ObjectivesSpread through air spaces (STAS), a new invasive pattern in lung adenocarcinoma (LUAD), is a risk factor for poor outcome in early-stage LUAD. This study aimed to develop and validate a CT-based radiomics model for predicting STAS in stage IA LUAD.MethodsA total of 395 patients (169 STAS positive and 226 STAS negative cases, including 316 and 79 patients in the training and test sets, respectively) with stage IA LUAD before surgery were retrospectively included. On all CT images, tumor size, types of nodules (solid, mix ground-glass opacities [mGGO] and pure GGO [pGGO]), and GGO percentage were recorded. Region of interest (ROI) segmentation was performed semi-automatically, and 1,037 radiomics features were extracted from every segmented lesion. Intraclass correlation coefficients (ICCs), Pearson’s correlation analysis and least absolute shrinkage and selection operator (LASSO) penalized logistic regression were used to filter unstable (ICC < 0.75) and redundant features (r > 0.8). A temporary model was established by multivariable logistic regression (LR) analysis based on selected radiomics features. Then, seven radiomics features contributing the most were selected for establishing the radiomics model. We then built two predictive models (clinical-CT model and MixModel) based on clinical and CT features only, and the combination of clinical-CT and Rad-score, respectively. The performances of these three models were assessed.ResultsThe radiomics model achieved good performance with an area under of curve (AUC) of 0.812 in the training set, versus 0.850 in the test set. Furthermore, compared with the clinical-CT model, both radiomics model and MixModel showed higher AUC and better net benefit to patients in the training and test cohorts.ConclusionThe CT-based radiomics model showed satisfying diagnostic performance in early-stage LUAD for preoperatively predicting STAS, with superiority over the clinical-CT model.

Objective: Tumor spread through air spaces(STAS) is a poor prognostic factor for early-stage non-small-cell cancers. This investigation sought to determine the correlations of preoperative peripheral blood parameters with STAS and survival outcomes in pathological stage I lung adenocarcinoma (ADC). Methods: We retrospectively reviewed 633 stage I ADC patients who underwent radical surgical resection for the presence of STAS using HE-stained pathological sections. The baseline clinicopathological features, preoperative peripheral blood indexes and follow-up data were analysed. Independent indicators of STAS were identified using multivariate logistic regression. Kaplan‒Meier analyses were used to examine overall survival (OS) and recurrence-free survival (RFS). Multivariate Cox regression analysis wasused to identifyindependent prognostic variables. Results: STAS was discovered in 285 (45.0%) of the 633 patients. STAS positivity was related to gender, smoking status, disease stage, predominant histological pattern, and differentiation. The multivariate logistic regression identified a level of carcinoembryonic antigen (CEA) ≥5 ug/L and absolute monocyte count (AMC) ≥0.38 G/L as an independent predictor of STAS (p=0.005; p=0.013) among the hematological parameters. STAS positivity was an independent poor prognostic factor for RFS and OS in the CEA <5 µg/L subgroup but not in the CEA ≥5 µg/L subgroup (RFS: HR=2.616, 95% CI=1.414-4.839, p=0.002; OS: HR=5.534, 95% CI=1.186-25.816, p=0.029). In STAS-negative patients but not in STAS-positive patients, CEA demonstrated an independent predictive influence for recurrence and death (RFS: HR=6.488, 95% CI=2.475-17.010, p=0.005; OS: HR=19.569, 95% CI=2.487-153.983, p=0.005). Conclusions: Preoperative hematological examination can be prioritised in predicting the presence of STAS, and CEA ≥5 ug/L and AMC ≥ 0.38 G/L were independent risk predictors for STAS in pathological stage I lung adenocarcinoma. Combining preoperative hematological markers with STAS can optimize the prediction of cancer mortality or recurrence following patient subclassification.

Purpose: In recent years, a rising number of multiple primary lung cancers have been detected with the advancement of imaging technology. No detailed study has assessed the prognosis of multiple primary lung adenocarcinomas based on computed tomography characteristics. The present study aimed to analyze outcomes and determine valuable factors for predicting the prognosis of multiple primary lung adenocarcinoma. Methods: This single-center retrospective study was performed from January 2013 to October 2021. All patients were divided into 3 groups based on tumor density as follows: multi-pure ground-glass nodules, at least one part-solid nodule without solid nodules, and at least one solid nodule. Clinicopathologic features, computed tomography signs, and survival outcomes were compared between these groups. The Kaplan-Meier method was used for survival analysis. The multivariable Cox proportional hazards regression model was used to identify independent predictors for recurrence-free survival and overall survival. Results: The sample included 283 patients with 623 lesions who met the inclusion criteria for multiple primary lung adenocarcinoma. Of these patients, 71 (25.1%) presented with multi-pure ground-glass nodules, 100 (35.3%) with at least one part-solid nodule without solid nodule, and 112 (39.6%) with at least one solid nodule. The 3 groups had distinguished clinicopathologic and radiological features of age, adjuvant therapy, types of tumor resection, TNM stage, pathological subtypes, pleural indentation, spicule, and vacuole (all P < .001). Multivariate analysis found that lesion number was an independent predictor for both recurrence-free survival (hazard ratio 2.41; 95% confidence interval 1.12-5.19; P = .025) and overall survival (hazard ratio 4.78; 95% confidence interval 1.88-12.18; P = .001), and the at least one solid nodule was an independent predictor for overall survival (hazard ratio 5.307; 95% confidence interval 1.16-24.31; P = .032). Stage III (hazard ratio 5.71; 95% confidence interval 1.94-16.81; P = .002) and adjuvant therapy (hazard ratio 2.52; 95% confidence interval 1.24-5.13; P = .011) influenced the recurrence-free survival. Conclusions: Survival of multiple primary lung adenocarcinoma patients is strongly correlated with the lesion number and the at least one solid nodule tumors in radiological. This information may be useful for predicting survival and making clinical decisions in future studies.