This article is a comprehensive review of the basic background, technique, and clinical
applications of artificial intelligence (AI) and radiomics in the field of neuro-oncology. A …

Meningiomas are the most common primary intracranial tumors. The majority of
meningiomas are benign, but they can present different grades of dedifferentiation from …

Background: In recent decades, the application of machine learning technologies to medical
imaging has opened up new perspectives in neuro-oncology, in the so-called radiomics …

Objectives To establish a deep learning (DL) model for predicting tumor grades and
expression of pathologic markers of meningioma. Methods A total of 1192 meningioma …

Medical imaging plays an indispensable role in evaluating, predicting, and monitoring a
range of medical conditions. Radiomics, a specialized branch of medical imaging, utilizes …

Artificial intelligence (AI) is rapidly integrating into diagnostic methods across many
branches of medicine. Significant progress has been made in tumor assessment using AI …

Anatomical cross‐sectional imaging methods such as contrast‐enhanced MRI and CT are
the standard for the delineation, treatment planning, and follow‐up of patients with …

Objectives This study investigated whether radiomic features can improve the prediction
accuracy for tumor recurrence over clinicopathological features and if these features can be …

Simple Summary Machine learning in radiology of the central nervous system has seen
many interesting publications in the past few years. Since the focus has largely been on …

Introduction: Radiomics now has significant momentum in the era of precision medicine.
Glioma is one of the pathologies that has been extensively evaluated by radiomics …