Construction of the prognostic model for small‐cell lung cancer based on inflammatory markers: A real‐world study of 612 cases with eastern cooperative oncology group performance score 0–1

Abstract Objectives This research aimed to explore the relationship between pre‐treatment inflammatory markers and other clinical characteristics and the survival of small‐cell lung cancer (SCLC) patients who received first‐line platinum‐based treatment and to construct nomograms for predicting overall survival (OS) and progression‐free survival (PFS). Methods A total of 612 patients diagnosed with SCLC between March 2008 and August 2021 were randomly divided into two cohorts: a training cohort (n = 459) and a validation cohort (n = 153). Inflammatory markers, clinicopathological factors, and follow‐up information of patients were collected for each case. Cox regression was used to conduct univariate and multivariate analyses and the independent prognostic factors were adopted to develop the nomograms. Harrell's concordance index (C‐index) and time‐dependent receiver operating characteristic curve were used to verify model differentiation, calibration curve was used to verify consistency, and decision curve analysis was used to verify the clinical application value. Results Our results showed that baseline C‐reactive protein/albumin ratio, neutrophil/lymphocyte ratio, NSE level, hyponatremia, the efficacy of first‐line chemotherapy, and stage were independent prognostic factors for both OS and PFS in SCLC. In the training cohort, the C‐index of PFS and OS was 0.698 and 0.666, respectively. In the validation cohort, the C‐index of PFS and OS was 0.727 and 0.747, respectively. The nomograms showed good predictability and high clinical value. Also, our new clinical models were superior to the US Veterans Administration Lung Study Group (VALG) staging for predicting the prognosis of SCLC. Conclusions The two prognostic nomograms of SCLC including inflammatory markers, VALG stage, and other clinicopathological factors had good predictive value and could individually assess the survival of patients.