RP-Rs-fMRIomics as a Novel Imaging Analysis Strategy to Empower Diagnosis of Brain Gliomas

SIMPLE SUMMARY: Resting-state functional magnetic resonance imaging (rs-fMRI), a popular neuroimaging technique, can provide rich information about functional processes in the brain with a large array of imaging parameters and is suitable for exploring the pathophysiological essence of gliomas. In this study, by applying omics analysis strategy to rs-fMRI with exhaustive regional parameters, we proposed a novel approach, named Regional Parameter of Resting-state fMRI-omics (RP-Rs-fMRIomics), and further evaluated the diagnosis performance of the method on brain gliomas. We found that the RP-Rs-fMRIomics, featuring entire investigation and high interpretability, presented superior performance in prediction of tumor grade, IDH genotype and prognosis of brain gliomas. This RP-Rs-fMRIomics not only contributed a new imaging method for brain glioma research, but also expanded the clinical application of rs-fMRI. ABSTRACT: Rs-fMRI can provide rich information about functional processes in the brain with a large array of imaging parameters and is also suitable for investigating the biological processes in cerebral gliomas. We aimed to propose an imaging analysis method of RP-Rs-fMRIomics by adopting omics analysis on rs-fMRI with exhaustive regional parameters and subsequently estimating its feasibility on the prediction diagnosis of gliomas. In this retrospective study, preoperative rs-fMRI data were acquired from patients confirmed with diffuse gliomas (n = 176). A total of 420 features were extracted through measuring 14 regional parameters of rs-fMRI as much as available currently in 10 specific narrow frequency bins and three parts of gliomas. With a randomly split training and testing dataset (ratio 7:3), four classifiers were implemented to construct and optimize RP-Rs-fMRIomics models for predicting glioma grade, IDH status and Karnofsky Performance Status scores. The RP-Rs-fMRIomics models (AUROC 0.988, 0.905, 0.801) were superior to the corresponding traditional single rs-fMRI index (AUROC 0.803, 0.731, 0.632) in predicting glioma grade, IDH and survival. The RP-Rs-fMRIomics analysis, featuring high interpretability, was competitive for prediction of glioma grading, IDH genotype and prognosis. The method expanded the clinical application of rs-fMRI and also contributed a new imaging analysis for brain tumor research.