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

Abstract

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.

A novel risk classifier for predicting the overall survival of patients with thymic epithelial tumors based on the eighth edition of the TNM staging system: A population-based study

“…Nowadays, the nomogram is a widely used predictive tool to predict the survival probability of cancer patients ( 8 , 9 ). It could easily visualize the risk of each patient according to the contribution to the study outcome of variables in the multivariate analysis.…”

“…Nomogram is a visual multivariate prognostic model that contains more predictors than traditional staging systems, thereby allowing individualized risk estimation. Previous publications revealed that nomogram has promising performance in predicting the survival probability of some malignancies compared to traditional TNM staging system (8,9). To our knowledge, several nomograms were developed to predict the recurrence risk of TETs in the past few years (10)(11)(12).…”

Role of artificial intelligence in risk prediction, prognostication, and therapy response assessment in colorectal cancer: current state and future directions

“…Skrede et al used CNN algorithms to stratify CRC patients based on survival rate to identify those patients who would likely not benefit from adjuvant chemotherapy versus those patients who would require such treatment (76). Similarly, another CNN algorithm developed by Jiang et al could predict disease recurrence risk and overall survival for stage III CRC using gradient boosting (74). A CNN algorithm was also used to predict survival based on stromal microenvironment data obtained from HE slides (77).…”

“…Prognostic predictors are valuable tools for treatment decision-making by helping clinicians choose the most suitable treatment modality for each patient ( 74 , 75 ). Survival prediction may be particularly valuable in early-stage CRC, as it can help clinicians decide whether adjuvant chemotherapy is suitable or not.…”

“…Similarly, another CNN algorithm developed by Jiang et al. could predict disease recurrence risk and overall survival for stage III CRC using gradient boosting ( 74 ). A CNN algorithm was also used to predict survival based on stromal microenvironment data obtained from HE slides ( 77 ).…”