Positive predictive value of the prostate imaging reporting and data system combined with single related indicators in prostate cancer across different prostate zones

Read full text
Authors: XiaFeng Shen, Luyao Yang, Shiwei Wang, Jianliang Shen
Cited by: 0
References: 25

Abstract

Introduction This study aimed to evaluate the positive predictive value (PPV) of the Prostate Imaging Reporting and Data System (PI-RADS) combined with single related indicators in diagnosing prostate cancer (PCa) across different prostate zones. Methods Patients with complete clinical data who underwent prostate magnetic resonance imaging from January 2019 to October 2024 were retrospectively analyzed. PI-RADS was used for diagnosis, zoning, and grading, with 533 cases scoring ≥3. PPVs for PCa across different prostate zones were calculated by combining age, prostate-specific antigen (PSA), PSA density (PSAd), and prostate volume. Differences between non-PCa and PCa groups were compared using independent sample t - and rank-sum tests. Diagnostic efficacy was assessed using area under the curve (AUC) values for receiver operating characteristic curves. Univariate logistic regression analysis was used to identify factors associated with malignant pathology. Results The PPV for PI-RADS scores 3–5 was 20.6% (33/160), 61.1% (159/260), and 80.5% (91/113), respectively. PPVs for PCa across peripheral, transitional, and multi-zones were 78.6% (96/122), 35.2% (114/323), and 82.9% (73/88), respectively. Age, PSA, PSAd, and prostate volume significantly differed between the non-PCa and PCa groups, with AUC values of 0.629, 0.709, 0.809, and 0.703, respectively, and were significantly associated with malignant pathology ( P < 0.001, univariate logistic regression analysis). Conclusion Combining the PI-RADS with other clinical indicators effectively enhanced its initially low PPV for transitional zone lesions, particularly when the PSAd was ≥0.15 ng/mL 2 or the PSA was >10 ng/mL.

Fields of Study & Subjects

Prostate Cancer Diagnosis and Treatment Radiomics and Machine Learning in Medical Imaging MRI in cancer diagnosis

Publication details

Journal / source
Frontiers in Oncology
Volume
16
Pages
1661267–1661267
Publication date
4 February 2026
Language
EN