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| Home > Articles > Published articles > Machine Learning Analysis of Single-Voxel Proton MR Spectroscopy for Differentiating Solitary Fibrous Tumors and Meningiomas |
(Universitat Autònoma de Barcelona. Departament de Bioquímica i de Biologia Molecular) (Institut d'Investigació Biomèdica de Bellvitge) (Institut d'Investigació Biomèdica de Bellvitge) (Universitat Autònoma de Barcelona. Departament de Bioquímica i de Biologia Molecular) (Universitat Autònoma de Barcelona. Departament de Bioquímica i de Biologia Molecular)| Date: | 2025 |
| Description: | 13 pàg. |
| 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. |
| Grants: | Instituto de Salud Carlos III PI20/00064 Instituto de Salud Carlos III PI20/00360 Ministerio de Economía y Competitividad SAF2014-52332-R Ministerio de Sanidad y Consumo CB06/01/0010 |
| Note: | Altres ajuts: acords transformatius de la UAB |
| Note: | Altres ajuts: Generalitat de Catalunya, Xartecsalut (2021 XARDI 00021) |
| Rights: | Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.  |
| Language: | Anglès |
| Document: | Article ; recerca ; Versió publicada |
| Subject: | Machine Learning ; Meningioma ; Solitary Fibrous Tumors ; Spectroscopy |
| Published in: | NMR in biomedicine, Vol. 38 Núm. 5 (May 2025) , ISSN 1099-1492 |
| Related work: | Tóth, Lili Fanni; Arus, Carles; Majos, Carles; Pons-Escoda, Albert; Julià-Sapé, Margarida, 2025, "Meningioma-SFT-Classifier (Software)", CORA.Repositori de Dades de Recerca, V1 https://doi.org/10.34810/data2109 |
