Sun Yan, Ge Xinyu, Niu Rong, Gao Jianxiong, Shi Yunmei, Shao Xiaoliang, Wang Yuetao, Shao Xiaonan
Department of Nuclear Medicine, the Third Affiliated Hospital of Soochow University, Changzhou, China.
Institute of Clinical Translation of Nuclear Medicine and Molecular Imaging, Soochow University, Changzhou, China.
Front Oncol. 2024 Nov 8;14:1491762. doi: 10.3389/fonc.2024.1491762. eCollection 2024.
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. 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 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.
肺癌是目前癌症相关死亡的主要原因，早期诊断和筛查可显著降低其死亡率。由于一些早期肺癌缺乏明显的临床症状，仅在影像学检查中表现为肺结节（PNs），准确判断PNs的良恶性对于提高患者生存率至关重要。F-FDG PET/CT在PNs的诊断中很重要，但其特异性有待提高。放射组学可以提供超越传统视觉评估的信息，通过从医学图像中提取高通量定量特征来克服其局限性。基于F-FDG PET/CT的放射组学特征和深度学习方法在PNs的无创诊断中显示出巨大潜力。本文综述了这些方法的最新进展，并讨论了它们对提高诊断准确性的贡献以及面临的挑战。
