Yin Yajun, Lu Jiawei, Tong Jichun, Cheng Youshuang, Zhang Ke
Department of Cardiothoracic Surgery, Changzhou Second People's Hospital, The Affiliated Hospital of Nanjing Medical University, Changzhou, Jiangsu Province, China.
Department of Cardiothoracic Surgery, Affiliated Ninth People's Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Front Genet. 2023 Feb 21;14:1142795. doi: 10.3389/fgene.2023.1142795. eCollection 2023.
Lung adenocarcinoma is one of the most common cancers in the world, and accurate diagnosis of lung nodules is an important factor in reducing its mortality. In the diagnosis of pulmonary nodules, artificial intelligence (AI) assisted diagnosis technology has been rapidly developed, so testing its effectiveness is conducive to promoting its important role in clinical practice. This paper introduces the background of early lung adenocarcinoma and lung nodule AI medical imaging, and then makes academic research on early lung adenocarcinoma and AI medical imaging, and finally summarizes the biological information. In the experimental part, the relationship analysis of 4 driver genes in group X and group Y showed that there were more abnormal invasive lung adenocarcinoma genes, and the maximum uptake value and uptake function of metabolic value were also higher. However, there was no significant correlation between mutations in the four driver genes and metabolic values, and the average accuracy of AI-based medical images was 3.88% higher than that of traditional images.
肺腺癌是世界上最常见的癌症之一，准确诊断肺结节是降低其死亡率的重要因素。在肺结节的诊断中，人工智能（AI）辅助诊断技术得到了快速发展，因此测试其有效性有助于促进其在临床实践中的重要作用。本文介绍了早期肺腺癌和肺结节AI医学成像的背景，然后对早期肺腺癌和AI医学成像进行了学术研究，最后总结了生物学信息。在实验部分，对X组和Y组中4个驱动基因的关系分析表明，侵袭性肺腺癌基因异常较多，代谢值的最大摄取值和摄取功能也较高。然而，这4个驱动基因的突变与代谢值之间没有显著相关性，基于AI的医学图像的平均准确率比传统图像高3.88%。
