RADIOMICS FEATURE-BASED MACHINE LEARNING FOR PREOPERATIVE GRADING OF MENINGIOMAS: A STUDY USING AUTOML

Authors : Emin Demırel, Çiğdem Özer Gökaslan

Pages : 168-173

Doi:10.18229/kocatepetip.1581078

View : 45 | Download : 56

Publication Date : 2025-04-28

Article Type : Research Paper

Abstract :OBJECTIVE: One of the most common primary intracranial neoplasms is meningiomas. Correct preoperative classification of these tumors is crucial for appropriate management of patients and treatment decisions. In this current study, we aimed to develop a radiomic feature-based machine learning model to predict grade I and grade II patients using open source software. MATERIAL AND METHODS: Meningioma-SEG-CLASS open source dataset was collected from 96 untreated patients who underwent surgical resection between 2010 and 2019. Radiomic features of tumors were extracted from segmentation data shared as open source. AutoGluon AutoML platform was used to develop our automated machine learning algorithms. RESULTS: AutoGluon AutoML machine learning models developed after necessary feature selection processes showed the best performance compared to the ensemble L2 model. These results are acceptable with 0.8205 AUC and 0.8000 F1 score on the test set, indicating good generalization ability of the model. CONCLUSIONS: This study suggests that extraction of radiomic features from various MR sequences may help grade meningiomas better than traditional radiologic tests. This facilitates noninvasive preoperative tumor prediction, enabling better surgical planning and management.
Keywords : Menenjiom, Makine Öğrenmesi, MRG