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Gliomas are neurologically devastating tumors with generally poor outcomes. Traditionally, survival prediction in glioma is studied from clinical features using statistical approaches. With the rapid development of artificial intelligence approaches encompassing machine learning and deep learning, there has been a keen interest among researchers to apply these methods to survival prediction in glioma allowing for integrated processes that encompass pathology, histology, molecular, imaging, and clinical features. This chapter provides an overview of the emerging computational approaches that have the potential to revolutionize survival prediction in glioma. Machine learning and deep learning techniques, including support vector machine, random forest, convolutional neural network, and radiomics, are discussed.
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