The IDH status has been reported as major prognostic factors for glioma behavior. Thus, noninvasively detecting molecular subtypes before surgery is important for predicting the outcome and choosing therapy. In this study, using machine-learning algorithms, the accurate prediction of IDH subtype was achieved for diffuse gliomas via noninvasive MR imaging, including ADC values and tumor morphologic features, and it is worth mentioning ADC measurements applied are available in clinical workstations. Furthermore, to our knowledge, no previous attempts have been made to use different machine-learning methods to build a suitable model to predict the IDH status for WHO grade II-IV gliomas.
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