Zhongguo quanke yixue (Jun 2022)
Inflencing Factors for Pulmonary Nodular Growth Predicted by Artificial Intelligence-based Follow-up
Abstract
Background Lung cancer ranks first in terms of incidence and mortality rates among cancers, with a 5-year survival rate of less than 20%. Many ways have been used to screen for early lung cancer, among which artificial intelligence (AI) has greatly improved the detection rate. However, how to use AI technologies to effectively manage atypical lung nodules to timely find early lung cancer, and to identify associated factors of lung nodule growth, which is an issue significantly associated with the guidance of clinical management of lung nodules. Objective To investigate the influencing factors of pulmonary nodules growth identified by AI-based follow-up and relevant clinical value. Methods A total of 175 patients with pulmonary nodules admitted to the Third Affiliated Hospital of Jinzhou Medical University in April 2019 were selected for a retrospective study. General clinical data, and AI-based analysis of imaging information related to pulmonary nodules was collected. The growth of pulmonary nodules〔solid nodules (in 82 cases) and ground-glass nodules (in 93 cases) classified by AI-based analysis〕 were observed by regular follow-ups. The influencing factors of pulmonary nodules growth were explored by Cox regression analysis. Results Patients with solid nodules had higher prevalence of solid components, and mean CT quantitative parameters of nodules than those with ground-glass nodules (P<0.001) . Multivariate Cox regression analysis showed that average diameter〔HR=2.185, 95%CI (1.079, 4.425) , P=0.030〕, volume〔HR=1.001, 95%CI (1.000, 1.001) , P=0.022〕, malignant probability〔HR=2.232, 95%CI (1.036, 4.806) , P=0.040〕and surface signs〔HR=2.125, 95%CI (1.006, 4.489) , P=0.048〕 of the nodule were associated with solid nodular growth. The average diameter〔HR=2.458, 95%CI (1.053, 5.739) , P=0.038〕, volume〔HR=1.001, 95%CI (1.000, 1.002) , P=0.010〕, prevalence of solid components〔HR=1.022, 95%CI (1.002, 1.041) , P=0.030〕, malignant probability〔HR=2.386, 95%CI (1.174, 4.850) , P=0.016〕, surface signs〔HR=3.026, 95%CI (1.492, 6.136) , P=0.002〕, mean CT quantitative parameters〔HR=1.002, 95%CI (1.000, 1.003) , P=0.045〕 of the nodule were associated with the growth of ground-glass nodules. Conclusion The growth of pulmonary nodules was affected by many factors, such as original nodule size, mean CT quantitative parameters, presence of surface signs and malignant probability. It is suggested that clinicians determine the effective follow-up time based on the inflencing factors of pulmonary nodules growth identified by AI technologies, so as to detect the growth of pulmonary nodules as soon as possible and deliver treatment measures timely.
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