Improving the screening ability of neuron-specific enolase on small cell lung cancer

Abstract

Neuron-specific enolase (NSE) is one of the most common biomarkers of small cell lung cancer (SCLC) and is widely used in lung cancer screening. But its specificity is affected by many factors. Using residual correction and machine learning, corrected NSE and its reference range were constructed based on metabolic factors and smoking history affecting NSE in the training set of 48,009 healthy individuals recruited from the First Affiliated Hospital of Nanjing Medical University. External validation including additional 64,553 healthy subjects and 105 SCLC patients were enrolled to evaluate the efficacy of NSE_corrected for SCLC screening. The reference range of NSE_corrected could significantly improve the specificity of NSE for SCLC and reduce false positives. In the external validation set, NSE_corrected increased the specificity from 85.71 % to 97.09 %(P < 0.0001), and reduced the false positive rate from 14.26 % to 2.91 %(P < 0.0001). ROC curve, calibration curve and decision analysis curve also showed that NSE_corrected had better screening performance. The calculation of NSE_corrected was converted into an online R-based app for more convenient use. NSE_corrected can improve the screening effect of SCLC, reduce the false positive rate, and is more suitable for large population screening and optimize the allocation of lung cancer resources.