A novel swarm intelligence-driven feature selection for interpretable machine learning in multiparametric MRI-Based GBM overall survival analysis

Duman, Abdulkerim, Sun, Xianfang ORCID: https://orcid.org/0000-0002-6114-0766, Powell, James R. and Spezi, Emiliano ORCID: https://orcid.org/0000-0002-1452-8813 2026. A novel swarm intelligence-driven feature selection for interpretable machine learning in multiparametric MRI-Based GBM overall survival analysis. Cancers 18 (12) , 1888. 10.3390/cancers18121888

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Abstract

Background/Objectives: In this study, we develop and validate an interpretable machine learning (ML) model that integrates a hybrid swarm intelligence (SI)-based feature selection method with multiparametric magnetic resonance imaging (MRI)-derived RFs to estimate overall survival (OS) in glioblastoma multiforme (GBM) patients. Methods: A cohort of 276 GBM patients with open-access pre-treatment MRI data was used to perform comprehensive radiomic analysis. In the training (discovery) dataset, we employed five-fold cross-validation combined with bootstrapping to ensure robust methodological validation. Model evaluation covered the concordance index (C-index) with 95% confidence intervals (CIs). Additionally, survival stratification was performed using Kaplan–Meier curves and the log-rank test to separate patients into low- and high-risk groups for OS. The final survival model integrates patient age and ten independent RFs. Results: The model’s performance in the holdout test dataset was evaluated by a C-index of 0.71 (95% CI: 0.63–0.80), exhibiting statistically significant risk stratification (p = 3 × 10−4). Upon external validation, the model achieved a C-index of 0.67, maintaining statistical significance (p = 1 × 10−2). Conclusions: The research combined a traditional regularized Cox regression (Cox-LASSO) model with a new SI-based LASSO-PSO method, yielding significant stratification. To our knowledge, the present study offers one of the first studies to document the use of an interpretable ML model with an SI-based approach for successful risk stratification based on OS.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Schools > Engineering Schools > Computer Science & Informatics
Additional Information:	License information from Publisher: LICENSE 1: URL: https://creativecommons.org/licenses/by/4.0/, Start Date: 2026-06-10
Publisher:	MDPI
Date of First Compliant Deposit:	25 June 2026
Date of Acceptance:	7 June 2026
Last Modified:	25 Jun 2026 11:00
URI:	https://orca.cardiff.ac.uk/id/eprint/187745

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