| Predicting malignant nodules from screening CT scans S Hawkins, H Wang, Y Liu, A Garcia, O Stringfield, H Krewer, Q Li, ... Journal of Thoracic Oncology 11 (12), 2120-2128, 2016 | 317 | 2016 |
| Radiomics of lung nodules: a multi-institutional study of robustness and agreement of quantitative imaging features J Kalpathy-Cramer, A Mamomov, B Zhao, L Lu, D Cherezov, S Napel, ... Tomography 2 (4), 430, 2016 | 142 | 2016 |
| Delta radiomics improves pulmonary nodule malignancy prediction in lung cancer screening SS Alahmari, D Cherezov, DB Goldgof, LO Hall, RJ Gillies, MB Schabath Ieee Access 6, 77796-77806, 2018 | 92 | 2018 |
| Stability and reproducibility of computed tomography radiomic features extracted from peritumoral regions of lung cancer lesions I Tunali, LO Hall, S Napel, D Cherezov, A Guvenis, RJ Gillies, ... Medical physics 46 (11), 5075-5085, 2019 | 65 | 2019 |
| Revealing tumor habitats from texture heterogeneity analysis for classification of lung cancer malignancy and aggressiveness D Cherezov, D Goldgof, L Hall, R Gillies, M Schabath, H Müller, ... Scientific reports 9 (1), 4500, 2019 | 46 | 2019 |
| Delta radiomic features improve prediction for lung cancer incidence: A nested case–control analysis of the National Lung Screening Trial D Cherezov, SH Hawkins, DB Goldgof, LO Hall, Y Liu, Q Li, ... Cancer medicine 7 (12), 6340-6356, 2018 | 38 | 2018 |
| A radiogenomics ensemble to predict EGFR and KRAS mutations in NSCLC S Moreno, M Bonfante, E Zurek, D Cherezov, D Goldgof, L Hall, ... Tomography 7 (2), 154-168, 2021 | 25 | 2021 |
| Semi‐automated pulmonary nodule interval segmentation using the NLST data Y Balagurunathan, A Beers, J Kalpathy‐Cramer, M McNitt‐Gray, ... Medical physics 45 (3), 1093-1107, 2018 | 20 | 2018 |
| Deep radiomics: deep learning on radiomics texture images R Paul, S Kariev, D Cherezov, MB Schabath, RJ Gillies, LO Hall, ... Medical Imaging 2021: Computer-Aided Diagnosis 11597, 8-17, 2021 | 9 | 2021 |
| Lung nodule sizes are encoded when scaling CT image for CNN's D Cherezov, R Paul, N Fetisov, RJ Gillies, MB Schabath, DB Goldgof, ... Tomography 6 (2), 209, 2020 | 9 | 2020 |
| Improving malignancy prediction through feature selection informed by nodule size ranges in NLST D Cherezov, S Hawkins, D Goldgof, L Hall, Y Balagurunathan, RJ Gillies, ... 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC …, 2016 | 9 | 2016 |
| Towards deep radiomics: nodule malignancy prediction using CNNs on feature images R Paul, D Cherezov, MB Schabath, RJ Gillies, LO Hall, DB Goldgof Medical Imaging 2019: Computer-Aided Diagnosis 10950, 997-1003, 2019 | 5 | 2019 |
| Rank acquisition impact on radiomics estimation (AсquIRE) in chest CT imaging: A retrospective multi-site, multi-use-case study D Cherezov, VS Viswanathan, P Fu, A Gupta, A Madabhushi Computer methods and programs in biomedicine 244, 107990, 2024 | 2 | 2024 |
| Resolving impact of technical and biological variability on the convolutional neural networks: evaluating chest x-ray scans D Cherezov, VS Viswanathan, A Gupta, A Madabhushi Medical Imaging 2023: Image Processing 12464, 805-816, 2023 | 1 | 2023 |
| A Radiogenomics Ensemble to Predict EGFR and KRAS Mutations in NSCLC. Tomography 2021, 7, 154–168 S Moreno, M Bonfante, E Zurek, D Cherezov, D Goldgof, L Hall, ... s Note: MDPI stays neutral with regard to jurisdictional claims in published …, 2021 | | 2021 |
| Standardization in Quantitative Imaging: A Comparison of Radiomics Feature Values Obtained by Different Software Packages On a Set of Digital Reference Objects M McNitt-Gray, S Napel, J Kalpathy-Cramer, A Jaggi, D Cherezov, ... MEDICAL PHYSICS 46 (6), E400-E400, 2019 | | 2019 |
| P1. 03-063 Quantitative Imaging Features Predict Incidence Lung Cancer in Low-Dose Computed Tomography (LDCT) Screening: Topic: Screening D Cherezov, S Hawkins, D Goldgof, L Hall, Y Balagurunathan, R Gillies, ... Journal of Thoracic Oncology 12 (1), S582, 2017 | | 2017 |
