Open Access

ARTICLE

Interpretable Cox-Guided Risk Stratification for Specialized Expert Learning in Pan-Cancer Survival Prediction

Manal Mohammed AL-Tamimi^1,2,*, Siti Norul Huda Sheikh Abdullah^1,*, Mohammad Khatim Hasan¹, Mohammed Azmi Al-Betar^3,4, Maw Shin Sim⁵, Abdulrahman Mohammed AL-Tamimi¹

1 Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia
2 Faculty of Administrative Science, Hadhramout University, AL-Mukalla, Yemen
3 Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, United Arab Emirates
4 Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, Jordan
5 Department of Pharmaceutical Life Sciences, Faculty of Pharmacy, Universiti Malaya, Kuala Lumpur, Malaysia

* Corresponding Authors: Manal Mohammed AL-Tamimi. Email: ; Siti Norul Huda Sheikh Abdullah. Email:

(This article belongs to the Special Issue: Artificial Intelligence Models in Healthcare: Challenges, Methods, and Applications)

Computer Modeling in Engineering & Sciences 2026, 147(2), 44 https://doi.org/10.32604/cmes.2026.079891

Received 09 February 2026; Accepted 15 April 2026; Issue published 27 May 2026

Abstract

Pan-cancer survival prediction remains a major challenge in personalized oncology due to profound tumor heterogeneity and the complexity of high-dimensional molecular data. Diverse risk profiles across cancer types and noisy, sparse features hinder deep learning models from capturing robust prognostic patterns. Prior pan-cancer studies predominantly focus on multimodal integration or unimodal gene expression analysis, leaving other informative modalities such as Copy Number Variation (CNV) and miRNA expression underexplored. We introduce a new formulation of mixture-of-experts (MoE) survival modeling that recasts expert assignment as a clinically interpretable risk-space decomposition problem. The proposed framework, CoxGuided-SE, constructs an ordered prognostic risk axis from a Cox proportional hazards model trained on clinical covariates, then uses quantile-based thresholds to deterministically stratify patients into risk-homogeneous groups and route them to specialized subnetworks. This design replaces black-box neural routing with a transparent, statistically principled alternative, preventing expert collapse without auxiliary regularization. Evaluated on 33 cancer types from TCGA across four modalities independently, namely clinical features, mRNA expression, miRNA expression, and CNV, CoxGuided-SE achieved substantial gains on high-dimensional genomic data, improving both discrimination and calibration. The most pronounced improvement occurred for CNV, widely considered one of the most challenging modalities for survival modeling, with a 24% increase in discrimination over the strongest baseline. The framework offers two-level interpretability: transparent patient routing based on clinical severity, and expert-specific feature importance within each subgroup. Feature-level analyses further confirm that experts learn complementary and non-redundant representations within each risk stratum. These results provide both a high-performing survival prediction framework and a principled explanation of when and why expert specialization is effective, advancing interpretable and trustworthy clinical AI for pan-cancer prognosis.

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Keywords

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Supplementary Material

Supplementary Material File

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