Machine-learning in grading of gliomas based on multi-parametric magnetic resonance imaging at 3T. (1st August 2018)

• Record Type: Journal Article
• Authors: Citak-Er, Fusun; Firat, Zeynep; Kovanlikaya, Ilhami; Ture, Ugur; Ozturk-Isik, Esin
• Is Part Of: Computers in biology and medicine. Volume 99(2018)
• Page Start: 154
• Page End: 160
• Publication Date: 2018-08-01
• Subjects: Machine learning -- Multi-parametric magnetic resonance imaging -- Gliomas; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2018.06.009
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Objective: The objective of this study was to assess the contribution of multi-parametric (mp) magnetic resonance imaging (MRI) quantitative features in the machine learning-based grading of gliomas with a multi-region-of-interests approach. Materials and methods: Forty-three patients who were newly diagnosed as having a glioma were included in this study. The patients were scanned prior to any therapy using a standard brain tumor magnetic resonance (MR) imaging protocol that included T1 and T2-weighted, diffusion-weighted, diffusion tensor, MR perfusion and MR spectroscopic imaging. Three different regions-of-interest were drawn for each subject to encompass tumor, immediate tumor periphery, and distant peritumoral edema/normal. The normalized mp-MRI features were used to build machine-learning models for differentiating low-grade gliomas (WHO grades I and II) from high grades (WHO grades III and IV). In order to assess the contribution of regional mp-MRI quantitative features to the classification models, a support vector machine-based recursive feature elimination method was applied prior to classification. Results: A machine-learning model based on support vector machine algorithm with linear kernel achieved an accuracy of 93.0%, a specificity of 86.7%, and a sensitivity of 96.4% for the grading of gliomas using ten-fold cross validation based on the proposed subset of the mp-MRI features. Conclusion: In this study, machine-learning based on multiregional and multi-parametric MRI data has proven to be an important tool in grading glial tumors accurately even in this limited patient population. Future studies are needed to investigate the use of machine learning algorithms for brain tumor classification in a larger patient cohort. Highlights: Machine learning-based grading of gliomas with a multi-region-of-interests approach. An accuracy of 93.0% with a specificity of 86.7% was achieved. The solid tumor, tumor periphery and peritumoral edema/normal regions were defined. mp-MRI: rCBV, ADC, Cho/NAA, FA, NAA (tumor); FA, MTT (periphery); Cho/NAA (edema).