A nomogram integrating the clinical and CT imaging characteristics for assessing spread through air spaces in clinical stage IA lung adenocarcinoma

被引：0

作者：

Yang, Yantao ^{[1
]}

Li, Li ^{[2
]}

Hu, Huilian ^{[3
]}

Zhou, Chen ^{[1
]}

Huang, Qiubo ^{[1
]}

Zhao, Jie ^{[1
]}

Duan, Yaowu ^{[1
]}

Li, Wangcai ^{[1
]}

Luo, Jia ^{[4
]}

Jiang, Jiezhi ^{[5
]}

Yang, Zhenghong ^{[1
]}

Zhao, Guangqiang ^{[1
]}

Huang, Yunchao ^{[1
]}

Ye, Lianhua ^{[1
]}

机构：

[1] Kunming Med Univ, Peking Univ Canc Hosp Yunnan, Affiliated Hosp 3, Yunnan Canc Hosp,Dept Thorac & Cardiovasc Surg, Kunming, Peoples R China

[2] Kunming Med Univ, Peking Univ Canc Hosp Yunnan, Yunnan Canc Hosp, Canc Biotherapy Ctr,Affiliated Hosp 3, Kunming, Peoples R China

[3] Qujing City Hosp Tradit Chinese Med, Dept Oncol, Qujing, Peoples R China

[4] Kunming Med Univ, Peking Univ Canc Hosp Yunnan, Yunnan Canc Hosp, Dept Pathol,Affiliated Hosp 3, Kunming, Peoples R China

[5] Kunming Med Univ, Peking Univ Canc Hosp Yunnan, Affiliated Hosp 3, Yunnan Canc Hosp,Dept Radiol, Kunming, Peoples R China

来源：

FRONTIERS IN IMMUNOLOGY | 2025年 / 16卷

基金：

中国国家自然科学基金;

关键词：

clinical feature; radiologic characteristic; lung adenocarcinoma; STAS; nomogram; TUMOR SPREAD; COMPUTED-TOMOGRAPHY; LIMITED RESECTION; PROGNOSTIC IMPACT; 8TH EDITION; CANCER; LOBECTOMY; CLASSIFICATION; RECURRENCE; ONCOLOGY;

D O I：

10.3389/fimmu.2025.1519766

中图分类号：

R392 [医学免疫学]; Q939.91 [免疫学];

学科分类号：

100102 ;

摘要：

Purpose: This study aimed to create a nomogram model to predict the spread through air spaces (STAS) in patients diagnosed with stage IA lung adenocarcinoma, utilizing a substantial sample size alongside a blend of clinical and imaging features. This model serves as a valuable reference for the preoperative planning process in these patients. Materials and methods: A total of 1244 individuals were included in the study. Individuals who received surgical intervention between January 2022 and May 2023 were categorized into a training cohort (n=950), whereas those treated from June 2023 to October 2023 were placed in a validation cohort (n=294). Data from clinical assessments and CT imaging were gathered from all participants. In the training cohort, analyses employing both multivariate and univariate logistic regression were performed to discern significant clinical and CT characteristics. The identified features were subsequently employed to develop a nomogram prediction model. The evaluation of the model's discrimination, calibration, and clinical utility was conducted in both cohorts. Results: In the training cohort, multivariate logistic regression analysis revealed several independent risk factors associated with invasive adenocarcinoma: maximum diameter (OR=2.459, 95%CI: 1.833-3.298), nodule type (OR=4.024, 95%CI: 2.909-5.567), pleura traction sign (OR=2.031, 95%CI: 1.394-2.961), vascular convergence sign (OR=3.700, 95%CI: 1.668-8.210), and CEA (OR=1.942, 95%CI: 1.302-2.899). A nomogram model was constructed utilizing these factors to forecast the occurrence of STAS in stage IA lung adenocarcinoma. The Area Under the Curve (AUC) measured 0.835 (95% CI: 0.808-0.862) in the training cohort and 0.830 (95% CI: 0.782-0.878) in the validation cohort. The internal validation conducted through the bootstrap method yielded an AUC of 0.846 (95% CI: 0.818-0.881), demonstrating a robust capacity for discrimination. The Hosmer-Lemeshow goodness-of-fit test confirmed a satisfactory model fit in both groups (P > 0.05). Additionally, the calibration curve and decision analysis curve demonstrated high calibration and clinical applicability of the model in both cohorts. Conclusion: By integrating clinical and CT imaging characteristics, a nomogram model was developed to predict the occurrence of STAS, demonstrating robust predictive performance and providing valuable support for decision-making in patients with stage IA lung adenocarcinoma.

引用

页数：12