MR Based Prediction of Molecular Pathology in Glioma Using Artificial Intelligence

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Non-invasive and precise prediction for molecular biomarkers such as 1p/19q co-deletion, MGMT methylation, IDH and TERTp mutations is challenging. With the development of artificial intelligence, much more potential lies in the preoperative conventional/advanced MR imaging (T1 weighted imaging, T2 w...

Non-invasive and precise prediction for molecular biomarkers such as 1p/19q co-deletion, MGMT methylation, IDH and TERTp mutations is challenging. With the development of artificial intelligence, much more potential lies in the preoperative conventional/advanced MR imaging (T1 weighted imaging, T2 weighted imaging, FLAIR, contrast-enhanced T1 weighted imaging, diffusion-weighted imaging, and perfusion imaging) could be excavated to aid prediction of molecular pathology of gliomas. The creation of a registry for primary glioma with detailed molecular pathology, radiological data and with sufficient sample size for deep learning (>1000) provide considerable opportunities for personalized prediction of molecular pathology with non-invasiveness and precision.

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