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 …

BACKGROUND Molecular characterization of glioma has implications for prognosis,
treatment planning, and prediction of treatment response. Current histopathology is limited …

Objective To develop prognostic models for survival (alive or deceased status) prediction of
COVID-19 patients using clinical data (demographics and history, laboratory tests, visual …

Objective The aim of this study was to identify the most important features and assess their
discriminative power in the classification of the subtypes of NSCLC. Methods This study …

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 …

Purpose The aim of this study was to develop radiomics–based machine learning models
based on extracted radiomic features and clinical information to predict the risk of death …

Purpose Considerable progress has been made in the assessment and management of non-
small cell lung cancer (NSCLC) patients based on mutation status in the epidermal growth …

Purpose To identify optimal classification methods for computed tomography (CT) radiomics-
based preoperative prediction of clear cell renal cell carcinoma (ccRCC) grade. Materials …

Aims Despite the promising results achieved by radiomics prognostic models for various
clinical applications, multiple challenges still need to be addressed. The two main limitations …

Introduction Being the most common primary brain tumor, glioblastoma presents as an
extremely challenging malignancy to treat with dismal outcomes despite treatment. Varying …