Evaluating machine learning models for post-surgery treatment response assessment in glioblastoma multiforme: a...

Developing quantitative methods to assess post-surgery treatment response in Glioblastoma Multiforme (GBM) is critical for improving patient outcomes and refining current subjective approaches. This study analyzes the performance of machine learning models trained on radiomic datasets derived from magnetic resonance imaging (MRI) scans of GBM patients.

MRI scans from 143 GBM patients receiving adjuvant therapy post-surgery were acquired and preprocessed. A total of 92 radiomic features, including 68 Gy-level co-occurrence matrix (GLCM)-based features calculated in four directions (0°, 45°, 90°, and 135°) and 24 Curvelet coefficient-based features, were extracted from each patient's segmented tumor cavity. Machine learning classifiers, including Support Vector Machine (SVM), Random Forest, K-Nearest Neighbors (KNN), AdaBoost, CatBoost, LightGBM, XGBoost, Gaussian Naïve Bayes (GNB), and Logistic Regression (LR), were trained on the extracted radiomics selected using sequential feature selection, LASSO, and PCA. Validation was performed with 10-fold cross-validation.

The proposed pipeline achieved an accuracy of 87% in classifying post-surgery treatment responses in GBM patients. This accuracy was achieved with the SVM trained on a combination of GLCM and Curvelet-based radiomics selected via forward sequential algorithm-8, and with KNN trained on GLCM and Curvelet radiomics combination selected using LASSO (alpha = 0.01). The LR model trained on Curvelet-based LASSO-selected radiomics (alpha = 0.01) also showed strong performance.

The results demonstrate that MRI-based radiomics, specifically GLCM and Curvelet features, can effectively train machine learning models to quantitatively assess GBM treatment response. These models serve as valuable tools to complement qualitative evaluations, enhancing accuracy and objectivity in post-surgery outcome assessment.

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