Yuan Lili, An Lin, Zhu Yandong, Duan Chongling, Kong Weixiang, Jiang Pei, Yu Qing-Qing
Jining NO.1 People's Hospital, Shandong First Medical University, Jining, People's Republic of China.
Translational Pharmaceutical Laboratory, Jining NO.1 People's Hospital, Shandong First Medical University, Jining, People's Republic of China.
Cancer Manag Res. 2024 Apr 24;16:361-375. doi: 10.2147/CMAR.S451871. eCollection 2024.
As a disease with high morbidity and high mortality, lung cancer has seriously harmed people's health. Therefore, early diagnosis and treatment are more important. PET/CT is usually used to obtain the early diagnosis, staging, and curative effect evaluation of tumors, especially lung cancer, due to the heterogeneity of tumors and the differences in artificial image interpretation and other reasons, it also fails to entirely reflect the real situation of tumors. Artificial intelligence (AI) has been applied to all aspects of life. Machine learning (ML) is one of the important ways to realize AI. With the help of the ML method used by PET/CT imaging technology, there are many studies in the diagnosis and treatment of lung cancer. This article summarizes the application progress of ML based on PET/CT in lung cancer, in order to better serve the clinical. In this study, we searched PubMed using machine learning, lung cancer, and PET/CT as keywords to find relevant articles in the past 5 years or more. We found that PET/CT-based ML approaches have achieved significant results in the detection, delineation, classification of pathology, molecular subtyping, staging, and response assessment with survival and prognosis of lung cancer, which can provide clinicians a powerful tool to support and assist in critical daily clinical decisions. However, ML has some shortcomings such as slightly poor repeatability and reliability.
作为一种发病率和死亡率都很高的疾病，肺癌严重危害了人们的健康。因此，早期诊断和治疗更为重要。PET/CT通常用于肿瘤尤其是肺癌的早期诊断、分期及疗效评估，但由于肿瘤的异质性以及人工图像解读存在差异等原因，它也无法完全反映肿瘤的真实情况。人工智能（AI）已应用于生活的方方面面。机器学习（ML）是实现AI的重要途径之一。借助PET/CT成像技术所采用的ML方法，在肺癌的诊断和治疗方面已有诸多研究。本文总结基于PET/CT的ML在肺癌中的应用进展，以便更好地服务于临床。在本研究中，我们以机器学习、肺癌和PET/CT作为关键词检索PubMed，查找过去5年及更久以前的相关文章。我们发现，基于PET/CT的ML方法在肺癌的检测、轮廓描绘、病理分类、分子亚型分析、分期以及生存和预后的反应评估方面均取得了显著成果，可为临床医生提供一个强大的工具，以支持和协助他们做出关键的日常临床决策。然而，ML存在一些缺点，如重复性和可靠性稍差。
