Related Experiment Video
Updated: Apr 14, 2026
An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Combined predictive model for prostate cancer screening: Development and validation study
Yu Li1,2, Fang Yang3, Xuebin Liu1
1Department of Ultrasound, Beijing Anzhen Hospital of Capital Medical University Nanchong Central Hospital, The Second Clinical Medical College, North Sichuan Medical College, Nan Chong, Sichuan 637000, China.
Background:
Early detection of prostate cancer (PCa) remains challenging, as prostate-specific antigen (PSA) testing and digital rectal examination (DRE) offer limited specificity. Transrectal ultrasound (TRUS) is routinely used for biopsy guidance, but its diagnostic potential for PCa screening is underexplored. We aimed to evaluate TRUS-derived morphological features and develop a nomogram that integrates clinical and TRUS characteristics to improve PCa risk stratification.
Methods:
Consecutive patients with suspected PCa were enrolled from two tertiary centers (training cohort: n = 154, October 2021-January 2023; validation cohort: n = 51, December 2021-June 2022). Demographic data, laboratory-derived PSA indices (including PSA density), and TRUS parameters (independently assessed by two blinded sonographers) were collected and analyzed. A predictive nomogram was constructed using multivariate logistic regression and externally validated.
Results:
In the training cohort (mean age 70.9 ± 8.0 years; 72 PCa, 82 benign), independent predictors of PCa included elevated PSA density (OR=3.86, 95 % CI: 1.30-11.40, P = 0.015), abnormal DRE (OR=3.06, 95 % CI: 1.09-8.60, P = 0.034), TRUS-defined ill-defined zone boundaries (OR=9.61, 95 % CI: 3.37-39.02, P = 0.002), and hyper-enhancement (OR=7.07, 95 % CI: 2.69-21.89, P < 0.001). The nomogram achieved strong discrimination (training C-index=0.933, 95 % CI: 0.881-0.986; validation C-index=0.907, 95 % CI: 0.792-0.970) with 84.7 % sensitivity, 87.8 % specificity, and 86.4 % accuracy. Pathological concordance was high (kappa=0.726).
Conclusion:
TRUS-derived features (ill-defined zones, hyper-enhancement) significantly enhance PCa detection when combined with clinical parameters. Our nomogram provides a practical, visual tool to guide biopsy decisions and demonstrates robust performance across cohorts.
