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Published on: October 11, 2018
A Novel Swarm Intelligence-Driven Feature Selection for Interpretable Machine Learning in Multiparametric MRI-Based
Abdulkerim Duman1, Xianfang Sun2, James R Powell3
1School of Engineering, Cardiff University, Cardiff CF24 3AA, UK.
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.
