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Objective. To establish and validate a nomogram to predict the overall survival (OS) and cancer-specific survival (CSS) in patients with nasopharyngeal carcinoma (NPC) receiving radiotherapy by integrating multiple independent prognostic factors. Materials and Methods. Data from 5663 patients with NPC who received definite radiotherapy between 2004 and 2018 were included and divided into training and validation cohorts. Univariate and multivariate Cox regression analyses were performed to determine the independent prognostic factors of patients with NPC after radiotherapy. Thereafter, the predictive accuracy of the nomogram model was evaluated. Results. Age, race, marital status, pathological type, tumor size, T stage, N stage, M stage, American Joint Committee on Cancer stage, and chemotherapy were independent factors affecting the prognosis of patients with NPC receiving radiotherapy. Nomograms with a concordance index of 0.726 (95% confidence interval (CI): 0.675–0.777) and 0.732 (95% CI: 0.680–0.785) were able to predict OS and CSS, respectively. The area under the curve showed excellent predictive performance. Additionally, the calibration curve indicated that the predicted survival rate was consistent with the actual survival rate, and the decision curve indicated its clinical value. The established risk stratification system was able to accurately stratify patients receiving radiotherapy for NPC into three risk subgroups with significant differences in prognosis (P < 0.05). Conclusions. The constructed nomogram had good prognostic performance and could be used as an effective tool to evaluate the prognosis of patients with NPC after radiotherapy. This nomogram could be further used to guide clinical decisions and personalized treatment plans. [ABSTRACT FROM AUTHOR]

Highlights: [•] Plasma phospholipid fatty acid profile for PCOS patients with and without IR has been characterized. [•] An GC–MS followed by multivariate statistical analysis was employed. [•] C24:1 n-9 and C20:3 n-6 were identified as the potential FA biomarkers of PCOS and its IR, respectively. [•] GC–MS-based FA profile might provide a complementary approach for diagnosis of PCOS and its IR. [ABSTRACT FROM AUTHOR]