Pancreatic cancer resulted in 411,600 deaths globally in 2015. Pancreatic ductal adenocarcinoma (PDAC) is the most common type of pancreatic cancer and it is highly lethal. Survival for patients with PDAC is dismal due to its aggressive nature, thus the development of novel and reliable prognostic model for early detection and therapy is much desired. Here we proposed a prognostic framework for prediction of overall survival of PDAC patients based on predictors derived from pancreas CT scans and patient clinical variables. Our framework includes three parts: feature extraction, feature selection and survival prediction. First, 2436 radiomics features were extracted from CT scans and were combined with the clinical variables, and a Cox model was fitted to each covariate individually to select the most predictive features. The optimal cut-off was determined by cross-validation. Finally, gradient boosting with component-wise Cox’s proportional hazards model was utilized to predict the overall survival of patients. Our framework achieves excellent performance on MICCAI 2018 Pancreatic Cancer Survival Prediction Challenge dataset, achieving mean concordance index of 0.7016 using five-fold cross-validation.
Sun, L., & Zhang, S. (2018). Pancreatic cancer survival prediction using CT scans and clinical variables. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11042 LNCS, pp. 193–201). Springer Verlag. https://doi.org/10.1007/978-3-030-01045-4_24