ObjectiveTo investigate the correlation between spread through air space (STAS) of sub-centimeter non-small cell lung cancer and clinical characteristics and radiological features, constructing a nomogram risk prediction model for STAS to provide a reference for the preoperative planning of sub-centimeter non-small cell lung cancer patients. MethodsThe data of patients with sub-centimeter non-small cell lung cancer who underwent surgical treatment in Nanjing Drum Tower Hospital from January 2022 to October 2023 were retrospectively collected. According to the pathological diagnosis of whether the tumor was accompanied with STAS, they were divided into a STAS positive group and a STAS negative group. The clinical and radiological data of the two groups were collected for univariate logistic regression analysis, and the variables with statistical differences were included in the multivariate analysis. Finally, independent risk factors for STAS were screened out and a nomogram model was constructed. The sensitivity and specificity were calculated based on the Youden index, and area under the curve (AUC), calibration plots and decision curve analysis (DCA) were used to evaluate the performance of the model. ResultsA total of 112 patients were collected, which included 17 patients in the STAS positive group, consisting of 11 males and 6 females, with a mean age of (59.0±10.3) years. The STAS negative group included 95 patients, with 30 males and 65 females, and a mean age of (56.8±10.3) years. Univariate logistic regression analysis showed that male, anti-GAGE7 antibody positive, mean CT value and spiculation were associated with the occurrence of STAS (P＜0.05). Multivariate regression analysis showed that associations between STAS and male (OR=5.974, 95%CI 1.495 to 23.872), anti-GAGE7 antibody positive (OR=11.760, 95%CI 1.619 to 85.408) and mean CT value (OR=1.008, 95%CI 1.004 to 1.013) were still significant (P＜0.05), while the association between STAS and spiculation was not significant anymore (P=0.438). Based on the above three independent predictors, a nomogram model of STAS in sub-centimeter non-small cell lung cancer was constructed. The AUC value of the model was 0.890, the sensitivity was 76.5%, and the specificity was 91.6%. The calibration curve was well fitted, suggesting that the model had a good prediction efficiency for STAS. The DCA plot showed that the model had a good clinically utility. ConclusionMale, anti-GAGE7 antibody positive and mean CT value are independent predictors of STAS positivity of sub-centimeter non-small cell lung cancer, and the nomogram model established in this study has a good predictive value and provides reference for preoperative planning of patients.
With the widespread adoption of lung cancer screening, an increasing number of patients are being diagnosed with early-stage lung adenocarcinoma. For stage ⅠA lung adenocarcinoma, sublobar resection is the primary treatment approach. However, in patients with concomitant spread through air space (STAS), numerous studies advocate for lobectomy as the mainstay of treatment. Due to the limitations in preoperative prediction and intraoperative frozen section evaluation for assessing STAS, current research is largely restricted to using clinical and imaging features to predict STAS occurrence, with results that are inconsistent and unsatisfactory. Furthermore, most studies focus on individual clinical or imaging characteristics, and there is a lack of large-sample investigations. The rise of artificial intelligence in recent years has provided new insights into solving this problem, and existing studies have shown that artificial intelligence demonstrates better performance in STAS prediction compared to conventional methods. This article reviews the value of artificial intelligence in predicting STAS.
Objective To investigate the significance of spread through air spaces (STAS) in early-stage non-small cell lung cancer (NSCLC) patients undergoing either sublobar resection or lobectomy by pooling evidence available, and to assess the accuracy of frozen sections in determining types of resection among patients with suspected presence of STAS. MethodsStudies were identified by searching databases including PubMed, EMbase, Web of Science, and The Cochrane Library from inception to July 2022. Two researchers independently searched, screened, evaluated literature, and extracted data. Statistical analysis was conducted using RevMan 5.4 and STATA 15.0. The Newcastle-Ottawa Scale (NOS) was used to evaluate the quality of the study. ResultsA total of 26 studies involving 23 surgical related studies (12 266 patients) were included, among which, 11 compared the outcomes of lobectomy with sublobar resection in the STAS-positive patients. NOS score≥6 points. Meta-analysis indicated that presence of STAS shortened patients' survival in both lobectomy group and sublobar resection group (RFS: HR=2.27, 95%CI 1.96-2.63, P＜0.01; OS: HR=2.08, 95%CI 1.74-2.49, P＜0.01). Moreover, lobectomy brought additional survival benefits to STAS-positive patients compared with sublobar resection (RFS: HR=1.97, 95%CI 1.59-2.44, P＜0.01; OS: HR=1.91, 95%CI 1.47-2.48, P＜0.01). Four studies were included to assess the accuracy of identifying presence of STAS on intraoperative frozen sections, of which the pooled sensitivity reached 55% (95%CI 45%-64%), the pooled specificity reached 92% (95%CI 77%-97%), and the pooled area under the curve was 0.68 (95%CI 0.64-0.72) based on the data available. Conclusion This study confirms that presence of STAS is a critical risk factor for patients with early-stage NSCLC. Lobectomy should be recommended as the first choice when presence of STAS is identified on frozen sections, as lobectomy can prolong patients' survival compared with sublobar resection in STAS-positive disease. The specificity of identifying STAS on frozen sections seems to be satisfactory, which may be helpful in determining types of resection. However, more robust methods are urgently in need to make up for the limited sensitivity and accuracy of frozen sections.
ObjectiveTo develop and validate a machine learning model based on preoperative clinical characteristics, laboratory indices, and radiological features for the non-invasive prediction of spread through air spaces (STAS) in patients with early-stage lung adenocarcinoma. Methods Preoperative data from patients with early-stage lung adenocarcinoma who underwent surgical resection at Northern Jiangsu People's Hospital between January 2020 and August 2025 were retrospectively collected. The data included clinical characteristics, laboratory indices, and radiological features. Patients were divided into a STAS-positive and a STAS-negative group based on postoperative pathological findings. The dataset was randomly split into a training set and a testing set at a 7 : 3 ratio. Feature variables were selected using the maximum relevance and minimum redundancy (mRMR) algorithm and the least absolute shrinkage and selection operator (LASSO) regression. Five machine learning models were constructed: logistic regression (LR), random forest (RF), support vector machine (SVM), light gradient boosting machine (LightGBM), and extreme gradient boosting (XGBoost). Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA). The shapley additive explanations (SHAP) method was employed to interpret the optimal prediction model. Results A total of 377 patients were included, comprising 177 (46.9%) males and 200 females (53.1%), with a mean age of (63.31±9.73) years. There were 261 patients in the training set and 116 patients in the testing set. In the training set, statistically significant differences were observed between the STAS-positive group (n=130) and STAS-negative group (n=131) across multiple features, including age, sex, neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), clinical T stage, and maximum solid component diameter (P<0.05). A final set of 10 feature variables was selected by combining mRMR and LASSO regression, and five machine learning models (LR, RF, SVM, LightGBM, XGBoost) were developed. The XGBoost model demonstrated superior predictive performance in both the training and testing sets, achieving AUCs of 0.947 [95%CI (0.920, 0.975)] and 0.943 [95%CI (0.894, 0.993)], respectively, and achieved the optimal level in the testing set. DCA indicated that the XGBoost model provided a high net clinical benefit across a wide range of threshold probabilities. SHAP analysis revealed that the vessel convergence sign, clinical T stage, age, consolidation-to-tumor ratio (CTR), and MLR were the features with the highest contributions to STAS prediction. Conclusion The XGBoost model effectively predicts preoperative STAS status in early-stage lung adenocarcinoma, exhibiting excellent discriminative performance and good clinical interpretability. Key predictors such as the vessel convergence sign, clinical T stage, age and CTR provide a crucial reference for preoperative risk assessment and the individualized selection of surgical strategies, ultimately benefiting patients.
ObjectiveTo investigate the predictive value of preoperative radiological features on spread through air spaces (STAS) in stage cⅠA lung adenocarcinoma with predominant ground-glass opacity, and to provide a basis for the selection of surgical methods for these patients.MethodsThe clinical data of 768 patients with stage cⅠA lung adenocarcinoma undergoing operation in our hospital from 2017 to 2018 were reviewed, and 333 early stage lung adenocarcinoma patients with predominant ground-glass opacity were selected. There were 92 males and 241 females, with an average age of 57.0±10.0 years. Statistical analysis was performed using SPSS 22.0.ResultsSTAS-positive patients were mostly invasive adenocarcinoma (P=0.037), and had more micropapillary component (P<0.001) and more epidermal growth factor receptor (EGFR) gene mutations (P=0.020). There were no statistically significant differences between the STAS-positive and STAS-negative patients in other clinicopathological features. Univariate analysis showed that the maximum diameter of tumor in lung window (P=0.029), roundness (P=0.035), maximum diameter of solid tumor component in lung window (P<0.001), consolidation/tumor ratio (CTR, P<0.001), maximum area of the tumor in mediastinum window (P=0.001), tumor disappearance ratio (TDR, P<0.001), average CT value (P=0.001) and lobulation sign (P=0.038) were risk factors for STAS positive. Multivariate logistic regression analysis showed that the CTR was an independent predictor of STAS (OR=1.05, 95%CI 1.02 to 1.07, P<0.001), and the area under the receiver operating characteristic (ROC) curve was 0.71 (95%CI 0.58 to 0.85, P=0.002). When the cutoff value was 19%, the sensitivity of predicting STAS was 66.7%, and the specificity was 75.2%.ConclusionCTR is a good radiological feature to predict the occurrence of STAS in early lung adenocarcinoma with predominant ground-glass opacity. For the stagecⅠA lung adenocarcinoma with predominant?ground-glass opacity and CTR ≥19%, the possibility of STAS positive is greater, and sublobar resection needs to be carefully considered.
ObjectiveTo investigate the effect of different lymph node dissection methods on the prognosis of patients with stage ⅠA spread through air space (STAS)-positive lung adenocarcinoma≤ 2 cm. MethodsClinical data of 3148 patients with lung adenocarcinoma who underwent surgery at the Department of Thoracic Surgery, the First Affiliated Hospital of University of Science and Technology of China from 2016 to 2018 were retrospectively analyzed. Patients with stage ⅠA STAS-positive lung adenocarcinoma≤ 2 cm were included and divided into two groups based on lymph node dissection methods: systematic lymph node dissection group and limited lymph node dissection group. Compare the clinical and pathological data of two groups of patients and use Cox proportional hazards regression model for multivariate survival analysis. ResultsA total of 209 STAS-positive patients were enrolled in the study, including 98 males and 111 females, aged 28-83 (60.42±10.15) years. Univariate analysis showed that the mode of lymph node dissection, past history, micropapillary histological subtype, and papillary histological subtype were risk factors for patient prognosis. Multifactorial analysis showed that lymph node dissection method, age, and micropapillary histological subtype were risk factors for patient prognosis. Meanwhile, among STAS-positive patients, systematic lymph node dissection had a better prognosis than limited lymph node dissection patients. ConclusionSTAS plays an important role in patient prognosis as an independent risk factor for prognosis of stage ⅠA ≤2 cm lung adenocarcinoma. When STAS is positive, the choice of systematic lymph node dissection may be more favourable to patients' long-term prognosis.