radiomics-based nomogram to predict the presence of MVI in patients with solitary HCC and further evaluate the performance of predictors for MVI in subgroups (HCC ≤ 3 cm and > 3 cm).Materials and MethodsBetween May 2015 and October 2020, 201 patients with solitary HCC were analysed. Radiomic features were extracted from precontrast T1WI, arterial phase, portal venous phase, delayed phase and hepatobiliary phase images in regions of the intratumoral, peritumoral and their combining areas. The mRMR and LASSO algorithms were used to select radiomic features related to MVI. Clinicoradiological factors were selected by using backward stepwise regression with AIC. A nomogram was developed by incorporating the clinicoradiological factors and radiomics signature. In addition, the radiomic features and clinicoradiological factors related to MVI were separately evaluated in the subgroups (HCC ≤ 3 cm and > 3 cm).ResultsHistopathological examinations confirmed MVI in 111 of the 201 patients (55.22%). The radiomics signature showed a favourable discriminatory ability for MVI in the training set (AUC, 0.896) and validation set (AUC, 0.788). The nomogram incorporating peritumoral enhancement, tumour growth type and radiomics signature showed good discrimination in the training (AUC, 0.932) and validation sets (AUC, 0.917) and achieved well-fitted calibration curves. Subgroup analysis showed that tumour growth type was a predictor for MVI in the HCC ≤ 3 cm cohort and peritumoral enhancement in the HCC > 3 cm cohort; radiomic features related to MVI varied between the HCC ≤ 3 cm and HCC > 3 cm cohort. The performance of the radiomics signature improved noticeably in both the HCC ≤ 3 cm (AUC, 0.953) and HCC > 3 cm cohorts (AUC, 0.993) compared to the original training set.ConclusionsThe preoperative nomogram integrating clinicoradiological risk factors and the MR radiomics signature showed favourable predictive efficiency for predicting MVI in patients with solitary HCC. The clinicoradiological factors and radiomic features related to MVI varied between subgroups (HCC ≤ 3 cm and > 3 cm). The performance of radiomics signature for MVI prediction was improved in both the subgroups.