Radiomics is the extraction of predefined mathematic features from medical images for
predicting variables of clinical interest. Recent research has demonstrated that radiomics …

The therapies targeting mutations of driver genes in cancer have advanced into clinical trials
for a variety of tumors. In glioblastoma (GBM), epidermal growth factor receptor (EGFR) is …

Computer vision methods utilizing transfer learning can offer a promising approach for
automated eye cancer diagnosis with three-dimensional dosimetry imaging. The latest …

Purpose We sought to develop and validate machine learning (ML) models for predicting
tumor grade and prognosis using 2-[18F] fluoro-2-deoxy-D-glucose ([18F] FDG) positron …

Single locus (Mendelian) diseases are a leading cause of childhood hospitalization,
intensive care unit (ICU) admission, mortality, and healthcare cost. Rapid genome …

Cancer treatment requires precise tumorspecific targeting at specific sites that allows for
highresolution diagnostic imaging and longterm patienttailorable cancer therapy; while …

Cancer is a major health concern due to its high incidence and mortality rates. Advances in
cancer research, particularly in artificial intelligence (AI) and deep learning, have shown …

Positron emission tomography (PET) has been widely used in nuclear medicine to diagnose
cancer. PET images suffer from degradation because of the scanner's physical limitations …

Simple Summary 18F-Fluoroethyl-L tyrosine radiomics are useful in the differentiation of true
progression from treatment-related changes in patients with glioblastoma, offering relevant …

Purpose This review offers insight into AI's current and future contributions to medical image
analysis. The article highlights the challenges associated with manual image interpretation …