The goal of oncology is to provide the longest possible survival outcomes with the
therapeutics that are currently available without sacrificing patients' quality of life. In lung …

Simple Summary In this comprehensive review, we aimed to summarize the advances made
by artificial intelligence in the field of lung cancer screening, diagnosis, and management …

Background Limited data was available for rapid and accurate detection of COVID-19 using
CT-based machine learning model. This study aimed to investigate the value of chest CT …

Background: Artificial neural networks are statistical methods that mimic complex neural
connections, simulating the learning dynamics of the human brain. They play a fundamental …

Small cell lung cancer (SCLC) is a highly aggressive malignancy with exceptionally poor
prognosis, comprising approximately 15% of lung cancers. Emerging knowledge of the …

Radiomics has become a research field that involves the process of converting standard
nursing images into quantitative image data, which can be combined with other data …

Purposes and Objectives The aim of this study was to predict the progression-free survival
(PFS) in patients with small cell lung cancer (SCLC) by radiomic signature from the contrast …

Rationale and Objectives To develop an end-to-end deep learning (DL) model for non-
invasively predicting non-small cell lung cancer (NSCLC) pathological subtypes based on …

Introduction Understanding of the patient-perceived symptom burden of small cell lung
cancer (SCLC) is limited. The objective of this study was to explore patients' experiences …

To enhance the specificity of Magnetic resonance imaging (MRI) based cirrhosis stage-
diagnosis, a method of diagnosis incorporating the scan image texture discovery with …