Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered
data with nuanced decision making. Cancer offers a unique context for medical decisions …

Radiomics describes a broad set of computational methods that extract quantitative features
from radiographic images. The resulting features can be used to inform imaging diagnosis …

BACKGROUND AND PURPOSE: The World Health Organization has recently placed new
emphasis on the integration of genetic information for gliomas. While tissue sampling …

Background Glioma prognosis depends on isocitrate dehydrogenase (IDH) mutation status.
We aimed to predict the IDH status of gliomas from preoperative MR images using a fully …

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 …

Sampling restrictions have hindered the comprehensive study of invasive non-enhancing
(NE) high-grade glioma (HGG) cell populations driving tumor progression. Here, we present …

Due to the exponential growth of computational algorithms, artificial intelligence (AI)
methods are poised to improve the precision of diagnostic and therapeutic methods in …

Purpose To investigate whether radiomic features at MRI improve survival prediction in
patients with glioblastoma multiforme (GBM) when they are integrated with clinical and …

The central role of MRI in neuro-oncology is undisputed. The technique is used, both in
clinical practice and in clinical trials, to diagnose and monitor disease activity, support …

The application of artificial intelligence (AI) is accelerating the paradigm shift towards patient-
tailored brain tumor management, achieving optimal onco-functional balance for each …