The study aims to investigate the value of intratumoral and peritumoral radiomics and clinical-radiological features for predicting spread through air spaces (STAS) in patients with clinical stage IA non-small cell lung cancer (NSCLC). A total of 336 NSCLC patients from our hospital were randomly divided into the training cohort (n = 236) and the internal validation cohort (n = 100) at a ratio of 7:3, and 69 patients from the other two external hospitals were collected as the external validation cohort. Univariate and multivariate analyses were used to select clinical-radiological features and construct a clinical model. The GTV, PTV5, PTV10, PTV15, PTV20, GPTV5, GPTV10, GPTV15, and GPTV20 models were constructed based on intratumoral and peritumoral (5 mm, 10 mm, 15 mm, 20 mm) radiomics features. Additionally, the radscore of the optimal radiomics model and clinical-radiological predictors were used to construct a combined model and plot a nomogram. Lastly, the ROC curve and AUC value were used to evaluate the diagnostic performance of the model. Tumor density type (OR = 6.738) and distal ribbon sign (OR = 5.141) were independent risk factors for the occurrence of STAS. The GPTV10 model outperformed the other radiomics models, and its AUC values were 0.887, 0.876, and 0.868 in the three cohorts. The AUC values of the combined model constructed based on GPTV10 radscore and clinical-radiological predictors were 0.901, 0.875, and 0.878. DeLong test results revealed that the combined model was superior to the clinical model in the three cohorts. The nomogram based on GPTV10 radscore and clinical-radiological features exhibited high predictive efficiency for STAS status in NSCLC.

Preoperative prediction of visceral pleural invasion (VPI) is important because it enables thoracic surgeons to choose appropriate surgical plans. This study aimed to develop and validate a multivariate logistic regression model incorporating the maximum standardized uptake value (SUV_max) and valuable computed tomography (CT) signs for the non-invasive prediction of VPI status in subpleural clinical stage IA lung adenocarcinoma patients before surgery. A total of 140 patients with subpleural clinical stage IA peripheral lung adenocarcinoma were recruited and divided into a training set (n = 98) and a validation set (n = 42), according to the positron emission tomography/CT examination temporal sequence, with a 7:3 ratio. Next, VPI-positive and VPI-negative groups were formed based on the pathological results. In the training set, the clinical information, the SUV_max, the relationship between the tumor and the pleura, and the CT features were analyzed using univariate analysis. The variables with significant differences were included in the multivariate analysis to construct a prediction model. A nomogram based on multivariate analysis was developed, and its predictive performance was verified in the validation set. The size of the solid component, the consolidation-to-tumor ratio, the solid component pleural contact length, the SUVmax, the density type, the pleural indentation, the spiculation, and the vascular convergence sign demonstrated significant differences between VPI-positive (n = 40) and VPI-negative (n = 58) cases on univariate analysis in the training set. A multivariate logistic regression model incorporated the SUV_max [odds ratio (OR): 1.753, P = 0.002], the solid component pleural contact length (OR: 1.101, P = 0.034), the pleural indentation (OR: 5.075, P = 0.041), and the vascular convergence sign (OR: 13.324, P = 0.025) as the best combination of predictors, which were all independent risk factors for VPI in the training group. The nomogram indicated promising discrimination, with an area under the curve value of 0.892 [95% confidence interval (CI), 0.813-0.946] in the training set and 0.885 (95% CI, 0.748-0.962) in the validation set. The calibration curve demonstrated that its predicted probabilities were in acceptable agreement with the actual probability. The decision curve analysis illustrated that the current nomogram would add more net benefit. The nomogram integrating the SUV_max and the CT features could non-invasively predict VPI status before surgery in subpleural clinical stage IA lung adenocarcinoma patients.

PURPOSE To investigate the value of clinical characteristics and radiological features for predicting spread through air spaces (STAS) in patients with clinical stage IA non-small cell lung cancer (NSCLC). METHODS A total of 336 patients with NSCLC from our hospital were randomly divided into two groups, i.e., the training cohort (n = 236) and the internal validation cohort (n = 100) (7:3 ratio). Furthermore, 69 patients from two other hospitals were collected as the external validation cohort. Eight clinical patient characteristics were recorded, and 20 tumor radiological features were quantitatively measured and qualitatively analyzed. In the training cohort, the differences in clinical characteristics and radiological features were compared using univariate and multivariate analysis. A nomogram was created, and the predictive efficacy of the model was evaluated in the validation cohorts. The receiver operating characteristic curve and area under the curve (AUC) value were used to evaluate the discriminative ability of the model. In addition, the Hosmer-Lemeshow test and calibration curve were used to evaluate the goodness-of-fit of the model, and the decision curve was used to analyze the model's clinical application value. RESULTS The best predictors included gender, the carcinoembryonic antigen (CEA), consolidation-to-tumor ratio (CTR), density type, and distal ribbon sign. Among these, the tumor density type [odds ratio (OR): 6.738] and distal ribbon sign (OR: 5.141) were independent risk factors for predicting the STAS status. Moreover, three different STAS prediction models were constructed, i.e., a clinical, radiological, and combined model. The clinical model comprised gender and the CEA, the radiological model included the CTR, density type, and distal ribbon sign, and the combined model comprised the above two models. A DeLong test results revealed that the combined model was superior to the clinical model in all three cohorts and superior to the radiological model in the external validation cohort; the cohort AUC values were 0.874, 0.822, and 0.810, respectively. The results also showed that the combined model had the highest diagnostic efficacy among the models. The HosmerLemeshow test showed that the combined model showed a good fit in all three cohorts, and the calibration curve showed that the predicted probability value of the combined model was in good agreement with the actual STAS status. Finally, the decision curve showed that the combined model had a better clinical application value than the clinical and radiological models. CONCLUSION The nomogram created in this study, based on clinical characteristics and radiological features, has a high diagnostic efficiency for predicting the STAS status in patients with clinical stage IA NSCLC and may support the creation of personalized treatment strategies before surgery.

Background: In lung cancer, preoperative prediction of visceral pleural invasion (VPI) is helpful for choosing the best treatment plan and improving the prognosis of patients. This study aimed to investigate the usefulness of computed tomography (CT) features in predicting VPI in clinical stage IA peripheral lung adenocarcinoma (LUAD) with pleural contact. Methods: This study divided the type of contact between tumor and pleura into indirect and direct contacts. This study retrospectively analyzed patients with clinical stage IA peripheral LUAD in three hospitals and enrolled 485 patients. The CT features of lesions were analyzed to predict VPI, including relative pleural features, tumor signs, and characteristics between the tumor and pleura. Univariate and multivariate logistic regression analyses were used to select the best combination of variables to predict VPI, and the prediction models were developed. Results: The multivariate logistic regression analysis identified solid component size, pleural tag type, and vascular convergence sign to be independent risk factors for VPI in indirect pleural contact type. The area under curve (AUC) values of the model for predicting VPI in the training, internal validation, and external validation sets were 0.887, 0.799, and 0.862, respectively. Solid component size and pleural indentation sign were identified as independent risk factors for predicting VPI in direct pleural contact type. The AUC values of the model for predicting VPI in the training, internal validation, and external validation sets were 0.903, 0.848, and 0.842, respectively. Conclusions: CT predictors associated with VPI differ based on the type of contact with the pleura. The multivariate logistic regression models utilizing CT features demonstrates acceptable diagnostic accuracy in predicting VPI in clinical stage IA LUAD with pleural contact.

Background and Objective: Pleural adhesion is a significant risk factor that can lead to a poor prognosis in thoracic surgery. The extent of these adhesions directly affects the selection of preoperative surgical approaches, the progress of the intraoperative surgical process, and the occurrence of serious postoperative complications. Accurate preoperative judgment of pleural adhesions and their severity is of great significance for formulating perioperative treatment plans and achieving precision medicine. This article focuses on summarizing and synthesizing various imaging methods for preoperative prediction of pleural adhesions and discusses the challenges and prospects facing future research. The aim is to improve preoperative diagnostic accuracy and risk stratification, thereby enhancing surgical safety and efficacy. Methods: A comprehensive, non-systematic review of the latest literature was carried out in order to investigate the progress of predicting pleural adhesions. PubMed database was being examined, and the last run was on December 17^th 2025. Key Content and Findings: This article reviews the definition, classification, and clinical significance of pleural adhesions, and summarizes the latest research progress of imaging prediction of pleural adhesions in the past 10 years. It was found that ultrasound, chest X-ray, computed tomography (CT), and ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography (PET)/CT can all effectively predict the pleural adhesion status before the operation. Conclusions: Pleural adhesions influence the survival prognosis of patients with thoracic surgery. Effective preoperative prediction of the state of pleural adhesions can help to improve the preoperative surgical plan formulation and reduce the occurrence of postoperative complications. Ultrasound, chest X-ray, CT, and ¹⁸F-FDG PET/CT have their advantages and disadvantages in predicting pleural adhesions, and artificial intelligence (AI) may be the direction of future research.

Preoperative accurate prediction of visceral pleural invasion (VPI) in subsolid nodular pulmonary adenocarcinoma (PA) can provide guidance for the surgical