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Beyond binary classification: a pilot study of imaging-derived glioma severity modeling using T1-weighted and
Pamela Franco1, Cristian Montalba2,3,4, Raúl Caulier-Cisterna5
1Energy Transformation Center, Faculty of Engineering, Universidad Andrés Bello, Santiago, Chile.
Magma (New York, N.Y.)
|April 28, 2026
Summary
This study introduces a new machine learning method to create a continuous glioma severity score using MRI scans. This approach helps better understand tumor aggressiveness and intra-grade differences.
Area of Science:
- Neuroimaging
- Oncology
- Machine Learning
Background:
- Gliomas are complex brain tumors with varied responses to treatment.
- Accurate assessment of glioma aggressiveness is crucial for patient prognosis and treatment planning.
- Current machine learning methods often classify gliomas discretely, potentially missing nuances in tumor heterogeneity.
Purpose of the Study:
- To develop a regression-based framework for a continuous, imaging-derived glioma severity score.
- To assess tumor aggressiveness non-invasively, complementing established World Health Organization (WHO) grading.
- To capture intra-grade glioma heterogeneity beyond discrete classification.
Main Methods:
- Utilized 3D T1-weighted and Diffusion Tensor Imaging (DTI) MRI data from 36 glioma patients.
- Extracted radiomic features and employed Sequential Feature Selection with a Random Forest regressor.
- Evaluated 15 machine learning regression models using metrics like Mean Squared Error (MSE) and R-squared, with nested cross-validation.
Main Results:
- The Random Forest model demonstrated the best performance.
- T1-weighted MRI features were most significant, but DTI-derived measures, specifically axial diffusivity, improved model accuracy.
- The model provided a continuous severity score, reflecting relative tumor aggressiveness.
Conclusions:
- Regression modeling of radiomic features from MRI can estimate a continuous glioma severity score.
- This imaging-derived score aids in assessing intra-grade glioma heterogeneity.
- The score serves as a complementary tool, not a replacement for definitive WHO clinical grading.
