Overall survival time estimation for epithelioid peritoneal mesothelioma patients from whole-slide images
File(s)
Published version
Author(s)
Papadopoulos, Kleanthis Marios
Barmpoutis, Panagiotis
Stathaki, Tania
Kepenekian, Vahan
Dartigues, Peggy
Type
Journal Article
Abstract
Background: The advent of Deep Learning initiated a new era in which neural networks relying solely on Whole-Slide Images can estimate the survival time of cancer patients. Remarkably, despite deep learning’s potential in this domain, no prior research has been conducted on image-based survival analysis specifically for peritoneal mesothelioma. Prior studies performed statistical analysis to identify disease factors impacting patients’ survival time. Methods: Therefore, we introduce MPeMSupervisedSurv, a Convolutional Neural Network designed to predict the survival time of patients diagnosed with this disease. We subsequently perform patient stratification based on factors such as their Peritoneal Cancer Index and on whether patients received chemotherapy treatment. Results: MPeMSupervisedSurv demonstrates improvements over comparable methods. Using our proposed model, we performed patient stratification to assess the impact of clinical variables on survival time. Notably, the inclusion of information regarding adjuvant chemotherapy significantly enhances the model’s predictive prowess. Conversely, repeating the process for other factors did not yield significant performance improvements. Conclusions: Overall, MPeMSupervisedSurv is an effective neural network which can predict the survival time of peritoneal mesothelioma patients. Our findings also indicate that treatment by adjuvant chemotherapy could be a factor affecting survival time.
Date Issued
2024-03-13
Date Acceptance
2024-03-06
Citation
BioMedInformatics, 2024, 4 (1), pp.823-836
ISSN
2673-7426
Publisher
MDPI AG
Start Page
823
End Page
836
Journal / Book Title
BioMedInformatics
Volume
4
Issue
1
Subjects
convolutional neural networks
deep learning
patient outcomes
survival analysis
survival prediction
whole-slide images
Publication Status
Published
Article Number
4
Date Publish Online
2024-03-13