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  1. Home
  3. Predicting Therapeutic Clinical Trial Enrollment For Adult Patients With Low- And High-grade Glioma Using Supervised Machine Learning.
  1. Home
  3. Predicting Therapeutic Clinical Trial Enrollment For Adult Patients With Low- And High-grade Glioma Using Supervised Machine Learning.

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Predicting therapeutic clinical trial enrollment for adult patients with low- and high-grade glioma using supervised

Mulki Mehari1, Gayathri Warrier1, Abraham Dada1

  • 1Department of Neurosurgery, University of California, San Francisco, San Francisco, CA 94143, USA.

Science Advances
|June 4, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Clinical trial enrollment for glioma patients varies by demographic group. Boosted neural networks revealed that oncologic factors influence enrollment in the general population, while socioeconomic factors are key for minority patients.

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Area of Science:

  • Neuro-oncology
  • Biostatistics
  • Clinical Trial Design

Background:

  • Glioma incidence and clinical trial enrollment rates are mismatched across diverse patient populations.
  • Traditional statistical methods have identified individual predictors of trial enrollment but lack insight into factor interactions.

Purpose of the Study:

  • To investigate the interactive effects of demographic, socioeconomic, and oncologic variables on clinical trial enrollment in glioma patients.
  • To develop predictive models for trial enrollment using boosted neural networks (BNNs).

Main Methods:

  • Designed and applied BNN models to datasets of glioma patients, including analyses for the whole cohort, women-only, and minority-only subgroups.
  • Externally validated the developed BNN models on independent patient cohorts.
  • Quantified the total effect (TE) of various variables on enrollment probability.

    • Main Results:

    • For the overall glioma cohort and women-only subgroup, oncologic variables (Karnofsky Performance Status, chemotherapy, tumor location, seizures) were the most influential factors in trial enrollment.
    • For the minority-only subgroup, socioeconomic variables (insurance status, occupation, employment status) demonstrated the highest influence on enrollment.

    Conclusions:

    • Understanding the distinct drivers of trial enrollment across different demographic groups is crucial for improving clinical trial accrual.
    • Tailored, patient-specific strategies addressing oncologic factors for the general population and socioeconomic factors for minority groups may enhance participation in glioma clinical trials.