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BMC Medical Imaging

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Open Access 01-12-2020 | Research article

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

Authors: Yucheng Zhang, Edrise M. Lobo-Mueller, Paul Karanicolas, Steven Gallinger, Masoom A. Haider, Farzad Khalvati

Published in: BMC Medical Imaging | Issue 1/2020

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.

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Title: CNN-based survival model for pancreatic ductal adenocarcinoma in medical imaging
Authors: Yucheng Zhang
Edrise M. Lobo-Mueller
Paul Karanicolas
Steven Gallinger
Masoom A. Haider
Farzad Khalvati
Publication date: 01-12-2020
Publisher: BioMed Central
Published in: BMC Medical Imaging / Issue 1/2020
Electronic ISSN: 1471-2342
DOI: https://doi.org/10.1186/s12880-020-0418-1

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

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

Abstract

Background

Results

Conclusions

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Abstract

Background

Results

Conclusions

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