Precise safe surgical resection and precisely directed radiation are important in clinical practice for low-grade gliomas (LGGs) patients in order to minimize the neurological deficit and radiation toxicity, respectively. Clinicians find difficulty in defining the border between the non-enhancing tumor and edema components. In this study, machine learning-based models were developed in order to distinguish non-enhancing tumors from vasogenic edema using quantitative perfusion parameters obtained using dynamic-contrast-enhanced MRI. The proposed approach may help in assisting radiologists, by defining precise tumor boundaries and hence, results in improving patients’ quality of life and overall survival.
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