Abstract Machine learning is frequently being leveraged to tackle problems in the health
sector including utilization for clinical decision-support. Its use has historically been focused …

Radiomic analysis offers a powerful tool for the extraction of clinically relevant information
from radiologic imaging. Radiomics can be used to predict patient outcome through …

Preoperative MRI is one of the most important clinical results for the diagnosis and treatment
of glioma patients. The objective of this study was to construct a stable and validatable …

Glioblastoma is the most common aggressive adult brain tumor. Numerous studies have
reported results from either private institutional data or publicly available datasets. However …

The natural history and treatment landscape of primary brain tumours are complicated by the
varied tumour behaviour of primary or secondary gliomas (high-grade transformation of low …

From diagnostics to prognosis to response prediction, new applications for radiomics are
rapidly being developed. One of the fastest evolving branches involves linking imaging …

Purpose The aim of this systematic review was to analyse literature on artificial intelligence
(AI) and radiomics, including all medical imaging modalities, for oncological and non …

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

Tumor microenvironments are the result of cellular alterations in cancer that support
unrestricted growth and proliferation and result in further modifications in cell behavior …

No predictive biomarkers can robustly identify patients with non–small cell lung cancer
(NSCLC) who will benefit from immune checkpoint inhibitor (ICI) therapies. Here, in a …