人工智能肺部结节辅助诊疗系统预测肺结节的良恶性及浸润情况  

Artificial intelligence-assisted diagnosis and treatment system in prediction of benign or malignant lung nodules and infiltration degree

作　　者：曹孟昆 姜杰[1] 朱晓雷 李宁[1] 王剑翁[1] 林俊峰[1] 刘鸿鸣[1] 邓城庆 蔡夕倩 耿国军[1] CAO Mengkun;JIANG Jie;ZHU Xiaolei;LI Ning;WANG Jianweng;LIN Junfeng;LIU Hongming;DENG Chengqing;CAI Xiqian;GENG Guojun(Department of Thoracic Surgery,The First Affiliated Hospital of Xiamen University,Xiamen,361000,Fujian,P.R.China;School of Basic Medicine,Fujian Medical University,Fuzhou,350005,P.R.China)

机构地区：[1]厦门大学附属第一医院胸外科,福建厦门361000 [2]福建医科大学基础医学院,福州350005

出　　处：《中国胸心血管外科临床杂志》2021年第3期283-287,共5页Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

基　　金：福建省自然科学基金(2020J01122609)。

摘　　要：目的评价人工智能肺部结节辅助诊疗系统鉴别肺结节良恶性及浸润程度的效能。方法回顾性分析2019年1月至2020年8月厦门大学附属第一医院收治的87例肺结节患者的临床资料,其中男33例(37.9%),平均年龄(55.1±10.4)岁;女54例(62.1%),平均年龄(54.5±14.1)岁。共纳入90枚结节,将结节分为恶性肿瘤组(80枚)和良性病变组(10枚),其中恶性肿瘤组又分为浸润性腺癌组(60枚)和非浸润性腺癌组(20枚)。比较各组的恶性概率和倍增时间等信息,分析其对结节的良恶性及浸润程度的预测能力。结果恶性肿瘤组与良性病变组恶性概率差异有统计学意义,且恶性概率可以较好地区分出恶性结节与良性病变(87.2%±9.1%vs.28.8%±29.0%,P=0.000),受试者工作特征(ROC)曲线下面积(AUC)为0.949。良性病变组结节最大径显著大于恶性肿瘤组[(1.270±0.481)cm vs.(0.990±0.361)cm,P=0.026];良性病变的倍增时间明显长于恶性结节[(1083.600±258.180)d vs.(527.025±173.176)d,P=0.000],AUC为0.975。对比浸润性腺癌组与非浸润性腺癌组发现,浸润性腺癌组结节最大径大于非浸润性腺癌组的最大径,且差异有统计学意义[(1.350±0.355)cm vs.(0.863±0.271)cm,P=0.000];两组间恶性概率差异无统计学意义(89.7%±5.7%vs.86.4%±9.9%,P=0.082),AUC为0.630。浸润性腺癌组倍增时间明显较非浸润性腺癌组短[(392.200±138.050)d vs.(571.967±160.633)d,P=0.000],AUC为0.829。结论基于人工智能的肺部结节辅助诊疗系统得出的肺结节恶性概率和倍增时间,可用于辅助鉴定术前肺结节的良恶性及评估浸润情况。Objective To evaluate the effectiveness of the artificial intelligence-assisted diagnosis and treatment system in distinguishing benign and malignant lung nodules and the infiltration degree.Methods Clinical data of 87 patients with pulmonary nodules admitted to the First Affiliated Hospital of Xiamen University from January 2019 to August 2020 were retrospectively analyzed,including 33 males aged 55.1±10.4 years,and 54 females aged 54.5±14.1 years.A total of 90 nodules were included,which were divided into a malignant tumor group(n=80)and a benign lesion group(n=10),and the malignant tumor group was subdivided into an invasive adenocarcinoma group(n=60)and a non-invasive adenocarcinoma group(n=20).The malignant probability and doubling time of each group were compared and its ability to predict the benign and malignant nodules and the invasion degree was analyzed.Results Between the malignant tumor group and the benign lesion group,the malignant probability was significantly different,and the malignant probability could better distinguish malignant nodules and benign lesions(87.2%±9.1%vs.28.8%±29.0%,P=0.000).The area under the curve(AUC)was 0.949.The maximum diameter of nodules in the benign lesion group was significantly longer than that in the malignant tumor group(1.270±0.481 cm vs.0.990±0.361 cm,P=0.026);the doubling time of benign lesions was significantly longer than that of malignant nodules(1083.600±258.180 d vs.527.025±173.176 d,P=0.000),and the AUC was 0.975.The maximum diameter of the nodule in the invasive adenocarcinoma group was longer than that of the non-invasive adenocarcinoma group(1.350±0.355 cm vs.0.863±0.271 cm,P=0.000),and there was no statistical difference in the probability of malignancy between the invasive adenocarcinoma group and the non-invasive adenocarcinoma group(89.7%±5.7%vs.86.4%±9.9%,P=0.082).The AUC was 0.630.The doubling time of the invasive adenocarcinoma group was significantly shorter than that of the non-invasive adenocarcinoma group(392.200±138.050 d vs.571.

关 键 词：人工智能 肺结节 体积倍增时间 浸润性腺癌 肿瘤 

分 类 号：R734.2[医药卫生—肿瘤]