Predicting patient outcomes (TNBC) based on positions of cancer islands and CD8+ T cells using machine learning approach

TL;DR

This paper introduces a machine learning approach to classify small tumor patches in triple-negative breast cancer (TNBC) and uses the proportion of 'good' patches to predict patient prognosis, offering an automated prognostic tool.

Contribution

The study presents a novel machine learning method for classifying TNBC tumor patches and predicting prognosis based on patch quality percentages.

Findings

High accuracy in classifying tumor patches

Proportion of 'good' patches correlates with patient outcomes

Method applicable to other cancer types

Abstract

Machine learning method is being applied in cancer research. In this work, we propose a method to classify the small patch of triple-negative breast cancer (TNBC) tumor and use the overall percentage of "good" patches as a marker to predict the prognosis, which is an automatic method of prognosis and could also be used for other cancers.