Oesophageal cancer multi-disciplinary tool: a co-designed, externally validated, machine learning tool for oesophageal cancer decision making.

ABSTRACT:

Background

The oesophageal cancer (OC) multi-disciplinary team (MDT) operates under significant pressures, handling complex decision-making. Machine learning (ML) can learn complex decision-making paradigms to improve efficiency, consistency, and cost if trained and deployed responsibly. We present an externally validated ML-based clinical decision support system (CDSS) designed to predict OC MDT treatment decisions and prognosticate palliative scenarios, co-designed using Responsible Research and Innovation (RRI) principles.

Methods

Clinicopathological data collected from 1931 patients between 4th September 2009, and 8th November 2022 were used to test and validate models trained through four ML algorithms to predict curative and palliative treatment pathways along with pal

SUBMITTER: Thavanesan N 

PROVIDER: S-EPMC12513115 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature