|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
|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
|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
|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
|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), 1-9, 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
|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
|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
|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
|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, 109503Z, 2019
|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
|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