TY - JOUR AU - Zhang, Yucheng AU - Lobo-Mueller, Edrise M. AU - Karanicolas, Paul AU - Gallinger, Steven AU - Haider, Masoom A. AU - Khalvati, Farzad PY - 2020 DA - 2020/02/03 TI - CNN-based survival model for pancreatic ductal adenocarcinoma in medical imaging JO - BMC Medical Imaging SP - 11 VL - 20 IS - 1 AB - Cox proportional hazard model (CPH) is commonly used in clinical research for survival analysis. In quantitative medical imaging (radiomics) studies, CPH plays an important role in feature reduction and modeling. However, the underlying linear assumption of CPH model limits the prognostic performance. In this work, using transfer learning, a convolutional neural network (CNN) based survival model was built and tested on preoperative CT images of resectable Pancreatic Ductal Adenocarcinoma (PDAC) patients. SN - 1471-2342 UR - https://doi.org/10.1186/s12880-020-0418-1 DO - 10.1186/s12880-020-0418-1 ID - Zhang2020 ER -