Pancreatic Cancer Survival Prediction Using CT Scans and Clinical Variables

Sun, Li; Zhang, Songtao

10.1007/978-3-030-01045-4_24

2018

16113349

1611-3349

SIMULATION, IMAGE PROCESSING, AND ULTRASOUND SYSTEMS FOR ASSISTED DIAGNOSIS AND NAVIGATION

11042

193-201

Granada, Spain

GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND

SPRINGER INTERNATIONAL PUBLISHING AG

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.

Pancreatic cancer Survival analysis Prognostic model

第一 ; 通讯

英语

CPCI-S ; EI

Computer Science

Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods

WOS:000477753600024

20184105917333

Diagnosis ; Diseases ; Feature extraction ; Forecasting ; Image segmentation ; Medical computing ; Medical imaging ; Patient treatment ; Ultrasonic applications

Medicine and Pharmacology:461.6 ; Computer Applications:723.5 ; Imaging Techniques:746 ; Ultrasonic Applications:753.3

Web of Science

Southern Univ Sci & Technol, Shenzhen 518055, Peoples R China

Sun, Li,Zhang, Songtao. Pancreatic Cancer Survival Prediction Using CT Scans and Clinical Variables[C]. GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND:SPRINGER INTERNATIONAL PUBLISHING AG,2018:193-201.