Construction and Validation of a Novel Nomogram to Predict the Overall Survival of Patients With Combined Small Cell Lung Cancer: A Surveillance, Epidemiology, and End Results Population-Based Study.

Introduction: Combined small cell lung cancer (C-SCLC) represents a rare subtype of all small cell lung cancer cases, with limited studies investigated its prognostic factors. The aim of this study was to construct a novel nomogram to predict the overall survival (OS) of patients with C-SCLC.

Methods: In this retrospective study, a total of 588 C-SCLC patients were selected from the Surveillance, Epidemiology, and End Results database. The univariate and multivariate Cox analyses were performed to identify optimal prognostic variables and construct the nomogram, with concordance index (C-index), receiver operating characteristic curves, and calibration curves being used to evaluate its discrimination and calibration abilities. Furthermore, decision curve analysis (DCA), integrated discrimination improvement (IDI), and net reclassification index (NRI) were also adopted to assess its clinical utility and predictive ability compared with the classic TNM staging system.

Results: Seven independent predictive factors were identified to construct the nomogram, including T stage, N stage, M stage, brain metastasis, liver metastasis, surgery, and chemotherapy. We observed a higher C-index in both the training (.751) and validation cohorts (.736). The nomogram has higher area under the curve in predicting 6-, 12-, 18-, 24-, and 36-month survival probability of patients with C-SCLC. Meanwhile, the calibration curves also revealed high consistencies between the actual and predicted OS. DCA revealed that the nomogram could provide greater clinical net benefits to these patients. We found that the NRI for 6- and 12-month OS were .196 and .225, and the IDI for 6- and 12-month OS were .217 and .156 in the training group, suggesting that the nomogram can predict a more accurate survival probability. Similar results were also observed in the validation cohort.

Conclusion: We developed and verified a novel nomogram that can help clinicians recognize high-risk patients with C-SCLC and predict their OS.