Explainable Machine Learning-Based Prediction of Progression-Free Survival in Prostate Cancer: Retrospective Cohort Study.
5 affiliations
- 1Pengiran Anak Puteri Rashidah Sa'adatul Bolkiah Institute of Health Sciences, Universiti Brunei Darussalam, Bandar Seri Begawan, Brunei-Muara District, Brunei Darussalam.
- 2School of Digital Science, Universiti Brunei Darussalam, Bandar Seri Begawan, Brunei Darussalam.
- 3Faculty of Engineering and Computing, Atlantic Technological University, Donegal, Ireland.
- 4The Brunei Cancer Centre, Jerudong Park Medical Centre, Bandar Seri Begawan, Brunei-Muara District, Brunei Darussalam.
- 5Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore.
Abstract
Background
Progression-free survival (PFS) is a critical end point in oncology, yet real-world applications of individualized, explainable machine learning (ML) predictions remain limited.Objective
This study aimed to develop and validate explainable ML models to predict PFS using retrospective data from a national prostate cancer cohort in Brunei Darussalam.Methods
We analyzed a retrospective cohort of 212 patients (478 longitudinal observations) treated at the Brunei Cancer Centre (January 2018 to December 2024). Clinical, laboratory, and treatment data were harmonized, with missing values imputed via extremely randomized trees. Longitudinal patterns were captured using a recurrent autoencoder to generate latent representations. We compared 4 modeling approaches: Cox proportional hazards, random survival forest (RSF), gradient boosting survival (GBS), and deep neural network survival models. Performance was evaluated using time-dependent area under the receiver operating characteristic curve (AUC), Harrell C-index, and integrated Brier score (IBS), with Shapley additive explanations (SHAP) used for interpretability.Results
RSF demonstrated improved discriminative performance and balanced calibration, achieving a C-index of 0.906 and AUCs of 0.941 and 0.917 at 4 and 5 years (IBS=0.0698). In contrast, the traditional CPH model performed poorly (C-index 0.531 and AUC 0.706 at 4 years and 0.833 at 5 years). Deep survival (AUCs of 0.941 at 4 years and 0.917 at 5 years, C-index 0.719, and IBS=0.0887) and GBS (AUCs of 0.765 at 4 years and 0.833 at 5 years, C-index 0.844, and IBS=0.0590) models showed moderate performance. SHAP analysis identified sodium, alanine aminotransferase, mean corpuscular hemoglobin, platelet count, and specific treatment categories as key drivers of increased progression risk.Conclusions
Tree-based ensemble approaches, particularly RSF integrated with SHAP, offer high accuracy for personalized risk stratification in prostate cancer. These findings highlight the potential of explainable ML to enhance clinical decision-making. However, external validation in a larger multi-institutional, multiomics dataset is required before routine clinical implementation.
Related
- Explainable machine learning–based prediction of postoperative pulmonary infection after aneurysmal subarachnoid hemorrhage clipping and exploratory analysis of enteral nutrition timing: a dual-cohort retrospective study
- BREAKING THE BLACK BOX IN HEMATOPOIETIC STEM CELL TRANSPLANTATION: AN EXPLAINABLE MACHINE LEARNING WEB APPLICATION FOR PERSONALIZED SURVIVAL PREDICTION
- Explainable machine learning for preoperative prediction of acute kidney injury after intertrochanteric fracture surgery in older adults
- Admission-time prediction of hospital length of stay in diverticular disease using explainable machine learning models
- Non-Invasive Prediction of Metabolic Syndrome Using Explainable Machine Learning
BibTeX
@article{tun2026explainable,
title = {{Explainable Machine Learning-Based Prediction of Progression-Free Survival in Prostate Cancer: Retrospective Cohort Study.}},
author = {Hein Minn Tun and Lin Naing and Owais Ahmed Malik and Muhammad Syafiq Abdullah and Thu Ta and Hanif Abdul Rahman},
journal = {JMIR cancer},
year = {2026},
month = oct,
doi = {10.2196/91510},
url = {https://europepmc.org/article/MED/42832781},
note = {CC BY},
}
RIS
TY - JOUR
TI - Explainable Machine Learning-Based Prediction of Progression-Free Survival in Prostate Cancer: Retrospective Cohort Study.
AU - Hein Minn Tun
AU - Lin Naing
AU - Owais Ahmed Malik
AU - Muhammad Syafiq Abdullah
AU - Thu Ta
AU - Hanif Abdul Rahman
PY - 2026
DA - 2026/10/05
JO - JMIR cancer
DO - 10.2196/91510
UR - https://europepmc.org/article/MED/42832781
AB - Background Progression-free survival (PFS) is a critical end point in oncology, yet real-world applications of individualized, explainable machine learning (ML) predictions remain limited.Objective This study aimed to develop and validate explainable ML models to predict PFS using retrospective data from a national prostate cancer cohort in Brunei Darussalam.Methods We analyzed a retrospective cohort of 212 patients (478 longitudinal observations) treated at the Brunei Cancer Centre (January 2018 to December 2024). Clinical, laboratory, and treatment data were harmonized, with missing values imputed via extremely randomized trees. Longitudinal patterns were captured using a recurrent autoencoder to generate latent representations. We compared 4 modeling approaches: Cox proportional hazards, random survival forest (RSF), gradient boosting survival (GBS), and deep neural network survival models. Performance was evaluated using time-dependent area under the receiver operating characteristic curve (AUC), Harrell C-index, and integrated Brier score (IBS), with Shapley additive explanations (SHAP) used for interpretability.Results RSF demonstrated improved discriminative performance and balanced calibration, achieving a C-index of 0.906 and AUCs of 0.941 and 0.917 at 4 and 5 years (IBS=0.0698). In contrast, the traditional CPH model performed poorly (C-index 0.531 and AUC 0.706 at 4 years and 0.833 at 5 years). Deep survival (AUCs of 0.941 at 4 years and 0.917 at 5 years, C-index 0.719, and IBS=0.0887) and GBS (AUCs of 0.765 at 4 years and 0.833 at 5 years, C-index 0.844, and IBS=0.0590) models showed moderate performance. SHAP analysis identified sodium, alanine aminotransferase, mean corpuscular hemoglobin, platelet count, and specific treatment categories as key drivers of increased progression risk.Conclusions Tree-based ensemble approaches, particularly RSF integrated with SHAP, offer high accuracy for personalized risk stratification in prostate cancer. These findings highlight the potential of explainable ML to enhance clinical decision-making. However, external validation in a larger multi-institutional, multiomics dataset is required before routine clinical implementation.
SN - 2369-1999
ER -