Background: Lung cancer is the cancer with the highest mortality at home and abroad at present. The detection of lung nodules is a key step to reducing the mortality of lung cancer. Artificial intelligence-assisted diagnosis system presents as the state of the art in the area of nodule detection, differentiation between benign and malignant and diagnosis of invasive subtypes, however, a validation with clinical data is necessary for further application. Therefore, the aim of this study is to evaluate the effectiveness of artificial intelligence-assisted diagnosis system in predicting the invasive subtypes of early‑stage lung adenocarcinoma appearing as pulmonary nodules.
Methods: Clinical data of 223 patients with early-stage lung adenocarcinoma appearing as pulmonary nodules admitted to the Lanzhou University Second Hospital from January 1st, 2016 to December 31th, 2021 were retrospectively analyzed, which were divided into invasive adenocarcinoma group (n=170) and non-invasive adenocarcinoma group (n=53), and the non-invasive adenocarcinoma group was subdivided into minimally invasive adenocarcinoma group (n=31) and preinvasive lesions group (n=22). The malignant probability and imaging characteristics of each group were compared to analyze their predictive ability for the invasive subtypes of early-stage lung adenocarcinoma. The concordance between qualitative diagnostic results of artificial intelligence-assisted diagnosis of the invasive subtypes of early-stage lung adenocarcinoma and postoperative pathology was then analyzed.
Results: In different invasive subtypes of early-stage lung adenocarcinoma, the mean CT value of pulmonary nodules (P<0.001), diameter (P<0.001), volume (P<0.001), malignant probability (P<0.001), pleural retraction sign (P<0.001), lobulation (P<0.001), spiculation (P<0.001) were significantly different. At the same time, it was also found that with the increased invasiveness of different invasive subtypes of early-stage lung adenocarcinoma, the proportion of dominant signs of each group gradually increased. On the issue of binary classification, the sensitivity, specificity, and area under the curve (AUC) values of the artificial intelligence-assisted diagnosis system for the qualitative diagnosis of invasive subtypes of early-stage lung adenocarcinoma were 81.76%, 92.45% and 0.871 respectively. On the issue of three classification, the accuracy, recall rate, F1 score, and AUC values of the artificial intelligence-assisted diagnosis system for the qualitative diagnosis of invasive subtypes of early-stage lung adenocarcinoma were 83.86%, 85.03%, 76.46% and 0.879 respectively.
Conclusions: Artificial intelligence-assisted diagnosis system could predict the invasive subtypes of early‑stage lung adenocarcinoma appearing as pulmonary nodules, and has a certain predictive value. With the optimization of algorithms and the improvement of data, it may provide guidance for individualized treatment of patients.
【中文题目：人工智能辅助诊断系统预测 肺结节早期肺腺癌浸润亚型的临床研究】 【中文摘要：背景与目的 肺癌是国内外致死率最高的恶性肿瘤，肺结节的精确检测是降低肺癌死亡率的关键。人工智能辅助诊断系统在肺结节检测、良恶性鉴别和浸润亚型诊断等领域发展迅速，对其效能进行验证是促进其应用于临床的前提。本研究旨在评估人工智能辅助诊断系统预测肺结节早期肺腺癌浸润亚型的效能。方法 回顾性分析2016年1月1日-2021年12月31日期间兰州大学第二医院收治的223例肺结节早期肺腺癌患者的临床资料，将早期肺腺癌分为浸润性腺癌组（n=170）和非浸润性腺癌组（n=53），其中非浸润性腺癌组又分为微浸润性腺癌组（n=31）和浸润前病变组（n=22）。比较各组的恶性概率和影像特征等信息，分析其对早期肺腺癌浸润亚型的预测能力，并对人工智能辅助诊断早期肺腺癌浸润亚型定性诊断的结果与术后病理进行一致性分析。结果 早期肺腺癌不同浸润亚型肺结节的平均CT值（P<0.001）、直径（P<0.001）、体积（P<0.001）、恶性概率（P<0.001）、胸膜凹陷征（P<0.001）、分叶征（P<0.001）、毛刺征（P<0.001）差异均有统计学意义；随着早期肺腺癌不同浸润亚型浸润性增加，各组参数显性征象比例也逐渐升高；在二分类问题上，人工智能辅助诊断系统定性诊断早期肺腺癌浸润亚型的敏感性、特异性及曲线下面积（area under the curve, AUC）分别为81.76%、92.45%和0.871；在三分类问题上，人工智能辅助诊断系统定性诊断早期肺腺癌浸润亚型的准确率、召回率、F1分数及AUC分别为83.86%、85.03%、76.46%和0.879。结论 该人工智能辅助诊断系统对肺结节早期肺腺癌浸润亚型具有一定的预测价值，随着算法的优化和数据的完善或可为患者个体化治疗提供指导。 】 【中文关键词：人工智能；肺结节；肺肿瘤；腺癌；浸润亚型】.
Keywords: Adenocarcinoma; Artificial intelligence; Invasive subtypes; Lung neoplasms; Pulmonary nodule.