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

Radiomics is the method of choice for investigating the association between cancer imaging
phenotype, cancer genotype and clinical outcome prediction in the era of precision …

Introduction Medical image analysis is of tremendous importance in serving clinical
diagnosis, treatment planning, as well as prognosis assessment. However, the image …

Purpose: Using standard-of-care CT images obtained from patients with a diagnosis of non–
small cell lung cancer (NSCLC), we defined radiomics signatures predicting the sensitivity of …

Immunotherapy offers the potential for durable clinical benefit but calls into question the
association between tumor size and outcome that currently forms the basis for imaging …

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

The field of radiomics is at the forefront of personalized medicine. However, there is concern
that high variation in imaging parameters will impact robustness of radiomic features and …

Background Identification of epidermal growth factor receptor (EGFR) mutation types is
crucial before tyrosine kinase inhibitors (TKIs) treatment. Radiomics is a new strategy to …

With the development of genome sequencing, the role of molecular targeted therapy in
cancer is becoming increasingly important. However, genetic testing remains expensive …

The objective of this systematic review was to analyze the current state of the art of imaging-
derived biomarkers predictive of genetic alterations and immunotherapy targets in lung …