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

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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.
Funding Information
  • National Key Technology (R&D) Program of the Ministry of Science and Technology (2018YFA0701703, 2022ZD0211800, XYKF202101, 2020AAA0109505, 82127806)
  • ational Science and Technology Innovation 2030−Major program of “Brain Science and Brain-Like Research” (2018YFA0701703, 2022ZD0211800, XYKF202101, 2020AAA0109505, 82127806)
  • Xuzhou Medical University Open Fund Project (2018YFA0701703, 2022ZD0211800, XYKF202101, 2020AAA0109505, 82127806)
  • National Key Research and Development Program of China (2018YFA0701703, 2022ZD0211800, XYKF202101, 2020AAA0109505, 82127806)
  • National Natural Science Foundation of China (2018YFA0701703, 2022ZD0211800, XYKF202101, 2020AAA0109505, 82127806)

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