Lung cancer is the second most common cancer and the leading cause of cancer-related
death worldwide. Molecular imaging using [18 F] fluorodeoxyglucose Positron Emission …

Radiological imaging is an integral component of cancer care, including diagnosis, staging,
and treatment response monitoring. It contains rich information about tumor phenotypes that …

Radiomics refers to the high-throughput extraction of quantitative features from radiological
scans and is widely used to search for imaging biomarkers for the prediction of clinical …

Hepatocellular carcinoma (HCC) is one of the most lethal neoplasms and has a 5-year
survival rate of only 18% in patients with metastatic diseases. Epigenetic modifiers and …

Survival models exist to study relationships between biomarkers and treatment effects. Deep
learning-powered survival models supersede the classical Cox proportional hazards …

Lymph node metastases are commonly observed in diverse malignancies where they
promote cancer progression and poor outcomes, although the molecular basis is …

Summary Background Pre-treatment FDG-PET/CT scans were analyzed with machine
learning to predict progression of lung malignancies and overall survival (OS). Methods A …

This review provides an overview of the application of artificial intelligence (AI) in radiation
therapy (RT) from a radiation oncologist's perspective. Over the years, advances in …

Introduction The treatment response to neoadjuvant immunochemotherapy varies among
patients with potentially resectable non-small cell lung cancers (NSCLC) and may have …

The aim of this work is to investigate the applicability of radiomic features alone and in
combination with clinical information for the prediction of renal cell carcinoma (RCC) …