Background: The management of a solitary pulmonary nodule is a challenging issue in pulmonary disease. Although many factors have been defined as predictors for malignancy in solitary pulmonary nodules, the accurate diagnosis can only be established with the permanent histological diagnosis.Objective: We tried to clarify the possible predictors of malignancy in solitary pulmonary nodules in patients who had definitive histological diagnosis.Methods: We made a retrospective study to collect the data of patients with solitary pulmonary nodules who had histological diagnosis either before or after surgery. We made a statistical analysis of both the clinic and radiological features of these nodules with respect to malignancy both in contingency tables and with logistic regression analysis.Results: We had a total of 223 patients with a radiological diagnosis of solitary pulmonary nodule. Age, smoking status and pack years of smoking, maximum standardized uptake value (SUVmax), and radiological features such as solid component, spiculation, pleural tag, lobulation, calcification, and higher density were significant predictors of malignancy in contingency tables. Age, smoking status and smoking (pack/year), SUVmax, and radiological features including spiculation, pleural tag, lobulation, calcification, and higher density were the significant predictors in univariate analysis. However, multivariate analysis revealed only SUVmax greater than 2.5 (p < 0.0001), spiculation (p = 0.009), and age older than 61 years (p = 0.015) as the significant predictors for malignancy.Conclusion: Age, SUVmax, and spiculation are the independent predictors of malignancy in patients with solitary pulmonary nodules.

Turkthoracj, Turkish Thoracic Journal

Background Postoperative air leak is a common problem in patients undergoing pulmonary resections. A conventional fissure dissection technique during videothoracoscopic lobectomy, particularly in patients with fused fissures is very likely to result in parenchymal damage and prolonged air leak (PAL). In contrast, fissureless video-assisted thoracoscopic surgery (VATS) lobectomy may have advantages regarding PAL and hospital stay. Methods We conducted a retrospective study consisting of 103 consecutive patients who underwent a VATS lobectomy either with a conventional or fissureless technique and statistically analyzed the results particularly with respect to PAL, chest tube duration (CTD), and length of hospital stay (LOS). Results We had 21 (20.4%) cases with PAL. Gender (p = 0.009), histological size of tumor (p = 0.003), and surgical technique (p = 0.009) showed statistically significant differences for PAL in contingency tables. Significant predictors for PAL in univariate analysis were male sex (p = 0.017), histological size of tumor more than 24 mm (p = 0.005), and conventional technique (p = 0.017). Similarly, multivariate analysis revealed male sex (p = 0.036), histological size of tumor more than 24 mm (p = 0.043), and conventional technique (p = 0.029) as significant predictors for PAL. In addition, both the medians of CTD (p = 0.015) and LOS (p = 0.005) were comparably lesser as 3 days, in patients who underwent fissureless videothoracoscopic lobectomy. Conclusion The fissureless technique helps for PAL in patients undergoing videothoracoscopic lobectomy.

Purpose: The aim of this study was to evaluate the potential role of [ ¹⁸ F]FDG positron emission tomography/computed tomography (PET/CT) in the characterization of thymic epithelial tumors (TETs). Materials and Methods: A total of 73 patients who underwent preoperative [ ¹⁸ F]FDG PET/CT were included in this study. Visual total score (VTS), maximum standard uptake values (SUV _max ), metabolic tumor volume (MTV), total lesion glycolysis (TLG), and heterogeneity index (HI) parameters were analyzed to investigate the prediction of histopathologic grade and advanced stage. Results: The cohort included 26 patients with low-grade thymoma (LGT), 36 patients with high-grade thymoma (HGT), and 11 patients with thymic carcinoma (TC). Ninety-one percent of TC had VTS >2, whereas 31% of LGT and 75% of HGT had VTS >2. SUV _max , MTV, and TLG were statistically significantly higher in the TC group than in both thymoma and HGT. Using the cutoff value of 7.25 for SUV _max , TC was differentiated from thymomas with 91% sensitivity and 74% specificity. TC had significantly lower HI values than thymomas. HI parameters showed good diagnostic ability to differentiate TC from thymoma and TC from HGT. SUV _max , MTV, and TLG were significantly higher in advanced-stage disease than in early-stage disease. Conclusions: Visual and quantitative parameters can reliably predict both advanced disease and the grade of primary tumor in TETs. Therefore, as a promising metabolic imaging method, [ ¹⁸ F]FDG PET/CT makes important contributions to preoperative evaluation in routine clinical practice.

Background: Pulmonary sarcomatoid carcinomas are very rare lung neoplasms, and no consensus exists regarding their optimal treatment. The prognosis of sarcomatoid carcinomas has been reported to be unfavorable compared with non-small-cell lung cancers; however, prognostic factors in patients undergoing surgery for sarcomatoid carcinomas remain unclear. Aims: To analyze clinicopathologic prognostic factors and survival outcomes in patients who underwent surgery for pulmonary sarcomatoid carcinoma. Study Design: Retrospective cross-sectional study. Methods: We designed a retrospective cross-sectional study in patients who underwent surgery for pulmonary sarcomatoid carcinomas and statistically analyzed the prognostic factors regarding clinicopathologic features with respect to survival outcomes. Results: We had a total of 44 patients with sarcomatoid carcinoma who had pulmonary resection. Sex distribution was 34 (77%) males and 10 (22.7%) females, which was determined by declaration. The mean age of patients was 57.3 +/- 16 years with a median of 60 years. The 5-year progression-free survival and overall survival rates were 59% and 58%, respectively. The 5-year survival rates were significantly different for tumors > 5 cm (P = 0.044), tumorstatus (T) (P=0.016), lymph node status (N) (P=0.005), and pathologic tumor-node-metastasis (TNM) stage (P = 0.0001). However, histologic subtype (P = 0.628) and adjuvant treatment (P = 0.804) did not have any significant effect on survival. Similarly, the significant prognostic factors in univariate analysis were tumor size (P = 0.085), T status (P = 0.005), N status (P = 0.028), and pathologic TNM stage (P = 0.0001). Multivariate analysis showed only T status (P = 0.058), N status (P = 0.018), and pathologic TNM stage (P = 0.019) as independent prognostic factors, with statistical power of 87%, 43.1% and 21.2%. Conclusion: Surgery appears to be an optimal treatment with favorable outcomes for patients with pulmonary sarcomatoid carcinoma. Patients with small tumors at earlier stages are very likely to benefit from surgery, regardless of histologic subtype.

Background: Lung cancer is a leading cause of cancer-related mortality worldwide. Accurate staging of mediastinal lymph nodes is a crucial step in determining appropriate treatment approaches. Current noninvasive diagnostic methods do not provide sufficient accuracy to confidently decide on surgery without histological confirmation. Our study aimed to develop a artificial intelligence model for the precise prediction of N2 lymph node metastasis. Methods: We retrospectively analyzed 1489 patients who underwent standard cervical mediastinoscopy at our department, including 472 patients diagnosed with non-small cell lung cancer. We developed three distinct prediction models for N2 lymph node station metastasis: one using standard statistical analysis, another utilizing an image processing deep learning algorithm with thoracic CT, and the third employing various machine learning methods with clinicopathological and radiological data. We compared diagnostic accuracy, area under the curve (AUC), sensitivity, and specificity rates, as well as the F1-score of all models. Results: Linear discriminant analysis, quadratic discriminant analysis, Gaussian naive Bayes, and artificial neural networks all surpassed 90% accuracy. The linear support vector machine demonstrated the highest performance, with an accuracy of 95.7%, an AUC of 93.5%, and an F1-score of 92%, respectively and outperformed the logistic regression-based statistical model, which reached an accuracy of 90.6% and an AUC of 85.7%. Conclusion: Machine learning models outperformed standard statistical analysis models in predicting N2 lymph node metastasis. Implementing these machine learning prediction models might greatly improve the accuracy of mediastinal lymph node metastasis detection, thereby enhancing clinical decision making and patient outcomes.