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

Area of Science:

• Urology
• Medical Imaging
• Machine Learning in Medicine

Background:

• Varicocele (VC) grading traditionally relies on subjective qualitative assessments of venous diameter and reflux.
• Lack of standardization in current VC grading methods leads to diagnostic inconsistencies.

Purpose of the Study:

• To evaluate spermatic vein reflux time (SVRT) as a quantitative indicator for varicocele grading.
• To enhance diagnostic accuracy for varicocele using machine learning (ML) models.

Main Methods:

• Utilized color Doppler ultrasound to measure SVRT, venous diameter, and hemodynamic parameters in 3855 VC patients.
• Developed and compared ML models (Random Forest, XGBoost, Logistic Regression) incorporating clinical and ultrasound features, including SVRT.
• Employed SHAP analysis to interpret feature importance and ROC analysis for diagnostic efficacy.

Main Results:

• SVRT significantly correlated with varicocele grade and venous diameter.
• XGBoost model integrating SVRT achieved high accuracy (89.2%) and AUC (0.941) for Grade III varicocele.
• SVRT demonstrated diagnostic efficacy (AUC 0.826-0.935) with derived grade-specific cutoffs.

Conclusions:

• SVRT serves as a reliable quantitative reference for varicocele severity grading.
• Developed ML models can aid in standardizing VC grading, reducing inter-observer variability and misclassification.
• Prospective studies with clinical outcome data are necessary for full clinical adoption.