Development and validation of multidimensional nomograms for predicting prostate cancer risk: a retrospective study
-
By
-
Tao Zhang
-
Xue Li
-
Junsong Zeng
-
Maosen Xu
-
Yan Tie
-
June 30, 2026
-
Clinical Scorecard: Creation and assessment of comprehensive nomograms for estimating prostate cancer risk: a retrospective analysis
At a Glance
| Category | Detail |
|---|
| Condition | Prostate Cancer Risk Assessment |
| Key Mechanisms | Integration of metabolic and inflammatory indicators with PSA levels |
| Target Population | Men with tPSA >10 ng/mL |
| Care Setting | Retrospective analysis in a clinical setting |
Key Highlights
- Two logistic regression models developed for predicting prostate cancer and high-grade disease.
- Model 1 achieved a validation AUC of 0.871; Model 2 achieved 0.779.
- Key variables included age, TyG index, NLR, and fPSA%.
- Models outperformed fPSA% alone in risk stratification.
- 54.7% of patients had prostate cancer, with 55.2% of those being high-grade.
Guideline-Based Recommendations
Diagnosis
- Use of total PSA and free PSA percentage as reference indicators.
Management
- Consider additional metabolic and inflammatory indicators for biopsy decisions.
Monitoring & Follow-up
- Regular assessment of PSA levels and associated metabolic indicators.
Risks
- Increased risk of prostate cancer associated with obesity and metabolic disorders.
Patient & Prescribing Data
Men undergoing prostate biopsy with elevated tPSA levels.
Nomograms can assist in reducing unnecessary biopsies.
Clinical Best Practices
- Incorporate metabolic and inflammatory markers in prostate cancer risk assessment.
- Utilize logistic regression models for improved diagnostic accuracy.
Related Resources & Content
