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.

To investigate the influencing factors of pulmonary nodules growth identified by AI-based follow-up and relevant clinical value.

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.

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.

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.

肺癌的发病率和死亡率均居世界首位，5年生存率不到20%，对于早期肺癌的筛查有多种方式，其中人工智能（AI）极大提高了早期肺癌的检出率，但目前仍存在对不典型肺结节如何有效管理以尽早发现早期肺癌的问题，探究肺结节增长的影响因素对指导临床管理具有重要意义。

探讨AI随访肺结节增长的影响因素及临床应用价值。

回顾性选取2019年4月就诊于锦州医科大学附属第三医院的175例肺结节患者作为研究对象，根据AI分类分为实性结节组82例和磨玻璃结节（GGN）组93例。收集研究对象的一般资料，并利用AI计算收集肺结节相关影像学信息，定期随访以观察不同肺结节的增长情况，应用多因素Cox比例风险回归分析探讨肺结节增长的影响因素。

实性结节组的实性占比、平均CT值高于GGN组（P<0.001）。多因素Cox比例风险回归分析结果显示，结节平均直径〔HR=2.185，95%CI（1.079，4.425），P=0.030〕、结节体积〔HR=1.001，95%CI（1.000，1.001），P=0.022〕、恶性概率〔HR=2.232，95%CI（1.036，4.806），P=0.040〕及表面征象〔HR=2.125，95%CI（1.006，4.489），P=0.048〕是实性结节增长的影响因素；平均直径〔HR=2.458，95%CI（1.053，5.739），P=0.038〕、体积〔HR=1.001，95%CI（1.000，1.002），P=0.010〕、实性占比〔HR=1.022，95%CI（1.002，1.041），P=0.030〕、恶性概率〔HR=2.386，95%CI（1.174，4.850），P=0.016〕及表面征象〔HR=3.026，95%CI（1.492，6.136），P=0.002〕、平均CT值〔HR=1.002，95%CI（1.000，1.003），P=0.045〕是GGN增长的影响因素。

肺结节增长受原始结节大小、平均CT值、有无表面征象及恶性概率等多种因素影响，建议临床医师结合AI计算的肺结节增长影响因素确定有效随访时间，以尽早发现肺结节增长并及时采取治疗措施。