Development and Validation of a Clinlabomics-Based Nomogram for Predicting the Prognosis of Small Cell Lung Cancer in
1Department of Clinical Laboratory, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, China.
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 internal validation set, while 80 samples from an alternate institution were allocated as the external independent validation set. Utilizing the log-rank test, univariate and multivariate Cox regression analyses, six prognostic indicators were discerned. A nomogram was subsequently developed based on these identified indicators.
Results:
Through the log-rank test, univariate and multivariate Cox regression analyses, total protein (TP) (HR = 0.47, p < 0.001), aspartate aminotransferase (AST) (HR = 1.82, p < 0.001), and lymphocyte ratio (Lym ratio) (HR = 0.47, p = 0.005) were identified as laboratory biomarkers related to prognosis from 61 blood-related laboratory tests, covering routine blood, biochemical, coagulation, and infectious disease markers, while age, stage, and smoking were identified as clinical independent prognostic factors. A nomogram was developed based on these six indicators. The AUC of time-independent ROC for 2- 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:
The clinlabomics-based nomogram helps to more effectively predict the prognosis of small cell lung cancer by leveraging blood laboratory data.
