Huang Guanghua, Wang Li, Zhao Zhewei, Wang Yadong, Li Bowen, Huang Zhicheng, Yu Xiaoqing, Liang Naixin, Li Shanqing
Department of Thoracic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No.1 Shuaifuyuan, Beijing, 100730, China.
Department of Hematology, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China.
Gen Thorac Cardiovasc Surg. 2025 Apr 28. doi: 10.1007/s11748-025-02152-z.
Spread through air spaces (STAS) in lung adenocarcinoma impacted prognosis and treatment decisions, but lacked reliable preoperative prediction. We aimed to construct an easy-to-use model for clinical stage IA adenocarcinoma patients.
This study analyzed 1212 patients with clinical stage IA lung adenocarcinoma undergoing lung resections from November 2020 to January 2022. Two logistic regression models were developed. Model 1 used demographic and computed tomography features, and Model 2 incorporated maximum standardized uptake values additionally. Internal validation used tenfold cross-validation. Model discrimination and calibration were described by the area under the curve (AUC) and Spiegelhalter z test, respectively.
Prevalence of STAS was 10.6%. Model 1 consisted of maximum tumor diameter, smoking history, location, spiculation and lobulation, showing moderate discrimination (AUC = 0.700). Model 2 consisted of smoking history, the maximum standardized uptake value, spiculation and lobulation, receiving an AUC of 0.807 and good calibration. Model 2 has a sensitivity and a specificity of 0.857 and 0.652. A nomogram for Model 2 was also developed.
Our study developed and validated two predictive models for STAS for clinical stage IA lung adenocarcinoma. Model 2, integrating maximum standardized uptake value, outperformed Model 1 and offered a more comprehensive approach to predicting STAS. Surgeon could consider the results of Model 2 and intraoperative frozen sections sequentially to optimize surgical strategies. External validation remained warranted.
肺腺癌中的气腔播散（STAS）影响预后和治疗决策，但术前缺乏可靠的预测方法。我们旨在为临床IA期腺癌患者构建一个易于使用的模型。
本研究分析了2020年11月至2022年1月期间1212例接受肺切除术的临床IA期肺腺癌患者。建立了两个逻辑回归模型。模型1使用人口统计学和计算机断层扫描特征，模型2额外纳入了最大标准化摄取值。内部验证采用十折交叉验证。模型的区分度和校准分别用曲线下面积（AUC）和Spiegelhalter z检验来描述。
STAS的患病率为10.6%。模型1由肿瘤最大直径、吸烟史、位置、毛刺征和分叶征组成，显示出中等区分度（AUC = 0.700）。模型2由吸烟史、最大标准化摄取值、毛刺征和分叶征组成，AUC为0.807，校准良好。模型2的灵敏度和特异度分别为0.857和0.652。还为模型2绘制了列线图。
我们的研究开发并验证了两种用于临床IA期肺腺癌STAS的预测模型。整合了最大标准化摄取值的模型2优于模型1，为预测STAS提供了更全面的方法。外科医生可以依次考虑模型2的结果和术中冰冻切片，以优化手术策略。仍需进行外部验证。
