Deep learning-based survival prediction for multiple cancer types using histopathology images

Ellery Wulczyn, David F Steiner, Zhaoyang Xu, Apaar Sadhwani, Hongwu Wang, Isabelle Flament-Auvigne, Craig H Mermel, Po-Hsuan Cameron Chen, Yun Liu and Martin C Stumpe

PLOS ONE, 2020, vol. 15, issue 6, 1-18

Abstract: Providing prognostic information at the time of cancer diagnosis has important implications for treatment and monitoring. Although cancer staging, histopathological assessment, molecular features, and clinical variables can provide useful prognostic insights, improving risk stratification remains an active research area. We developed a deep learning system (DLS) to predict disease specific survival across 10 cancer types from The Cancer Genome Atlas (TCGA). We used a weakly-supervised approach without pixel-level annotations, and tested three different survival loss functions. The DLS was developed using 9,086 slides from 3,664 cases and evaluated using 3,009 slides from 1,216 cases. In multivariable Cox regression analysis of the combined cohort including all 10 cancers, the DLS was significantly associated with disease specific survival (hazard ratio of 1.58, 95% CI 1.28–1.70, p

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0233678

DOI: 10.1371/journal.pone.0233678

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