OS08.4.A Retrospective analysis of in vivo1H-magnetic resonance spectroscopy based on a machine learning approach enables reliable prediction of IDH mutation in patients with glioma. (9th September 2021)

• Record Type: Journal Article
• Authors: Bumes, E; Fellner, C; Lenz, S; Linker, R; Weis, S; Wendl, C; Wimmer, S; Hau, P; Gronwald, W; Hutterer, M
• Is Part Of: Neuro-oncology. Volume 23: Supplement 2 (2021)
• Page Start: ii11
• Page End: ii11
• Publication Date: 2021-09-09
• Subjects: Brain Neoplasms -- Periodicals; Brain -- Tumors -- Periodicals; Brain -- Cancer -- Periodicals; Nervous system -- Cancer -- Periodicals; 616.99481
• DOI: 10.1093/neuonc/noab180.034
• Languages: English
• ISSNs: 1522-8517

Abstract

Abstract: BACKGROUND: Mutation of isocitrate dehydrogenase (IDH ) is not only an important landmark in the development of low-grade gliomas, but also has prognostic significance and is a potential therapeutic target. There is a high need to determinate IDH mutation status at diagnosis and during the course of therapy in a non-invasive and reliable manner. We established a machine learning approach based on a support vector machine to detect IDH mutation status in in vivo standard 1 H-magnetic resonance spectroscopy ( 1 H-MRS) at 3T with an accuracy of 88.2%, a sensitivity of 95.5% (95% CI, 77.2–99.9%), and a specificity of 75% (95% CI, 42.85–94.5%) in a prospective monocentric clinical trial. Here, the same method is applied in a retrospective cohort at 1.5T and tested for transferability. MATERIAL AND METHODS: Validation cohort. The validation cohort comprised 100 patients with glioma for which standard in vivo 1 H-MRS spectra had been acquired between 2002 and 2007. Standard single voxel spectroscopy had been measured at 1.5T using a PRESS sequence with a TR of 1500ms and a TE of 30ms. One sample had to be excluded due to non-malignant histology and for 15 samples the IDH mutation status was not available. Therefore, the validation cohort comprised 84 samples, of which 35 were bearing an IDH mutation in immunohistochemistry (sequencing for confirmation is outstanding). Machine learning . To transfer our method to an independent validation cohort our previously established machine learning approach was first trained on all samples of the 3T group. The trained algorithm was then applied to the data of the validation cohort. Here, among other factors the different field strengths, with which the spectra were acquired (3T vs. 1.5T) had to be considered. RESULTS: 27 samples of the validation cohort had to be excluded due to poor spectra quality. Our approach correctly detected IDH mutation status in 47 of 62 patients (75.8%), although the technical conditions were significantly different from our published prospective cohort. 17 of 30 patients bearing an IDH mutation were correctly identified, while 30 of 32 wild type patients were determined successfully. CONCLUSION: Our approach to detect IDH mutation status has promising application in an unselected retrospective cohort, demonstrating transferability across different technical conditions. Further investigations to improve our technique and an advanced neuropathological processing of the samples are planned.