Classifying Intraspinal Tumors: A Multi-modal MRI and Machine Learning Approach

Abstract Objective (s):Develop a predictive model for benign and malignant intraspinal tumors using MRI radiomics combined with machine learning, enhancing diagnostic accuracy. Methods: Retrospective data from 132 patients with intraspinal tumors (2020-2023) were analyzed. MRI scans were processed with 3D Slicer and Pyradiomics to extract features. Features were reduced using the LASSO algorithm and recursive elimination, followed by model construction using logistic regression, SVM, DT, RF, and XGBoost. Results: 15 key features were identified post-screening. The XGBoost model showed the highest accuracy, with AUCs of 0.992 (training) and 0.542 (test). RF and DT models also performed well, with AUCs of 0.964/0.885 and 0.986/0.543 respectively. Conclusion(s): Integrating MRI radiomics with machine learning, particularly XGBoost, effectively differentiates intraspinal tumors, offering a non-invasive diagnostic tool that enhances early identification and treatment planning.