| Title: |
Oesophageal cancer multi-disciplinary tool: a co-designed, externally validated, machine learning tool for oesophageal cancer decision making |
| Authors: |
Thavanesan, N; Naiseh, M; Terol, M; Rahman, SA; Hill, SL; Parfitt, C; Walters, ZS; Ramchurn, S; Markar, S; Owen, R; Maynard, N; Azim, T; Belkatir, Z; Vallejos Perez, E; McCord, M; Underwood, T; Vigneswaran, G |
| Publisher Information: |
Elsevier |
| Publication Year: |
2025 |
| Collection: |
Oxford University Research Archive (ORA) |
| Description: |
BackgroundThe 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.MethodsClinicopathological 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 palliative prognosis. 953 OC cases treated at University Hospitals Southampton (UHS) were used to train ML models which were externally validated on 978 OC cases from Oxford University Hospitals (OUH). Model performance was evaluated using Area Under Curve (AUC) for treatment classifiers and calibration curves for survival models. A parallel RRI program at the University of Southampton (United Kingdom) combining clinician interviews and inter-disciplinary workshops was conducted between 16.3.23 and 23.5.24. The RRI program comprised a group of 17 domain experts comprising programmers, computer scientists, clinicians and patient representatives to allow end-users to contribute towards the co-design of the CDSS user interface.FindingsCohorts differed in baseline characteristics, with the external cohort (OUH) being younger, having better performance status, and a higher prevalence of pulmonary and vascular disease. Despite these differences, on internal validation (UHS cohort) mean AUCs for the primary treatment model were: MLR 0.905 ± 0.048, XGB 0.909 ± 0.044 and RF 0.883 ± 0.059 (k = 5 cross-validation) and MLR 0.866 (95% CI 0.866-0.867), XGB 0.863 (0.862-0.864), RF 0.863 (0.867-0.868) on bootstrapped resampling. For the palliative classifier, mean ... |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| Relation: |
https://doi.org/10.1016/j.eclinm.2025.103527 |
| DOI: |
10.1016/j.eclinm.2025.103527 |
| Availability: |
https://doi.org/10.1016/j.eclinm.2025.103527; https://ora.ox.ac.uk/objects/uuid:8dbbcc50-acdd-4df9-9f25-23db21328c62 |
| Rights: |
info:eu-repo/semantics/openAccess ; CC Attribution (CC BY) |
| Accession Number: |
edsbas.4E207E45 |
| Database: |
BASE |