Pretreatment prediction of tumour spread through air spaces in clinical stage I non-small-cell lung cancer

OBJECTIVES: The aim of this study was to construct a nomogram prediction model for tumour spread through air spaces (STAS) in clinical stage I non-small-cell lung cancer (NSCLC) and discuss its potential application value. METHODS: A total of 380 patients with clinical stage I NSCLC in Tianjin Chest Hospital were collected as the training cohort and 285 patients in Fujian Provincial Hospital were collected as the validation cohort. Univariable and multivariable logistic regression analyses were performed to determine independent factors for STAS in the training cohort. Based on the results of the multivariable analysis, the nomogram prediction model of STAS was constructed by R software. RESULTS: The incidence of STAS in the training cohort was 39.2%. STAS was associated with worse overall survival and recurrence-free survival (P < 0.01). Univariable analysis showed that maximum tumour diameter, consolidation-to-tumour ratio, spiculation, vacuole and carcinoembryonic antigen were associated with STAS (P < 0.05). Multivariable analysis showed that maximum tumour diameter, consolidation-to-tumour ratio, spiculation sign and vacuole were independent risk factors for STAS (P < 0.05). Based on this, the nomogram prediction model of STAS in clinical stage I NSCLC was constructed and internally validated by bootstrap. The Hosmer–Lemeshow test showed a χ(2) value of 7.218 (P = 0.513). The area under the receiver operating characteristic curve and C-index were 0.724 (95% confidence interval: 0.673–0.775). The external validation conducted on the validation cohort produced an area under the receiver operating characteristic curve of 0.759 (95% confidence interval: 0.703–0.816). CONCLUSIONS: The constructed nomogram prediction model of STAS in clinical stage I NSCLC has good calibration and can potentially be applied to guide treatment selection.