Population-based research of pulmonary subsolid nodule CT screening and artificial intelligence application

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. Key words: Lung neoplasms; Cancer screening; Multidetector computed tomography; Subsolid nodule; Artificial intelligence