Colorectal Cancer Outcome Prediction from H&E Whole Slide Images using Machine Learning and Automatically Inferred Phenotype Profiles

TL;DR

This paper presents a novel machine learning framework that predicts colorectal cancer outcomes from digitized H&E stained slides, effectively handling large, heterogeneous pathology images and extracting meaningful clinical features.

Contribution

The study introduces a new machine learning approach for colorectal cancer prognosis using automatically inferred phenotype profiles from whole slide images.

Findings

Effective prediction of colorectal cancer outcomes demonstrated

Framework extracts clinically meaningful features

Method handles large, heterogeneous pathology images

Abstract

Digital pathology (DP) is a new research area which falls under the broad umbrella of health informatics. Owing to its potential for major public health impact, in recent years DP has been attracting much research attention. Nevertheless, a wide breadth of significant conceptual and technical challenges remain, few of them greater than those encountered in the field of oncology. The automatic analysis of digital pathology slides of cancerous tissues is particularly problematic due to the inherent heterogeneity of the disease, extremely large images, amongst numerous others. In this paper we introduce a novel machine learning based framework for the prediction of colorectal cancer outcome from whole digitized haematoxylin & eosin (H&E) stained histopathology slides. Using a real-world data set we demonstrate the effectiveness of the method and present a detailed analysis of its different elements which corroborate its ability to extract and learn salient, discriminative, and clinically meaningful content.