Abstract

Background: Spread through air spaces (STAS) is a prognostic factor for survival in non-small cell lung cancer (NSCLC), but the chance to identify it before surgery remains challenging. In this study, we consider clinical, pathological and metabolic factors, with the aim to identify possible STAS predictors in NSCLC. Methods: Clinical and pathological characteristics of patients who underwent anatomical lung resection from 1 January 2018 to 31 December 2023 were retrospectively reviewed and analyzed. Patients with GGO, AIS, or MIA tumors, metastases, or undergoing neoadjuvant therapy were excluded. The parameters assessed by 18F-FDG PET/CT were: SUVmax, SUVmean, SUVpeak, TLG and MTV. The primary endpoint was the association between clinical, metabolic, and pathologic characteristics with the presence of STAS. A univariate and multivariate logistic regression model was developed to identify independent predictors of STAS. Results: The final analysis was conducted on 224 patients. Non-lepidic-acinar adenocarcinomas showed a statistically significant higher risk of STAS than the lepidic-acinar histotype: 20.8% vs. 4.3%, p = 0.003, OR 5.87, 95%CI 1.83–18.78. Furthermore, tumor grade was significantly associated with STAS: 4.6% in G1–G2 tumors vs. 23.5% in G3 tumors (p = 0.001, OR 5.79, 95%CI 2.12–15.78); as well as lymph vascular invasion (p = 0.034) and tumor size >2 cm (p = 0.017). Multivariate analysis confirmed high tumor grade as an independent risk factor (OR 4.73, 95%CI 1.16–19.23, p = 0.030). Conclusions: In our study, risk of STAS is not correlated with metabolic parameters, while adenocarcinoma subtypes and tumors grading seem to stratify the risk of STAS occurrence. These results may be consolidated in an external validation analysis to possibly better plan the extent of lung resection.

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