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 …

The number of publications on deep learning for cancer diagnostics is rapidly increasing,
and systems are frequently claimed to perform comparable with or better than clinicians …

Background Glioblastoma multiforme (GBM) is the most common cancer of the central
nervous system, while Parkinson's disease (PD) is a degenerative neurological condition …

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 …

The accurate prediction of isocitrate dehydrogenase (IDH) mutation and glioma
segmentation are important tasks for computer-aided diagnosis using preoperative …

Motivation Brain or central nervous system cancer is the tenth leading cause of death in men
and women. Even though brain tumour is not considered as the primary cause of mortality …

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 …

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

Radiogenomics, focusing on the relationship between genomics and imaging phenotypes,
has been widely applied to address tumour heterogeneity and predict immune …

Low-grade glioma (LGG) is a highly aggressive disease in the skull. On the other hand,
anoikis, a specific form of cell death induced by the loss of cell contact with the extracellular …