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Cancer Survival Analysis01:21

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...

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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.

Cancers
|June 26, 2026
PubMed
Summary

An interpretable machine learning (ML) model using swarm intelligence (SI) and multiparametric MRI radiomics effectively predicts overall survival (OS) in glioblastoma multiforme (GBM) patients, achieving significant risk stratification.

Keywords:
GBMartificial intelligencebrain tumorprecision oncologyquantitative imaging biomarkersradiomicssurvival analysis

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Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Medicine
  • Oncology

Background:

  • Glioblastoma multiforme (GBM) is an aggressive brain tumor with poor prognosis.
  • Accurate prediction of overall survival (OS) is crucial for treatment planning in GBM patients.
  • Multiparametric MRI-derived radiomic features (RFs) hold potential for non-invasive prognostic assessment.

Purpose of the Study:

  • To develop and validate an interpretable machine learning (ML) model for OS estimation in GBM patients.
  • To integrate a hybrid swarm intelligence (SI)-based feature selection with MRI-derived RFs.
  • To achieve robust risk stratification using an interpretable ML approach.

Main Methods:

  • A cohort of 276 GBM patients with pre-treatment MRI data was analyzed.
  • A hybrid SI-based feature selection method was combined with a regularized Cox regression model (Cox-LASSO).
  • Model performance was evaluated using concordance index (C-index) and validated through cross-validation and external testing.

Main Results:

  • The developed ML model achieved a C-index of 0.71 in the test dataset and 0.67 upon external validation.
  • The model demonstrated statistically significant risk stratification for OS (p < 0.001).
  • The final model incorporated patient age and ten independent radiomic features.

Conclusions:

  • The study successfully developed an interpretable ML model integrating SI-based feature selection and MRI radiomics for GBM OS prediction.
  • The model achieved significant risk stratification, outperforming traditional methods.
  • This represents one of the first studies to utilize an SI-based interpretable ML model for GBM survival stratification.