Development and validation of a nomogram for the prediction of brain metastases in small cell lung cancer

INTRODUCTION: The aim was to develop and validate a nomogram for the prediction of brain metastases (BM) in small cell lung cancer (SCLC), to explore the risk factors and assist clinical decision‐making. METHODS: We reviewed the clinical data of SCLC patients between 2015 and 2021. Patients between 2015 and 2019 were included to develop, whereas patients between 2020 and 2021 were used for external validation. Clinical indices were analysed by using the least absolute shrinkage and selection operator (LASSO) logistic regression analyses. The final nomogram was constructed and validated by bootstrap resampling. RESULTS: A total of 631 SCLC patients between 2015 and 2019 were included to construct model. Gender, T stage, N stage, Eastern Cooperative Oncology Group (ECOG), haemoglobin (HGB), the absolute value of lymphocyte (LYMPH #), platelet (PLT), retinol‐binding protein (RBP), carcinoembryonic antigen (CEA) and neuron‐specific enolase (NSE) were identified as risk factors and included into the model. The C‐indices were 0.830 and 0.788 in the internal validation by 1000 bootstrap resamples. The calibration plot revealed excellent agreement between the predicted and the actual probability. Decision curve analysis (DCA) showed better net benefits with a wider range of threshold probability (net clinical benefit was 1%–58%). The model was further externally validated in patients between 2020 and 2021 with a C‐index of 0.818. CONCLUSIONS: We developed and validated a nomogram to predict the risk of BM in SCLC patients, which could help clinicians to rationally schedule follow‐ups and promptly implement interventions.