Glioma is the most common primary intraparenchymal tumor of the brain and the 5-year
survival rate of high-grade glioma is poor. Magnetic resonance imaging (MRI) is essential for …

AIM To review how machine learning (ML) is applied to imaging biomarkers in neuro-
oncology, in particular for diagnosis, prognosis, and treatment response monitoring …

Glioma represents a dominant primary intracranial malignancy in the central nervous
system. Artificial intelligence that mainly includes machine learning, and deep learning …

BACKGROUND Molecular characterization of glioma has implications for prognosis,
treatment planning, and prediction of treatment response. Current histopathology is limited …

Context With the advent of new imaging modalities, radiologists are faced with handling
increasing volumes of data for diagnosis and treatment planning. The use of automated and …

The successful early diagnosis of brain tumors plays a major role in improving the treatment
outcomes and thus improving patient survival. Manually evaluating the numerous magnetic …

Simple Summary With the advancement of artificial intelligence, including machine learning,
the field of oncology has seen promising results in cancer detection and classification …

Importance Deep learning may be able to use patient magnetic resonance imaging (MRI)
data to aid in brain tumor classification and diagnosis. Objective To develop and clinically …

This review delves into the most recent advancements in applying artificial intelligence (AI)
within neuro-oncology, specifically emphasizing work on gliomas, a class of brain tumors …

Convolutional neural networks (CNNs) constitute a widely used deep learning approach that
has frequently been applied to the problem of brain tumor diagnosis. Such techniques still …