CNN-based survival model for pancreatic ductal adenocarcinoma in medical imaging

BMC Medical Imaging

Edrise M. Lobo-Mueller¹  Paul Karanicolas²  Masoom A. Haider³  Farzad Khalvati³  Yucheng Zhang³  Steven Gallinger⁴ 

[1] Department of Radiology, McMaster University and Hamilton Health Sciences, Juravinski Hospital and Cancer Centre;Department of Surgery, Sunnybrook Health Sciences Centre;Institute of Medical Science, University of Toronto;Lunenfeld-Tanenbaum Research Institute, Sinai Health System;

关键词: Cox proportional hazard model;    Radiomics;    Convolutional neural network;    Survial analysis;   

DOI  :  10.1186/s12880-020-0418-1

来源: DOAJ

【 摘 要 】

Abstract Background 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. Results The proposed CNN-based survival model outperformed the traditional CPH-based radiomics approach in terms of concordance index and index of prediction accuracy, providing a better fit for patients’ survival patterns. Conclusions The proposed CNN-based survival model outperforms CPH-based radiomics pipeline in PDAC prognosis. This approach offers a better fit for survival patterns based on CT images and overcomes the limitations of conventional survival models.

【 授权许可】

Unknown