Pulmonary sarcomatoid carcinoma (PSC) is a group of five rare non-small cell lung cancer subtypes. In the present study, the clinical characteristics and outcomes of patients with PSC registered in the Surveillance, Epidemiology and End Results (SEER) database were investigated. For this purpose, data for patients with PSC (n=1,723) who received their initial diagnosis between 1988 and 2016 were collected from the SEER database. Survival analysis was performed using the Kaplan-Meier curves and the log-rank test. Subsequently, multivariate analyses with the Cox proportional hazards model were used to identify significant independent predictors. A nomogram model was established to predict survival performance using the concordance index (C-index). From the total cohort, patients with pulmonary blastoma demonstrated improved 1-year overall survival (OS) rate compared with other pathological types (P<0.001). The 2-year overall survival rates of the 'only radiotherapy' cohort and the 'no specific treatment' cohort were 9.1 and 5.4% (P<0.001), respectively. Radiotherapy significantly improved the OS rate in stage I-III patients with PSC (P<0.001) when stratified by stage. After matching the propensity scores, the 'surgery combined with radiotherapy' group comprised 156 patients and the 'surgery-only' group had 247 patients (1:1.6). However, no significant differences in prognosis were found between the 2 subgroups (P= 0.052). The multivariate Cox analysis demonstrated that older age (≥76 years old), male, unmarried, pathological type, larger tumor size (≥56 mm), later tumor node metastasis stages and treatment modalities were independent prognostic factors. A nomogram model was established to predict the survival of patients with PSC. This model incorporated the seven aforementioned independent prognostic factors (C-index for survival, 0.75; 95% confidence interval, 0.74-0.76). Radiotherapy needs to considered for stage I-III patients with PSC who undergo radiation therapy without surgical resection.

The aim of this study was to investigate the prognostic value of the prognostic nutritional index (PNI) on the long-term survival of non-small cell lung cancer (NSCLC) patients who received platinum-based chemotherapy. Data on nutritional parameters and clinicopathological characteristics [e.g., albumin, total protein, body mass index (BMI), eastern cooperative oncology group (ECOG) performance status, stage, pathology, treatment strategy] were analyzed and retrospectively correlated with overall survival (OS). The PNI was calculated based on the concentration of albumin and lymphocyte count [10 × albumin, (g/dl) + 0.005 × lymphocyte (count/mm 3)]. A receiver operating characteristic curve (ROC) analysis was used to find the optimal cutoff value of PNI. Univariate and multivariate analyses were used to evaluate the prognostic value of PNI. A total of 186 patients met the inclusion criteria. The optimal cutoff value for PNI was 50.45. Compared with the parameters of the low PNI group (n=76), high PNI was significantly associated with adenocarcinoma type, stage III, better ECOG and comprehensive treatment modality. The univariate analysis demonstrated that OS was superior when PNI ≥50.45, albumin ≥35 g/l, platelet-lymphocyte ratio (PLR) ≥163 and ECOG <2, and when the patient received a comprehensive treatment modality. In the multivariate analysis, PNI, TNM stage and treatment strategy were identified as independent predictors of survival in this study. This retrospective study demonstrated that a low PNI was related to worse overall survival in patients with stage III/IV NSCLC who received platinum-based chemotherapy. These data provided a conceptual basis for further research on the clinical application of the PNI index for patients receiving chemotherapy for intermediateand advanced-stage NSCLC.

Purpose: To develop and validate a predictive combined model for metastasis in patients with clear cell renal cell carcinoma (ccRCC) by integrating multimodal data. Materials and Methods: In this retrospective study, the clinical and imaging data (CT and ultrasound) of patients with ccRCC confirmed by pathology from three tertiary hospitals in different regions were collected from January 2013 to January 2023. We developed three models, including a clinical model, a radiomics model, and a combined model. The performance of the model was determined based on its discriminative power and clinical utility. The evaluation indicators included AUC value, accuracy, sensitivity, specificity, negative predictive value, positive predictive value and DCA(Decision Curve Analysis) curve. Results:A total of 251 patients were evaluated. Patients (n=166) from Shandong University Qilu Hospital (Jinan) were divided into the training cohort, of which 50 patients developed metastases; patients (n=37) from Shandong University Qilu Hospital (Qingdao) were used as testing set 1, of which 15 patients developed metastases; patients (n=48) from Changzhou Second People's Hospital were used as testing set 2, of which 13 patients developed metastases. In the training set, the combined model showed the highest performance (area under the receiver operating characteristic curve [AUC], 0.924) in predicting lymph node metastasis, while the clinical and radiomics models both had AUCs of 0.875 and 0.870, respectively. In the testing set 1, the combined model had the highest performance (AUC, 0.877) for predicting lymph node metastasis, while the AUCs of the clinical and radiomics models were 0.726 and 0.836, respectively. In the testing set 2, the combined model had the highest performance (AUC, 0.849) for predicting lymph node metastasis, while the AUCs of the clinical and radiomics models were 0.708 and 0.804, respectively. The DCA curve showed that the combined model had a significant prediction probability in predicting the risk of lymph node metastasis in ccRCC patients compared with the clinical model or the radiomics model. Conclusion: The combined model was superior to the clinical and radiomics models in predicting lymph node metastasis in ccRCC patients.