Tumor histology is an important predictor of therapeutic response and outcomes in lung
cancer. Tissue sampling for pathologist review is the most reliable method for histology …

Adenocarcinoma (AC) and squamous cell carcinoma (SCC) are frequent reported cases of
non-small cell lung cancer (NSCLC), responsible for a large fraction of cancer deaths …

Deep learning (DL) is a breakthrough technology for medical imaging with high sample size
requirements and interpretability issues. Using a pretrained DL model through a radiomics …

Background Non-small-cell lung cancer (NSCLC) patients often demonstrate varying clinical
courses and outcomes, even within the same tumor stage. This study explores deep …

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 …

Lung cancer has a high incidence and mortality rate. Early detection and diagnosis of lung
cancers is best achieved with low-dose computed tomography (CT). Classical radiomics …

Rationale and Objectives Histological subtypes of lung cancers are critical for clinical
treatment decision. In this study, we attempt to use 3D deep learning and radiomics methods …

Purpose: Tumors are continuously evolving biological systems, and medical imaging is
uniquely positioned to monitor changes throughout treatment. Although qualitatively tracking …

Introduction Lung cancer ranks second in new cancer cases and first in cancer-related
deaths worldwide. Precision medicine is working on altering treatment approaches and …

Background Radiomics can quantify tumor phenotypic characteristics non-invasively by
applying feature algorithms to medical imaging data. In this study of lung cancer patients, we …