A Large Scale Multi Institutional study for Radiomics Driven Machine Learning for Meningioma Grading
Document Type
Article
Publication Date
10-31-2024
Abstract
This study aims to develop and evaluate radiomics-based machine learning (ML) models for predicting meningioma grades using multiparametric magnetic resonance imaging (MRI). The study utilized the BraTS-MEN dataset's training split, including 698 patients (524 with grade 1 and 174 with grade 2-3 meningiomas). We extracted 4872 radiomic features from T1, T1 with contrast, T2, and FLAIR MRI sequences using PyRadiomics. LASSO regression reduced features to 176. The data was split into training (60%), validation (20%), and test (20%) sets. Five ML algorithms (TabPFN, XGBoost, LightGBM, CatBoost, and Random Forest) were employed to build models differentiating low-grade (grade 1) from high-grade (grade 2-3) meningiomas. Hyperparameter tuning was performed using Optuna, optimizing model-specific parameters and feature selection. The CatBoost model demonstrated the best performance, achieving an area under the receiver operating characteristic curve (AUROC) of 0.838 [95% confidence interval (CI): 0.689-0.935], precision of 0.492 (95% CI: 0.371-0.623), recall of 0.838 (95% CI: 0.689-0.935), F1 score of 0.620 (95% CI: 0.495-0.722), accuracy of 0.729 (95% CI: 0.650-0.800), an area under the precision-recall curve (AUPRC) of 0.620 (95% CI: 0.433-0.753), and Brier score of 0.156 (95% CI: 0.122-0.200). Other models showed comparable performance, with mean AUROCs ranging from 0.752 to 0.784. The radiomics-based ML approach presented in this study showcases the potential for non-invasive and pre-operative grading of meningiomas using multiparametric MRI. Further validation on larger and independent datasets is necessary to establish the robustness and generalizability of these findings.
Recommended Citation
Karabacak, Mert; Patil, Shiv; Feng, Rui; Shrivastava, Raj K.; and Margetis, Konstantinos, "A Large Scale Multi Institutional study for Radiomics Driven Machine Learning for Meningioma Grading" (2024). Department of Medicine Faculty Papers. Paper 471.
https://jdc.jefferson.edu/medfp/471
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
PubMed ID
39478140
Language
English
Comments
This article is the author's final published version in Scientific Reports, Volume 14, Issue 1, 2024, Article number 26191.
The published version is available at https://doi.org/10.1038/s41598-024-78311-8.
Copyright © The Author(s) 2024