Machine Learning-Assisted Prediction of Varicocele Grade Using Multidimensional Spermatic Vein Reflux Time Analysis.

BACKGROUND: Varicocele (VC) grading has long relied on qualitative assessments of venous diameter and reflux signals, lacking standardization. OBJECTIVES: This study aimed to explore the value of spermatic vein reflux time (SVRT) as a quantitative indicator for VC grading and to enhance diagnostic accuracy using machine learning (ML) models. MATERIALS AND METHODS: A total of 3855 VC patients were enrolled, with color Doppler ultrasound used to measure SVRT, venous diameter, and testicular hemodynamic parameters (peak systolic velocity [PSV], end-diastolic velocity [EDV], and resistance index [RI]). VC grades were defined using an institutional clinical grading protocol based on a dual-index grading standard (venous diameter + SVRT). Statistical analyses included one-way ANOVA, Spearman correlation, and receiver operating characteristic (ROC) curve analysis. Three ML models (Random Forest, XGBoost, and Logistic Regression) were developed using clinical and ultrasound features; two XGBoost versions (with/without SVRT) were designed to rule out circular reasoning. SHAP analysis was applied to interpret feature contributions. RESULTS: SVRT increased significantly with VC grade (left side: Grade I, 2.85 ± 1.12 s; Grade II, 5.28 ± 0.92 s; Grade III, 7.61 ± 1.32 s; all p < 0.001) and showed significant correlations with venous diameter (r = 0.389-0.426) and reflux velocity (r = 0.478-0.512, all p < 0.001). ROC analysis demonstrated SVRT's diagnostic efficacy (AUC = 0.826-0.935) and derived grade-specific cutoffs (Grade I: 2.30 s; Grade II: 4.50 s; Grade III: 6.20 s). XGBoost (with SVRT) achieved the highest performance: overall accuracy 89.2% (95% CI: 0.881-0.903), Macro-F1 0.882, and AUC = 0.941 (95% CI: 0.925-0.957) for Grade III, outperforming the traditional SVRT-based ROC (AUC = 0.935, 95% CI: 0.922-0.948; DeLong test: Z = 2.13, p = 0.03) and the SVRT-excluded model (accuracy 80.4%, 95% CI: 0.783-0.825; Grade III AUC = 0.872, 95% CI: 0.844-0.899). SHAP analysis identified left SVRT (mean absolute SHAP value = 0.23), left venous diameter (0.19), and left EDV (0.15) as key predictive features. For SVRT regression, the Random Forest model showed high accuracy (R2 = 0.820, 95% CI: 0.802-0.838; RMSE = 0.76 ± 0.12 s; MAE = 0.58 ± 0.09 s), with minimal error for severe VC cases (SVRT > 6.2 s, MAE = 0.41 ± 0.07 s). DISCUSSION AND CONCLUSION: These findings support SVRT as a quantitative reference for VC severity grading, and the developed ML models may assist in standardizing VC grading by integrating multidimensional features, with the potential to reduce inter-observer variability and borderline case misclassification. However, the absence of clinical outcome data means these tools have been validated only against grading criteria, not against patient-relevant endpoints; prospective studies with outcome data are needed before clinical adoption.