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Objective <p>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.</p> Methods <p>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 <i>z</i> test, respectively.</p> Results <p>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.</p> Conclusion <p>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.</p>

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Development and internal validation of predictive models for spread through air spaces in clinical stage IA lung adenocarcinoma

  • 期刊论文

  • 发表日期：

    2025年4月28日

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  • Guanghua Huang,
  • Li Wang,
  • Zhewei Zhao,
  • Yadong Wang,
  • Bowen Li,
  • Zhicheng Huang,
  • Xiaoqing Yu,
  • Naixin Liang,
  • Shanqing Li

摘要

Objective

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.

Methods

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.

Results

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.

Conclusion

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.