Population-based research of pulmonary subsolid nodule CT screening and artificial intelligence application
10.3760/cma.j.cn112434-20191126-00420
- VernacularTitle:人群肺亚实性结节CT筛查及人工智能应用研究初探
- Author:
Feng YANG
1
;
Jun FAN
;
Junyi TIANZHOU
;
Fan YANG
;
Yun LI
;
Xianping LIU
;
Jianfeng LI
;
Guanchao JIANG
;
Jun WANG
Author Information
1. 北京大学人民医院胸外科暨胸部微创中心 100044
- From:
Chinese Journal of Thoracic and Cardiovascular Surgery
2020;36(3):145-150
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the application of low-dose chest CT(LDCT) in the screening of pulmonary subsolid nodules in population and the application value of artificial intelligence.Methods:People who received chest LDCT screening between January 2015 and December 2017 were included. A retrospective study was developed to analyze the enrolled population features , detection of pulmonary subsolid nodules and independent predictors of subsolid nodules , and to evaluate the accuracy of the artificial intelligence reading method.Results:Result of three cross-sectional studies reveals that the detection rates of pulmonary subsolid nodules were 0.42%, 0.69% and 0.92% in three rounds. 726 cases who completed the three rounds of screening were included in the cohort study. The cohort population was predominantly male(83.2%), with a median age of 43 years, and nearly half of the subjects(47.0%) had a history of smoking. GEE revealed that the patient's family history of lung cancer( OR=8.753, 95% CI: 1.877-40.816, P=0.006) was an independent predictor of the detection of subsolid nodules. In the 110 kVp tube voltage group, AUC of AI model was 0.740, and AUC of the manual reading method was 0.721, no significant differences were observed( P=0.502); when the preseted cutoff value of AI model was 0.75, the NRI was -0.15, indicating the accuracy of AI model was inferior to manual method( P=0.006). In the 130 kVp tube voltage group, AUC of the model was 0.888, and AUC of the manual reading method was 0.756, no significant differences were observed( P=0.128); and the NRI was 0.19, indicating the accuracy of AI model was not inferior to manual method( P=0.123). Conclusion:This population' s detection rates of pulmonary subsolid nodules were 0.42%-0.92%. Family history of lung cancer was an independent predictor of subsolid pulmonary nodules. The result of AI pulmonary nodule detection model could be a reference when the training set data parameters match the actual application parameters.
