A Simplified Nomogram for Primary Population-Based Screening of Esophageal Cancer: An Internally Validated Shandong...

To develop and validate a user-friendly nomogram for predicting the risk of esophageal squamous cell carcinoma (ESCC) and high-grade intraepithelial neoplasia (HGIN), designed for initial screening settings while addressing variable complexity and class imbalance in traditional models.

Based on a screening cohort of 23,257 participants from Tai'an, Shandong, between 2024 and 2025 (positive rate: 1.54%), a 1:10 case-control sampling method was applied to address the low event rate (positive rate: 1.54%) and correct class imbalance. Predictors were initially screened using LASSO regression with 10-fold cross-validation (λ.min criterion) and further refined via multivariable logistic regression to establish the final model, which was presented as a nomogram and evaluated via internal split-sample validation.

Seven easily accessible predictors were identified: age, sex, education level, BMI, smoking history, hot-food consumption, and family history of esophageal cancer. The model showed strong discriminatory performance, with an AUC of 0.823 (95% CI: 0.798-0.848) in the training set and 0.835 (95% CI: 0.805-0.865) in the internal validation set. Calibration curves indicated high consistency between predicted and observed risks. Decision curve analysis demonstrated net clinical benefit across risk thresholds of 0-0.6.

The proposed simplified nomogram demonstrates promising potential for risk stratification in primary ESCC/HGIN screening. However, prospective external validation in diverse cohorts is necessary before its large-scale clinical implementation.