Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study.

ABSTRACT:

Background

For virtually every patient with colorectal cancer (CRC), hematoxylin-eosin (HE)-stained tissue slides are available. These images contain quantitative information, which is not routinely used to objectively extract prognostic biomarkers. In the present study, we investigated whether deep convolutional neural networks (CNNs) can extract prognosticators directly from these widely available images.

Methods and findings

We hand-delineated single-tissue regions in 86 CRC tissue slides, yielding more than 100,000 HE image patches, and used these to train a CNN by transfer learning, reaching a nine-class accuracy of >94% in an independent data set of 7,180 images from 25 CRC patients. With this tool, we performed automated tissue decomposition of representative multitis

SUBMITTER: Kather JN 

PROVIDER: S-EPMC6345440 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature