In oncology, the patient state is characterized by a whole spectrum of modalities, ranging
from radiology, histology, and genomics to electronic health records. Current artificial …

Identifying the relationship between imaging features and genetic variation (a term coined
“imaging genomics”) offers valuable insight into the pathogenesis of cancer, as well as …

Gliomas can be classified into five molecular groups based on the status of IDH mutation,
1p/19q codeletion, and TERT promoter mutation, whereas they need to be obtained by …

Editorial expression of concern: In the May 2021 edition, the American Journal of
Neuroradilogy published the article “MRI-Based Deep-Learning Method for Determining …

Background Diagnostic classification of diffuse gliomas now requires an assessment of
molecular features, often including IDH-mutation and 1p19q-codeletion status. Because …

OBJECTIVE IDH-mutant diffuse low-grade gliomas (dLGGs; WHO grade 2) are often
considered to have a more indolent course. In particular, in patients with 1p19q codeleted …

Purpose: We aimed to develop and validate a deep learning (DL) model to automatically
segment posterior fossa ependymoma (PF-EPN) and predict its molecular subtypes [Group …

Subtype classification is critical in the treatment of gliomas because different subtypes lead
to different treatment options and postoperative care. Although many radiological-or …

Purpose Glioma is the most common primary brain tumor, with varying degrees of
aggressiveness and prognosis. Accurate glioma classification is very important for treatment …

Abstract In recent years, Machine Learning (ML), a key component of artificial intelligence
(AI), has become increasingly popular in data analysis and processing. ML is now widely …