Primary healthcare-friendly prostate cancer prediction model using routine clinical parameters: a multicenter study

Area of Science:

• Urology
• Oncology
• Medical Informatics

Background:

• Elevated prostate-specific antigen (PSA) levels in men often necessitate prostate biopsy to rule out prostate cancer (PCa).
• Accurate risk stratification is crucial to avoid unnecessary invasive procedures and associated costs.
• Existing risk assessment tools may lack generalizability or applicability in primary care settings.

Purpose of the Study:

• To develop and validate a nomogram model for PCa risk stratification using routinely available parameters.
• To create a tool applicable in primary healthcare settings for optimizing biopsy decisions.
• To reduce the rate of unnecessary prostate biopsies in patients with elevated PSA.

Main Methods:

• A retrospective cohort of 2,844 patients undergoing prostate biopsy was analyzed.
• Patients were randomly assigned to training (70%) and internal validation (30%) sets.
• A nomogram was constructed using independent predictive factors identified via logistic regression and validated internally and externally (n=281).

Main Results:

• The nomogram integrated age, prostate volume, total PSA (tPSA), and free-to-total PSA ratio (f/t PSA).
• The model demonstrated strong discriminative performance with AUCs of 0.816 (training), 0.833 (internal validation), and 0.776 (external validation).
• Calibration curves showed good agreement, and decision curve analysis confirmed clinical utility across various risk thresholds.

Conclusions:

• The developed nomogram is a non-invasive, cost-effective tool for individualized PCa risk assessment in Chinese patients with elevated PSA.
• External validation confirmed the model's generalizability and suitability for primary healthcare settings.
• This tool can optimize biopsy decisions, reduce healthcare burden, and improve PCa management.