Predicting post-surgical functional status in high-grade glioma with resting state fMRI and machine learning

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High-grade gliomas (HGG) represent 60–70% of brain tumor cases, with a median survival rate of only 14 months post-diagnosis.
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Surgical resection of HGG is essential but poses risks of functional impairment, impacting patients' quality of life and overall outcomes.
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Resting state fMRI (RS-fMRI) offers advantages over task fMRI by assessing brain connectivity without requiring patient engagement in tasks.
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Machine learning models were developed to predict postoperative functional outcomes in HGG patients using RS-fMRI data and clinical characteristics.
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The study demonstrated that machine learning can accurately forecast functional outcomes for HGG patients at initial diagnosis, aiding surgical planning.

Journal Of Neuro-Oncology

by Patrick H. Luckett, Michael O. Olufawo, Ki Yun Park, Bidhan Lamichhane, Donna Dierker, Gabriel Trevino Verastegui, John J. Lee, Peter Yang, Albert Kim, Omar H. Butt, Milan G. Chheda, Abraham Z. Snyder, Joshua S. Shimony, Eric C. Leuthardt
May 24, 2024

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