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人工智能图像特征与肺腺癌表皮生长因子受体基因突变之间的关系

Relationship between the image characteristics of artificial intelligence and EGFR gene mutation in lung adenocarcinoma.

作者信息

Zhou Guoping, Xu Shuhua, Liu Xiaoli, Ge Jingjun, He Qiyu, Cao Weikang, Ding Junning, Kai Xinghua

机构信息

Department of Cardiothoracic Surgery, Dongtai Hospital of Traditional Chinese Medicine, Dongtai, China.

Department of Pathology, Dongtai Hospital of Traditional Chinese Medicine, Dongtai, China.

出版信息

Front Genet. 2023 Jan 4;13:1090180. doi: 10.3389/fgene.2022.1090180. eCollection 2022.

Abstract

Lung Adenocarcinoma (LUAD) is a kind of Lung Cancer (LCA) with high incidence rate, which is very harmful to human body. It is hidden in the human body and is not easy to be discovered, so it brings great inconvenience to the treatment of LUAD. Artificial Intelligence (AI) technology provides technical support for the diagnosis and treatment of LUAD and has great application space in intelligent medicine. In this paper, 164 patients with primary LUAD who underwent surgery in Hospital A from January 2020 to December 2021 were selected as the study subjects, and the correlation between the imaging characteristics of LUAD and Epidermal Growth Factor Receptor (EGFR) gene mutation was analyzed. Finally, the conclusion was drawn. In terms of the study on the correlation between EGFR mutation of LUAD and the imaging characteristics of Computed Tomography (CT), it was concluded that there were significant differences between the patient's sex, smoking history, pulmonary nodule morphology and the EGFR gene, and there was no significant difference between the patient's tumor size and EGFR gene; in the study of the relationship between EGFR gene mutation and CT signs of LUAD lesions, it was found that there were significant differences between the symptoms of cavity sign, hair prick sign and chest depression sign and EGFR gene, but there was no significant difference between the symptoms of lobulation sign and EGFR gene; in the study of pathological subtype and EGFR gene mutation status of LUAD patients, it was concluded that the pathological subtype was mainly micropapillary. The mutation rate was 44.44%, which was the highest; in terms of CT manifestations of adjacent structures of lung cancer and the study of EGFR gene mutation status, it was found that there was a statistical difference between the tumor with vascular convergence sign and EGFR gene mutation, and pleural effusion, pericardial effusion, pleural thickening and other signs in tumor imaging were not significantly associated with EGFR gene mutation; in terms of the study of CT manifestations of adjacent structures of LCA and EGFR gene mutation status, it was concluded that pleural effusion, pericardial effusion, pleural thickening and other signs in tumor images were not significantly associated with EGFR gene mutation; in terms of analysis and cure of LUAD, it was concluded that the cure rate of patients was relatively high, and only a few people died of ineffective treatment. This paper provided a reference for the field of intelligent medicine and physical health.

摘要

肺腺癌(LUAD)是一种发病率较高的肺癌(LCA),对人体危害极大。它隐匿于人体内,不易被发现,这给LUAD的治疗带来了极大不便。人工智能(AI)技术为LUAD的诊断和治疗提供了技术支持,在智能医学领域具有广阔的应用空间。本文选取2020年1月至2021年12月在A医院接受手术的164例原发性LUAD患者作为研究对象,分析LUAD的影像学特征与表皮生长因子受体(EGFR)基因突变之间的相关性。最后得出结论。在关于LUAD的EGFR突变与计算机断层扫描(CT)影像学特征相关性的研究中,得出患者的性别、吸烟史、肺结节形态与EGFR基因之间存在显著差异,而患者的肿瘤大小与EGFR基因之间无显著差异;在EGFR基因突变与LUAD病变的CT征象关系的研究中,发现空洞征、毛刺征和胸廓凹陷征与EGFR基因之间存在显著差异,而分叶征与EGFR基因之间无显著差异;在LUAD患者的病理亚型与EGFR基因突变状态的研究中,得出病理亚型主要为微乳头型。突变率为44.44%,为最高;在肺癌相邻结构的CT表现及EGFR基因突变状态的研究中,发现具有血管集束征的肿瘤与EGFR基因突变之间存在统计学差异,肿瘤影像学中的胸腔积液、心包积液、胸膜增厚等征象与EGFR基因突变无显著相关性;在LCA相邻结构的CT表现及EGFR基因突变状态的研究中,得出肿瘤图像中的胸腔积液、心包积液、胸膜增厚等征象与EGFR基因突变无显著相关性;在LUAD的分析与治疗方面,得出患者的治愈率相对较高,仅有少数人因治疗无效死亡。本文为智能医学和身体健康领域提供了参考。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1cfe/9846484/3284e3115c4c/fgene-13-1090180-g001.jpg

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