# Predictive power of combined inflammatory markers and magnetic resonance imaging features for glioma grading using machine learning: a retrospective study

## TL;DR

This study shows that combining MRI scans and blood markers with machine learning can accurately predict the severity of brain tumors called gliomas.

## Contribution

The novel contribution is integrating inflammatory markers with MRI features using machine learning to improve glioma grading accuracy.

## Key findings

- The support vector machine model achieved an AUC of 0.92 and F1 score of 0.91 in distinguishing glioma grades.
- Combining MRI features with inflammatory markers improved predictive performance for glioma grading.
- Five-fold cross-validation confirmed the reliability of the model's performance.

## Abstract

This study aimed to explore the potential of integrating magnetic resonance imaging (MRI) features with inflammatory markers to predict glioma grading using machine learning models.

A total of 179 glioma patients were analyzed. The dataset was randomly split into a training set (75%) and an independent test set (25%) to test hyperparameter. To enhance reliability, five-fold cross-validation was also applied during the training phase. Key MRI features and inflammatory markers, including relative apparent diffusion coefficient (rADC), monocyte count, lymphocyte-to-CRP ratio (LCR) and hemoglobin-albumin-lymphocyte-platelet index (HALP), were extracted and used as inputs for multiple machine learning classifiers. Model performance was assessed using metrics such as area under the curve (AUC), accuracy, and F1 score.

The support vector machine model exhibited superior predictive performance, achieving an AUC of 0.92 and an F1 score of 0.91, effectively distinguishing between high-grade and low-grade gliomas.

The combination of MRI features and inflammatory markers, analyzed through machine learning models like SVM, provides some clues for refining glioma prognosis and guiding personalized treatment strategies.

The online version contains supplementary material available at 10.1186/s12880-025-01946-0.