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ARTICLE

Exploring Machine Learning Approaches for Decision Support in Neoadjuvant Therapy of Locally Advanced Rectal Cancer

Eshita Dhar1,2, Muhammad Ashad Kabir3, Divyabharathy Ramesh Nadar4, Li-Jen Kuo5, Jitendra Jonnagaddala6,7, Yaoru Huang1, Mohy Uddin8,*, Shabbir Syed-Abdul1,2,9,*
1 Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
2 International Center for Health Information Technology, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
3 School of Computing, Mathematics and Engineering, Charles Sturt University, Bathurst, Australia
4 CGD Health Pvt Ltd., Mumbai, India
5 Division of Colorectal Surgery, Department of Surgery, Taipei Medical University Hospital, Taipei, Taiwan
6 Discipline of General Practice, School of Clinical Medicine, Faculty of Medicine, UNSW Sydney, Kensington, Australia
7 NMC Royal Hospital, Khalifa City, Abu Dhabi, United Arab Emirates
8 Research Quality Management Department, King Abdullah International Medical Research Center, King Saud bin Abdulaziz University for Health Sciences, Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia
9 School of Gerontology and Long-Term Care, College of Nursing, Taipei Medical University, Taipei, Taiwan
* Corresponding Author: Mohy Uddin. Email: email; Shabbir Syed-Abdul. Email: email

Oncology Research https://doi.org/10.32604/or.2026.074385

Received 10 October 2025; Accepted 07 January 2026; Published online 20 January 2026

Abstract

Objectives: Decisions regarding CT after nCCRT for locally advanced rectal cancer (LARC) are challenging due to limited evidence guiding treatment. This study aimed to (i) evaluate the predictive performance of machine learning (ML) models in patients treated with neoadjuvant concurrent chemoradiotherapy (nCCRT) alone vs. those receiving nCCRT plus chemotherapy (CT), (ii) identify features associated with treatment improvement, and (iii) derive ML-based thresholds for treatment response. Methods: This retrospective study included 409 patients with LARC treated at three affiliated hospitals of Taipei Medical University. Patients were categorised into two groups: nCCRT alone followed by surgery (n = 182) and nCCRT plus additional CT (n = 227). Thirty-four baseline demographic, tumor, and laboratory variables were analysed. Four ML algorithms (K-Star, Random Forest, Multilayer Perceptron, and Random Committee) were evaluated, while five feature-ranking algorithms identified influential attributes among improved patients across both treatments. Decision Stump and AdaBoostM1 were applied to derive threshold-based patterns. Results: K-Star achieved the highest accuracy for nCCRT alone (80.8%; AUC = 0.89), while Random Committee performed best for nCCRT plus CT (77.3%; AUC = 0.84). Clinical N stage (cN) ranked highest, followed by Sodium (Na), Glutamic pyruvic transaminase, estimated glomerular filtration rate, body weight, red blood cell count, mean corpuscular hemoglobin concentration, and blood urea nitrogen. Threshold patterns suggested that CT-related improvement aligned with higher lymphocyte percentage and lower platelet distribution width, whereas nCCRT-only improvement aligned with elevated eGFR, GPT, and cN = 2. Conclusions: ML-based analysis identified key predictors and demonstrated good model performance, supporting individualised post-nCCRT chemotherapy decisions.

Keywords

Machine learning; chemoradiotherapy; rectal cancer; treatment response; predictive modelling

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