Association of clinical and CT features with the prognosis of lung cancer associated with cystic airspaces and construction of a risk prediction model
- 1Department of Radiology,The Affiliated Hospital of Yangzhou University,Yangzhou,Jiangsu 225000,China
Abstract
Objective To explore the correlation between the clinical and CT characteristics of lung cancer associated with cystic airspaces(LCCA)and progression-free survival(PFS),and to construct a prognostic prediction model.Methods A total of 58 patients with LCCA who were diagnosed and treated from February 2017 to December 2024 at the Affiliated Hospital of Yangzhou University and Baoying Hospital of Traditional Chinese Medicine were selected for a retrospective analysis. Univariate Cox regression analysis was used to screen factors related to PFS,and for small sample data,Firth's penalized regression was used to construct a PFS prediction model,with performance compared to the multivariate Cox regression model,and internal validation was conducted through Bootstrap. The clinical application value of the model was evaluated by calibration,clinical decision,survival and time-dependent receiver operating characteristic(ROC)curves. Results Univariate Cox regression analysis showed that patients 'age,lesion long diameter,lobulated or spiculated margins,pleural traction sign,and surgical treatment were significantly associated with PFS(P<0.05). Both multivariate Cox and Firth's penalized regression model showed that pleural traction sign was an independent risk factor for PFS(HR=8.90,95%CI:2.99-26.51,P<0.01; HR=18.61,95%CI:4.39-114.62,P<0.01),while surgical treatment was an independent protective factor(HR=0.28,95%CI:0.12-0.64,P<0.01; HR=0.23,95%CI:0.04-0.95,P<0.05). The original C-index of the Firth's model was 0.881,and the corrected C-index was 0.873,which was higher than the 0.793 of the Cox model. The calibration curve showed that the model's predicted probabilities were highly consistent with the actual observed probabilities. Decision curve analysis demonstrated a superior net clinical benefit over a wide threshold range compared to treat-all or treat-none strategies. Time-dependent ROC analysis further confirmed the model's high predictive accuracy at various time points,with area under curve(AUC)of 0.85 at 24 months,0.82 at 36 months,and 0.95 at 60 months. Conclusion Both the Firth's penalized regression prognostic model and the multivariate Cox model,established on the small sample data of this study,identify pleural traction sign and surgical treatment as significant predictors of PFS in LCCA patients,but the Firth's method proves superior. For lesions with cystic airspaces on CT,follow-up should be conducted. If there is progression or pleural traction sign indicated by CT,surgical treatment should be considered.
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BibTeX
@article{di2026association,
title = {{Association of clinical and CT features with the prognosis of lung cancer associated with cystic airspaces and construction of a risk prediction model}},
author = {LUO Di and DU Fang and ZHOU Changwu},
journal = {Zhongguo linchuang yanjiu},
year = {2026},
month = oct,
doi = {10.13429/j.cnki.cjcr.2026.10.005},
url = {https://doi.org/10.13429/j.cnki.cjcr.2026.10.005},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Association of clinical and CT features with the prognosis of lung cancer associated with cystic airspaces and construction of a risk prediction model
AU - LUO Di
AU - DU Fang
AU - ZHOU Changwu
PY - 2026
DA - 2026/10/26
JO - Zhongguo linchuang yanjiu
DO - 10.13429/j.cnki.cjcr.2026.10.005
UR - https://doi.org/10.13429/j.cnki.cjcr.2026.10.005
AB - Objective To explore the correlation between the clinical and CT characteristics of lung cancer associated with cystic airspaces(LCCA)and progression-free survival(PFS),and to construct a prognostic prediction model.Methods A total of 58 patients with LCCA who were diagnosed and treated from February 2017 to December 2024 at the Affiliated Hospital of Yangzhou University and Baoying Hospital of Traditional Chinese Medicine were selected for a retrospective analysis. Univariate Cox regression analysis was used to screen factors related to PFS,and for small sample data,Firth's penalized regression was used to construct a PFS prediction model,with performance compared to the multivariate Cox regression model,and internal validation was conducted through Bootstrap. The clinical application value of the model was evaluated by calibration,clinical decision,survival and time-dependent receiver operating characteristic(ROC)curves. Results Univariate Cox regression analysis showed that patients 'age,lesion long diameter,lobulated or spiculated margins,pleural traction sign,and surgical treatment were significantly associated with PFS(P<0.05). Both multivariate Cox and Firth's penalized regression model showed that pleural traction sign was an independent risk factor for PFS(HR=8.90,95%CI:2.99-26.51,P<0.01; HR=18.61,95%CI:4.39-114.62,P<0.01),while surgical treatment was an independent protective factor(HR=0.28,95%CI:0.12-0.64,P<0.01; HR=0.23,95%CI:0.04-0.95,P<0.05). The original C-index of the Firth's model was 0.881,and the corrected C-index was 0.873,which was higher than the 0.793 of the Cox model. The calibration curve showed that the model's predicted probabilities were highly consistent with the actual observed probabilities. Decision curve analysis demonstrated a superior net clinical benefit over a wide threshold range compared to treat-all or treat-none strategies. Time-dependent ROC analysis further confirmed the model's high predictive accuracy at various time points,with area under curve(AUC)of 0.85 at 24 months,0.82 at 36 months,and 0.95 at 60 months. Conclusion Both the Firth's penalized regression prognostic model and the multivariate Cox model,established on the small sample data of this study,identify pleural traction sign and surgical treatment as significant predictors of PFS in LCCA patients,but the Firth's method proves superior. For lesions with cystic airspaces on CT,follow-up should be conducted. If there is progression or pleural traction sign indicated by CT,surgical treatment should be considered.
SN - 1674-8182
ER -