Machine learning for differentiating lung squamous cell cancer from adenocarcinoma using Clinical-Metabolic characteristics and 18F-FDG PET/CT radiomics

• Published in: PloS one Vol. 19; no. 4; p. e0300170
• Main Authors: Zhang, Yalin, Liu, Huiling, Chang, Cheng, Yin, Yong, Wang, Ruozheng
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 03.04.2024; Public Library of Science (PLoS)
• Subjects: Adenocarcinoma; Adenocarcinoma - diagnostic imaging; Algorithms; Analysis; Biology and Life Sciences; Carcinoma, Non-Small-Cell Lung - diagnostic imaging; Carcinoma, Squamous Cell - diagnostic imaging; Care and treatment; Cell differentiation; Computer and Information Sciences; Diagnosis; Epithelial Cells; Fluorodeoxyglucose F18; Humans; Lung; Lung cancer; Lung cancer, Non-small cell; Lung Neoplasms - diagnostic imaging; Machine Learning; Medical imaging equipment; Medicine and Health Sciences; PET imaging; Physical Sciences; Positron Emission Tomography Computed Tomography; Radiomics; Research and Analysis Methods; Retrospective Studies; Squamous cell carcinoma; China

Noninvasive differentiation between the squamous cell carcinoma (SCC) and adenocarcinoma (ADC) subtypes of non-small cell lung cancer (NSCLC) could benefit patients who are unsuitable for invasive diagnostic procedures. Therefore, this study evaluates the predictive performance of a PET/CT-based rad...