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

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...

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

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.571.967±160.633 d,P=0.000),and the AUC was 0.829.Conclusion The malignant probability and doubling time of lung nodules calculated by the artificial intelligence-assisted diagnosis and treatment system can be used in the assessment of the preoperative benign and malignant lung nodules and the infiltration degree.