Artificial intelligence-assisted diagnosis and treatment system in prediction of benign or malignant lung nodules and infiltration degree
- VernacularTitle: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
- Author:
Mengkun CAO
1
;
Jie JIANG
1
;
Xiaolei ZHU
1
;
Ning LI
1
;
Jianweng WANG
1
;
Junfeng LIN
1
;
Hongming LIU
1
;
Chengqing DENG
1
;
Xiqian CAI
2
;
Guojun GENG
1
Author Information
1. Department of Thoracic Surgery, The First Affiliated Hospital of Xiamen University, Xiamen, 361000, Fujian, P.R.China
2. School of Basic Medicine, Fujian Medical University, Fuzhou, 350005, P.R.China
- Publication Type:Journal Article
- Keywords:
Artificial intelligence;
lung nodules;
volume doubling time;
invasive adenocarcinoma;
tumor
- From:
Chinese Journal of Clinical Thoracic and Cardiovascular Surgery
2021;28(03):283-287
- CountryChina
- Language:Chinese
-
Abstract:
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 (1 083.600±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.
