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. 2025 Apr 23;20(1):218.
doi: 10.1186/s13019-025-03441-7.

Nomogram model for the preoperative prediction of spread through air spaces in sub-centimeter non-small cell lung cancer

Xiao Wang  1 , Jingwei Shi  2 , Zhengcheng Liu  3
Affiliations

Nomogram model for the preoperative prediction of spread through air spaces in sub-centimeter non-small cell lung cancer

Xiao Wang et al. J Cardiothorac Surg. .

Abstract

Introduction: To construct and validate a nomogram risk prediction model based on clinical characteristics and radiological features to predict spread through air spaces (STAS) of stage IA sub-centimeter non-small cell lung cancer.

Methods: 112 patients who underwent surgical treatment in Nanjing Drum Tower Hospital with pathologically diagnosed stage IA sub-centimeter non-small cell lung cancer were retrospectively collected. The training cohort and the validation cohort were chosen in a 7:3 ratio. Based on the presence or absence of STAS in pathology results, they were divided into STAS positive and STAS negative groups. The independent risk predictors of STAS in clinical characteristics and radiological features were selected by univariate and multivariate logistic regression analysis and then used to construct a nomogram. The sensitivity and specificity were calculated based on the Youden index, area under the curve (AUC), calibration curves and decision curve analysis (DCA) were used to evaluate the performance of the model.

Results: The incidence of STAS in the training cohort was 17.9%. Univariate logistic regression analysis showed that male, anti-GAGE7 antibody positive and mean CT value were associated with the occurrence of STAS; multivariate logistic regression analysis showed that male (OR = 7.900, 95%CI: 1.502-41.545), anti-GAGE7 antibody positive (OR = 10.065, 95%CI: 1.256-80.659) and mean CT value (OR = 1.009, 95%CI: 1.004-1.014) were independent predictors for STAS. The nomogram based on the above factors achieved good predictive performance for STAS with AUC was 0.897 (sensitivity was 0.929, specificity was 0.781) in the training cohort and 0.860 in the validation cohort. The calibration curve and DCA validated the good performance of the model.

Conclusion: The nomogram model established in this study had good predictive performance for STAS status of sub-centimeter lung cancer, and provide reference significance for preoperative planning of patients.

Keywords: Nomogram; Prediction model; Spread through air spaces; Sub-centimeter lung cancer.

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Conflict of interest statement

Declarations. Ethical approval and consent to participate: This retrospective study was approved by the Institutional Ethics Committee of Nanjing Drum Tower Hospital. Written informed consent was waived due to the retrospective nature of this study. Conflict of interest: The authors declare that they have no conflicts of interest.

Figures

Fig. 1
Fig. 1
Patient selection process
Fig. 2
Fig. 2
Preoperative prediction model for STAS in stage IA sub-centimeter non-small cell lung cancer. (A) Nomogram for predicting the incidence of STAS. (B) The calibration curve of the nomogram. (C) The ROC curve of the nomogram in the training cohort. (D) The ROC curve of the nomogram in the validation cohort
Fig. 3
Fig. 3
A 48-year-old woman (left) with lung adenocarcinoma of the right lower lobe, STAS(+). Solid nodule (green arrow), anti-CAGE7 antibody (-), mean CT value: 166.53Hu, nomoscore: 90.73 > 73.00. A 55-year-old woman (right) with lung adenocarcinoma of the left lower lobe, STAS(-). Mixed ground glass nodule (green arrow), anti-CAGE7 antibody (-), mean CT value: -109.97Hu, nomoscore: 65.56 < 73.00
Fig. 4
Fig. 4
DCA plot of the prediction model in the training cohort
See this image and copyright information in PMC

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