Predicting overall survival using CT and pathological image features
Aim 1. To extract CT image texture features and to test the hypothesis that combining CT image texture features with clinical and epidemiological risk factors can reduce the false positive rate of CT image diagnosis.
Aim 2. To extract pathological image features and to identify markers from CT and pathological image features that are associated with overall survival.
Prediction of lung cancer risk at follow-up screening with low-dose CT: a training and validation study of a deep learning method.
Peng Huang*, Cheng T Lin*, Yuliang Li, Martin C Tammemagi, Malcolm V Brock, Sukhinder Atkar-Khattra, Yanxun Xu, Ping Hu, John R Mayo, Heidi Schmidt, Michel Gingras, Sergio Pasian, Lori Stewart, Scott Tsai, Jean M Seely, Daria Manos, Paul Burrowes, Rick Bhatia, Ming-Sound Tsao, Stephen Lam (*Joint first authors)
Lancet. 2019 Oct 17; Volume 1 (Issue 7): Pages E353-E362
Added Value of Computer-aided CT Image Features for Early Lung Cancer Diagnosis with Small Pulmonary Nodules: A Matched Case-Control Study.
Huang P, Park S, Yan R, Lee J, Chu LC, Lin CT, Hussien A, Rathmell J, Thomas B, Chen C, Hales R, Ettinger DS, Brock M, Hu P, Fishman EK, Gabrielson E, Lam S
Radiology. 2018; Volume 286 (Issue 1): Pages 286-295 PUBMED