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Cancer Epidemiology, Biomarkers & Prevention : a Publication of the American Association for Cancer Research, Cosponsored by the American Society of Preventive Oncology|September 12, 2020
Radiomics Improves Cancer Screening and Early DetectionRobert J Gillies, Matthew B Schabath
Cold Spring Harbor Perspectives in Medicine|January 12, 2021
Application of Radiomics and Artificial Intelligence for Lung Cancer Precision MedicineIlke Tunali, Robert J Gillies, Matthew B Schabath
Cancer Research|June 6, 2022
Images Are Data: Challenges and Opportunities in the Clinical Translation of RadiomicsWei Mu, Matthew B Schabath, Robert J Gillies
Scientific Reports|June 14, 2019
Quantitative Imaging features Improve Discrimination of Malignancy in Pulmonary nodulesYoganand Balagurunathan, Matthew B Schabath, Hua Wang, et al.
European Journal of Nuclear Medicine and Molecular Imaging|December 7, 2019
Radiomics of 18F-FDG PET/CT images predicts clinical benefit of advanced NSCLC patients to checkpoint blockade immunotherapyWei Mu, Ilke Tunali, Jhanelle E Gray, et al.
IEEE Access : Practical Innovations, Open Solutions|January 5, 2019
Delta Radiomics Improves Pulmonary Nodule Malignancy Prediction in Lung Cancer ScreeningSaeed S Alahmari, Dmitry Cherezov, Dmitry Goldgof, et al.
European Journal of Radiology|October 26, 2016
Clinical and CT characteristics of surgically resected lung adenocarcinomas harboring ALK rearrangements or EGFR mutationsHua Wang, Matthew B Schabath, Ying Liu, et al.
Clinical Lung Cancer|November 16, 2017
Comparison Between Radiological Semantic Features and Lung-RADS in Predicting Malignancy of Screen-Detected Lung Nodules in the National Lung Screening TrialQian Li, Yoganand Balagurunathan, Ying Liu, et al.
British Journal of Cancer|April 8, 2021
Radiomics predicts risk of cachexia in advanced NSCLC patients treated with immune checkpoint inhibitorsWei Mu, Evangelia Katsoulakis, Christopher J Whelan, et al.
Conference Proceedings. IEEE International Conference on Systems, Man, and Cybernetics|November 27, 2018
Improving malignancy prediction through feature selection informed by nodule size ranges in NLSTDmitry Cherezov, Samuel Hawkins, Dmitry Goldgof, et al.
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