Clinical Report: Creation and assessment of comprehensive nomograms for estimating prostate cancer risk
Overview
This study developed two logistic regression models to predict prostate cancer and high-grade disease in men with total PSA levels greater than 10 ng/mL. The models demonstrated improved risk stratification compared to free PSA percentage alone.
Background
Prostate cancer is a prevalent malignancy and a leading cause of cancer-related mortality in men. The limitations of PSA screening, particularly in the 10–20 ng/mL range, necessitate the development of more accurate predictive tools.
Data Highlights
Parameter
Model 1 AUC
Model 2 AUC
High-Grade Cancer Prevalence
Validation
0.871
0.779
55.2%
Key Findings
Among 461 patients, 54.7% were diagnosed with prostate cancer.
High-grade disease was present in 55.2% of malignant cases.
Model 1 included age, TyG index, NLR, fPSA%, smoking, hypertension, and lesion location.
Model 2 included age, TyG index, LDH, and smoking history.
Model 1 achieved a sensitivity of 80.3% and specificity of 74.6%.
Both models provided higher net benefits than fPSA% alone.
Clinical Implications
The developed nomograms may assist in risk stratification for prostate cancer and high-grade disease in patients with elevated PSA levels.
Conclusion
The integration of clinical and laboratory variables into nomograms enhances prostate cancer risk assessment.
Harold Burstein, MD, PhD, and Erica Mayer, MD, MPH discuss results from the SERENA-6 trial, which were presented at the 2026 ESMO Breast Cancer Congress.
