PET/CT radiomics and deep learning in the diagnosis of benign and malignant pulmonary nodules: progress and challenges

作者：Yan Sun, Xinyu Ge, Rong Niu, Jianxiong Gao, Yunmei Shi, Xiaoliang Shao, Xiaoliang Shao, Yuetao Wang, Xiaonan Shao, Xiaonan Shao · 发表于：Frontiers in Oncology · 年份：2024 · DOI：10.3389/fonc.2024.1491762 · 被引用次数：19 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Lung Cancer Diagnosis and Treatment、Advanced X-ray and CT Imaging

Lung cancer is currently the leading cause of cancer-related deaths, and early diagnosis and screening can significantly reduce its mortality rate. Since some early-stage lung cancers lack obvious clinical symptoms and only present as pulmonary nodules (PNs) in imaging examinations, accurately determining the benign or malignant nature of PNs is crucial for improving patient survival rates. 18 F-FDG PET/CT is important in diagnosing PNs, but its specificity needs improvement. Radiomics can provide information beyond traditional visual assessment, overcoming its limitations by extracting high-throughput quantitative features from medical images. Radiomics features based on 18 F-FDG PET/CT and deep learning methods have shown great potential in the noninvasive diagnosis of PNs. This paper reviews the latest advancements in these methods and discusses their contributions to improving diagnostic accuracy and the challenges they face.