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Development and validation of multidimensional nomograms for predicting prostate cancer risk: a retrospective study
Tao Zhang1, Xue Li1, Junsong Zeng2
1Department of Biotherapy, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China.
Frontiers in Oncology
|July 15, 2026
Summary
New models integrating clinical and metabolic factors improve prostate cancer risk prediction in men with elevated PSA (prostate-specific antigen) levels. These tools can help reduce unnecessary prostate biopsies.
Area of Science:
- Urology
- Oncology
- Biostatistics
Background:
- Prostate-specific antigen (PSA) screening lacks specificity for prostate cancer detection in the 10-20 ng/mL range, complicating biopsy decisions.
- Metabolic disorders and inflammation markers show potential for enhancing prostate cancer risk prediction.
- Current risk stratification models need improvement for men with total PSA (tPSA) >10 ng/mL.
Purpose of the Study:
- To develop and validate predictive models for prostate cancer and high-grade prostate cancer in men with tPSA >10 ng/mL.
- To integrate multidimensional indicators, including metabolic and inflammatory markers, into risk prediction models.
- To compare the performance of developed models against free PSA percentage (fPSA%) alone.
Main Methods:
- Retrospective analysis of 461 men with tPSA >10 ng/mL undergoing prostate biopsy.
- Development of two logistic regression models: one for benign vs. malignant (Model 1) and one for high-grade vs. low-grade cancer (Model 2).
- Model performance evaluated using Area Under the Curve (AUC), calibration, and Decision Curve Analysis (DCA), compared to fPSA% using the DeLong test.
Main Results:
- The study identified 252 prostate cancer cases (54.7%) and 139 high-grade cases (55.2% of malignant).
- Malignant cases were associated with older age, higher BMI, TyG index, NLR, and tPSA, and lower fPSA%.
- Model 1 (cancer prediction) achieved a validation AUC of 0.871, and Model 2 (high-grade prediction) achieved 0.779, both outperforming fPSA% alone.
Conclusions:
- Developed nomograms incorporating routine clinical and laboratory variables effectively stratify risk for prostate cancer and high-grade disease in men with tPSA >10 ng/mL.
- These models demonstrate superior performance compared to fPSA% alone, potentially reducing unnecessary biopsies.
- The findings support the use of integrated models for improved decision-making in prostate cancer screening.
