Background: The purpose of this study is to develop a radiomics approach to predict brain metastasis (BM) for stage III/IV ALK-positive non-small cell lung cancer (NSCLC) patients. Methods: Patients with ALK-positive III/IV NSCLC from 2014 to 2017 were enrolled retrospectively. Their pretreatment thoracic CT images were collected, and the gross tumor volume (GTV) was defined by two experienced radiation oncologists. An in-house feature extraction code-set was performed based on MATLAB 2015b (Mathworks, Natick, MA, USA) in patients’ CT images to extract features. Patients were randomly divided into training set and test set (4:1) by using createDataPartition function in caret package. A test-retest in RIDER NSCLC dataset was performed to identify stable radiomics features. LASSO Cox regression and a leave-one-out cross-validation were conducted to identify optimal features for the logistic regression model to evaluate the predictive value of radiomics feature(s) for BM. Furthermore, extended validation for the radiomics feature(s) and Cox regression analyses which combined radiomics feature(s) and treatment elements were implemented to predict the risk of BM during follow-up. Results: In total, 132 patients were included, among which 27 patients had pretreatment BM. The median follow-up time was 11.8 (range, 0.1–65.2) months. In the training set, one radiomics feature (W_GLCM_LH_Correlation) showed discrimination ability of BM (P value =0.014, AUC =0.687, 95% CI: 0.551–0.824, specificity =83.5%, sensitivity =57.1%). It also exhibited reposeful performance in the test set (AUC =0.642, 95% CI: 0.501–0.783, specificity =60.0%, sensitivity =83.3%). Those 105 patients without pretreatment BM were divided into stage III (n=57) and stage IV (n=48) groups. The radiomics feature (W_GLCM_LH_Correlation) had moderate performance to predict BM during/after treatment in separate groups (stage III: AUC =0.682, 95% CI: 0.537–0.826, specificity =64.4%, sensitivity =75.0%; stage IV: AUC =0.653, 95% CI: 0.503–0.804, specificity =70.4%, sensitivity =75.0%). Meanwhile, stage III patients could be divided into low risk and high risk groups for BM during surveillance according to Cox regression analysis (log-rank P value =0.021). Conclusions: We identified one wavelet texture feature derived from pretreatment thoracic CT that presented potential in predicting BM in stage III/IV ALK-positive NSCLC patients. This could be beneficial to risk stratification for such patients. Further investigation is necessary to include expanded sample size investigation and external multicenter validation.

Background: The aim of this study was to develop and validate reliable nomograms to predict individual overall survival (OS) and cancer-specific survival (CSS) for patients with primary tracheal tumors and further estimate the role of postoperative radiotherapy (PORT) for these entities. Patients and methods: A total of 405 eligible patients diagnosed between 1988 and 2015 were selected from the Surveillance, Epidemiology, and End Results database. All of them were randomly divided into training (n=303) and validation (n=102) sets. For the purpose of establishing nomograms, the Akaike information criterion was employed to select significant prognostic factors in multivariate Cox regression models. Both internal and external validations of the nomograms were evaluated by Harrell’s concordance index (C-index) and calibration plots. Propensity score matching (PSM) method was performed to reduce the influence of selection bias between the PORT group and the non-PORT group. Results: Two nomograms shared common variables including age at diagnosis, histology, N and M stages, tumor size, and treatment types, while gender was only incorporated in the CSS nomogram. The C-indices of OS and CSS nomograms were 0.817 and 0.813, displaying considerable predictive accuracy. The calibration curves indicated consistency between the nomograms and the actual observations. When the nomograms were applied to the validation set, the results remained reconcilable. Moreover, the nomograms showed superiority over the Bhattacharyya’s staging system with regard to the C-indices. After PSM, PORT was not associated with significantly better OS or CSS. Only squamous cell carcinoma (SCC) patients in the PORT group had improved OS compared to non-PORT group. Conclusion: The first two nomograms for predicting survival in patients with primary tracheal tumors were proposed in the present study. PORT seems to improve the prognosis of SCC patients, which needs further exploration.

Background The prognostic value of surgery and postoperative radiotherapy (PORT) for primary thymic neuroendocrine tumors (TNETs) was estimated using the SEER database. Methods This retrospective study used SEER data of TNET patients between 1998 and 2015. Propensity score matching (PSM) was performed according to whether surgery was performed. The prognostic effects on overall survival (OS) and cancer-specific survival (CSS) were evaluated using multivariate Cox regression. Results A total of 3947 patients were included: 293 (7.4%) TNET, 2788 (70.6%) thymoma, and 866 (21.9%) thymic carcinoma. Compared to other subtypes, TNET patients were younger, included a larger proportion of men, had a well or moderately differentiated histological grade, higher disease stage at diagnosis, and were more likely to have regional lymph node metastasis. The median OS and CSS for TNET were 82.9 (95% confidence interval 74.3–91.4) and 101.9 (95% confidence interval 91.9–111.8) months, respectively, significantly shorter than for thymomas. In the matched cohort of TNET patients, multivariate analysis of OS and CSS revealed a significantly poorer prognosis in the non-surgery group (P < 0.001). Compared to total/radical resection, TNET patients who underwent debulking resection had significantly inferior outcomes (P < 0.05). Postoperative radiotherapy favorably impacted OS and CSS in Masaoka–Koga stage III–IV TNET patients; this OS impact was also observed in stage IIB patients. Conclusion TNETs are extremely rare with relatively dismal outcomes. This analysis revealed the role of complete surgical resection and the favorable effect of postoperative radiotherapy in specific TNET subgroups.

Background: Adjuvant chemotherapy (ACT) is routinely the recommended treatment for patients with advanced non-small cell lung cancer (NSCLC) but remains a controversial option in stage IB patients. We therefore pooled the current evidence to determine the prognostic impact of ACT in stage IB NSCLC patients in the context of the eighth tumor, node, metastasis (TNM) staging system. Methods: Five electronic databases were searched for eligible studies up to December 2020 without language restrictions. The primary and secondary outcomes were overall survival (OS) and disease-free survival (DFS). Search results were filtered by a set of eligibility criteria and analyzed in line with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The risk of bias was assessed independently using a modified set. Stata 16.0 was used for general data analysis and meta-analysis, and subgroup analyses were performed to investigate the source of interstudy heterogeneity. Results: In all, 12 eligible studies were identified and 15,678 patients included. Our results demonstrated that ACT was associated with improved OS [n=11; hazard ratio (HR) =0.65; 95% confidence interval (CI): 0.60–0.70; P<0.001; I²=33.4%, P=0.131] and DFS (n=9; HR =0.73; 95% CI: 0.63–0.83; P<0.001; I²=66.7%, P=0.002) in stage IB NSCLC patients. Subgroup analysis by histology indicated that administration of ACT conferred more favorable survival to both stage IB squamous cell carcinoma (n=1; HR =0.56; 95% CI: 0.28–0.84; P<0.001) and adenocarcinoma (n=6; HR =0.59; 95% CI: 0.47–0.71; P<0.001; I²=31.0%, P=0.203). Meanwhile, both platinum-based ACT (n=7; HR =0.62; 95% CI: 0.51–0.74; P<0.001; I²=44.8%, P=0.093) and other regimens (n=2; HR =0.66; 95% CI: 0.61–0.72; P<0.001; I²=0.7%, P=0.316) could benefit patients with stage IB disease. Discussion: ACT might provide survival benefits to patients with stage IB NSCLC irrespective of histology or regimens. Patient selection and time trend biases were inevitable due to the limitation of retrospective studies. More prospective studies should be initiated to investigate the optimal ACT regimens in different histologic types in stage IB NSCLC patients.

Background: Lower thoracic esophageal cancer (LTEC) with celiac node metastasis and upper thoracic esophageal cancer (UTEC) with supraclavicular node metastasis were previously categorized as M1a diseases. Our study aimed to investigate whether the clinical significance of supraclavicular and celiac lymph node metastasis should be reevaluated in thoracic esophageal cancer.Methods: A total of 6178 patients with thoracic esophageal cancer were identified from the Surveillance, Epidemiology, and End Results (SEER) database during 2004–2015. Treatment strategies and outcomes (OS, overall survival; CSS, cancer‐specific survival) of patients with different nodal status were reviewed. The Cox proportional hazards regression model was applied to evaluate the prognostic factors. Statistical analyses were performed in all subgroups.Results: Multivariate analysis identified supraclavicular node metastasis but not celiac node metastasis as an independent predictor of both OS and CSS in LTEC. However, metastasis to supraclavicular or celiac nodes was not an independent predictor of OS and CSS in UTEC. Surgery was not associated with increased OS and CSS for UTEC with celiac or supraclavicular node metastasis but was favored as a predictor of better OS and CSS for LTEC with celiac or supraclavicular node metastasis. Radiotherapy benefited OS and CSS in LTEC involving celiac or supraclavicular nodes and in UTEC involving celiac nodes, while only OS benefited from radiotherapy in UTEC involving supraclavicular nodes.Conclusions: These results provide preliminary evidence that the clinical significance of supraclavicular and celiac lymph node metastasis should be reevaluated in thoracic esophageal cancer with different prognostic information according to the primary sites.