Development and validation of a Clinlabomics based nomogram for predicting the prognosis of small cell lung cancer in China: a multicenter, retrospective cohort study

Abstract

Background and Objective Small cell lung cancer has a high incidence and mortality rate, frequently metastasizes, and is associated with a poor prognosis. However, traditional prognostic models based on stage alone cannot meet clinical needs. This study aims to establish a clinlabomics based, highly accessible prognostic model for small cell lung cancer Methods We conducted a multicenter observational retrospective study, enrolling clinical laboratory data of 276 small cell lung cancer patients. The cohort from Sichuan Cancer Hospital comprised a total of 196 samples. Of these, 88 samples were designated as the independent internal validation set, while 80 samples from an alternate institution were allocated as the external validation set. Utilizing univariate and multivariate Cox regression analyses, six prognostic indicators were discerned. A nomogram was subsequently developed based on these identified indicators. Results The analysis identified three clinlabomic biomarkers—Total Protein (TP), Aspartate Aminotransferase (AST), and Lymphocyte Ratio (Lym Ratio)—as well as three clinical indicators—Age, Stage, and Smoking History—as independent prognostic factors. Nomogram was developed based on these six indicators. The AUC of time independent ROC for 2-year and 3-year Overall survival (OS) was 0.74, 0.74 in the training cohort, and 0.64, 0.74 in the validation cohort, respectively. The novel nomogram accurately predicted the prognosis for two independent cohorts with p-values < 0.001, and performed risk adjustment, which classified patients with different OS at the same extensive stage (ES) or limited-stage (LS) . Conclusions Clinlabomics-based nomogram accurately predicts small cell lung cancer prognosis by leveraging blood laboratory data.