Study on the application of artificial intelligence system in the detection and differentiation of benign and malignant pulmonary nodules
10.3760/cma.j.cn112434-20200817-00375
- VernacularTitle:人工智能系统在肺结节检出及良恶性鉴别中的应用研究
- Author:
Ci YIN
1
;
Wenjie MAO
;
Bin LI
;
Cheng WANG
;
Peng JIANG
;
Duojie ZHU
;
Jianbao YANG
;
Yuqi MENG
;
Xiaoping WEI
;
Tao JING
;
Haiming FENG
;
Shaobo ZHANG
;
Junping LIN
Author Information
1. 兰州大学第二医院胸外科 兰州大学第二临床医学院 730030
- From:
Chinese Journal of Thoracic and Cardiovascular Surgery
2020;36(9):553-556
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To evaluate the efficacy of artificial intelligence assisted pulmonary nodule diagnosis system in detection pulmonary nodule and predicting the malignant probability of pulmonary nodule.Methods:A retrospectively analyze the clinical data of 199 patients with lung nodules in the Thoracic Surgery Department of Lanzhou University Second Hospital from May 2016 to July 2020. The preoperative chest CT was imported into the artificial intelligence system to record the detected lung nodules, to measure nodal diameter and density classification and malignant probability prediction value of each nodule. The detection rate of pulmonary nodules by artificial intelligence system was calculated, and the sensitivity, specificity, positive likelihood ratio and negative likelihood ratio of artificial intelligence system in the differential diagnosis of benign and malignant pulmonary nodules were calculated and compared with manual film reading. and the sensitivity and specificity in the differential diagnosis of benign and malignant pulmonary nodules under the condition of different size and density of pulmonary nodules.Results:A total of 204 pulmonary nodules were pathologically diagnosed by surgical resection, and the detection rate of pulmonary nodules by artificial intelligence system was 100%. The artificial intelligence system can distinguish benign and malignant pulmonary nodules with a sensitivity of 95.83%(95% CI: 0.8967-0.9883), specificity 25.00%(95% CI: 0.1717-0.3425), and a positive likelihood ratio of 1.27(95% CI: 1.14-1.44), negative likelihood ratio 0.17(95% CI: 0.06-0.46), Manual reading for the differentiation of benign and malignant pulmonary nodules has a sensitivity of 87.36%(95% CI: 0.7850-0.9352), specificity 72.17%(95% CI: 0.6214-0.8079), and a positive likelihood ratio of 3.14(95% CI: 2.26-4.37), the negative likelihood ratio is 0.18(95% CI: 0.10-0.31). 5mm≤diameter of pulmonary nodule<10 mm, sensitivity 100%(95% CI: 0.6637-1.0000), specificity 50.00%(95% CI: 0.01258-0.98740), 10 mm≤diameter of pulmonary nodule <20 mm, sensitivity 94.29%(95% CI: 0.8084-0.9930), specificity 29.83%(95% CI: 0.1843-0.4340), 20 mm≤ diameter of pulmonary nodule ≤30 mm, sensitivity 96.15%(95% CI: 0.8679-0.9953), specificity 18.37%(95% CI: 0.0876-0.9953), sensitivity of subsolid lung nodules: 100%(95% CI: 0.9051-1.0000), specificity 20.00%(95% CI: 0.0051-0.7164), solid lung nodule sensitivity 93.22%(95% CI: 0.8354-0.9812), specificity 25.24%(95% CI: 0.1720-0.3476). Conclusion:The artificial intelligence assistant diagnosis system of pulmonary nodules has a strong performance in the detection of pulmonary nodules, but it can not meet the clinical requirements in the differentiation of benign and malignant pulmonary nodules. At present, the artificial intelligence system can be used as an auxiliary tool for doctors to detect pulmonary nodules and assist in the diagnosis of benign and malignant pulmonary nodules.
