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

Personalized treatment strategies for cancer frequently rely on the detection of genetic
alterations which are determined by molecular biology assays. Historically, these processes …

Rationale and Objectives Recent advancements in artificial intelligence (AI) render a
substantial promise for epidermal growth factor receptor (EGFR) mutation status prediction …

Radiomics is a technique that extracts quantitative features from medical images using data‐
characterization algorithms. Radiomic features can be used to identify tissue characteristics …

Advancements in the development of computer-aided decision (CAD) systems for clinical
routines provide unquestionable benefits in connecting human medical expertise with …

Purpose Lung cancer has significant genetic and phenotypic heterogeneity, leading to poor
prognosis. Radiomic features have emerged as promising predictors of the tumor …

Simple Summary Actional mutations and PD-L1 expression are of paramount importance for
the precision treatment of lung cancer. Radiogenomics is a promising field that integrated …

Radiogenomic and radiotranscriptomic studies have the potential to pave the way for a
holistic decision support system built on genomics, transcriptomics, radiomics, deep features …

Lung cancer has the highest mortality rate among all cancers in the world. Hence, early
diagnosis and personalized treatment plans are crucial to improving its 5-year survival rate …

The TP53 gene is renowned as a tumor suppressor, playing a pivotal role in overseeing the
cell cycle, apoptosis, and maintaining genomic stability. Dysregulation of p53 often …