Machine Learning Analysis of Single‐Voxel Proton MR Spectroscopy for Differentiating Solitary Fibrous Tumors and Meningiomas

Abstract

Solitary fibrous tumor (SFT), formerly known as hemangiopericytoma, is an uncommon brain tumor often confused with meningioma on MRI. Unlike meningiomas, SFTs exhibit a myoinositol peak on magnetic resonance spectroscopy (MRS). This study aimed to develop automated classifiers to distinguish SFT from meningioma grades using MRS data from a 26-year patient cohort.Four classification tasks were performed on short echo (SE), long echo (LE) time, and concatenated SE + LE spectra, with datasets split into 80% training and 20% testing sets. Sequential forward feature selection and linear discriminant analysis identified features to distinguish between meningioma Grade 1 (Men-1), meningioma grade 2 (Men-2), meningioma grade 3 (Men-3), and SFT (the 4-class classifier); Men-1 from Men-2 + 3 + SFT; meningioma (all) from SFT; and Men-1 from Men-2 + 3 and SFT. The best classifier was defined by the smallest balanced error rate (BER) in the testing phase.A total of 136 SE cases and 149 LE cases were analyzed. The best features in the 4-class classifier were myoinositol and alanine at SE, and myoinositol, glutamate, and glutamine at LE. Myoinositol alone distinguished SFT from meningiomas. Differentiating Men-1 from Men-2 was not possible with MRS, and combining higher meningioma grades did not improve distinction from Men-1. Notably, combining short and long echo times (TE) enhances classification performance, particularly in challenging outlier cases. Furthermore, the robust classifier demonstrates efficacy even when dealing with spectra of suboptimal quality. The resulting classifier is available as Supporting Information of the publication. Extensive documentation is provided, and the software is free and open to all users without a login requirement.