Evaluation of Predictive Factors for Prostate Cancer Detection in PI-RADS 2 Lesions
Urology Journal,
Vol. 23 No. 03 (2026),
29 Shahrivar 2026
,
Page 107-113
https://doi.org/10.22037/uj.v23i03.8796
Abstract
Purpose: To evaluate clinical, biochemical, and hematological parameters as predictors of prostate cancer (PCa) in patients with Prostate Imaging Reporting and Data System (PI-RADS) 2 lesions.
Materials and Methods: A total of 955 patients underwent multiparametric magnetic resonance imaging (mpMRI) during the study period, of whom 268 had PI-RADS 2 lesions. Among these, 147 patients with histopathological diagnoses, including 122 benign and 25 malignant cases, were included. Clinical, biochemical, and hematological parameters were compared between groups using appropriate statistical tests. Receiver operating characteristic (ROC) curve analyses were performed to evaluate diagnostic performance.
Results: Prostate-specific antigen (PSA) density (P = .044), neutrophil count (P = .019), monocyte count (P = .021), neutrophil-to-platelet ratio (NPR; P = .016), and monocyte-to-platelet ratio (MPR; P = .019) differed significantly between the malignant and benign groups. Compared with the benign group, neutrophil count, monocyte count, NPR, and MPR were lower, whereas PSA density was higher in patients with PCa. ROC analyses demonstrated moderate discriminatory performance for PSA density (area under the curve [AUC], 0.654; 95% confidence interval [CI], 0.561–0.739; P = .007), monocyte count (AUC, 0.655; 95% CI, 0.546–0.754; P = .011), MPR (AUC, 0.657; 95% CI, 0.547–0.755; P = .037), neutrophil count (AUC, 0.635; 95% CI, 0.523–0.737; P = .046), and NPR (AUC, 0.644; 95% CI, 0.536–0.743; P = .055).
Conclusion: PSA density demonstrated moderate discriminatory performance for detecting PCa in biopsied patients with PI-RADS 2 lesions. Although several hematological indices also showed moderate discriminatory performance, their clinical utility remains limited. Larger prospective studies are required to validate these findings before clinical implementation.
- Prostatic Neoplasms
- Magnetic Resonance Imaging
- Prostate-Specific Antigen
- Biomarkers, Tumor
- Hematologic Tests, Predictive Value of Tests
How to Cite
References
1. Zhang Z, Liu H, Gu X, et al. Multimodal fusion radiomic-immunologic scoring model: accurate identification of prostate cancer progression. BMC Med Imaging. 2025;25:324.
2. Bas O, Sahin TK, Karahan L, Rizzo A, Guven DC. Prognostic significance of the cachexia index (CXI) in patients with cancer: a systematic review and meta-analysis. Clin Nutr ESPEN. 2025;68:240-247.
3. Santoni M, Büttner T, Rescigno P, et al. Apalutamide in metastatic castration-sensitive prostate cancer: results from the multicenter real-world ARON-3 study. Eur Urol Oncol. 2025;8:444-451.
4. Ren H, Peng Y, Si Y, Ye Y, Gong L. Accuracy, intra-, and inter-radiologist variability of PI-RADS v2.1 scoring for clinically significant prostate cancer detection. Quant Imaging Med Surg. 2025;15:7080-7089.
5. Oerther B, Nedelcu A, Engel H, et al. Update on PI-RADS version 2.1 diagnostic performance benchmarks for prostate MRI: systematic review and meta-analysis. Radiology. 2024;312:e233337. Erratum in: Radiology. 2024;312:e249024.
6. Cornford P, van den Bergh RCN, Briers E, et al. EAU-EANM-ESTRO-ESUR-ISUP-SIOG guidelines on prostate cancer-2026 update. Part I: screening, diagnosis, and local treatment with curative intent. Eur Urol. 2026;S0302-2838(26)02114-7.
7. Wei JT, Barocas D, Carlsson S, et al. Early detection of prostate cancer: AUA/SUO guideline Part II: considerations for a prostate biopsy. J Urol. 2023;210:54-63.
8. Jahnen M, Hausler T, Meissner VH, et al. Predicting clinically significant prostate cancer following suspicious mpMRI: analyses from a high-volume center. World J Urol. 2024;42:290.
9. Aslanoğlu A, Saygın H, Öztürk A, Ergin İE, Asdemir A, Velibeyoğlu AF. Correlation between PSA density and multiparametric prostate MRI in the diagnosis of prostate cancer. Bull Urooncol. 2024;23:29-35.
10. Pasecinic V, Novacescu D, Zara F, et al. Predictors of ISUP grade group discrepancies between biopsy and radical prostatectomy: a single-center analysis of clinical, imaging, and histopathological parameters. Cancers (Basel). 2025;17:2595.
11. Xiao Y, Tang B, Wang J, Cai Z, An H, Tao N. Study of the interaction between cardiometabolic index and inflammatory index on the risk of prostate cancer development. Front Immunol. 2025;16:1591879.
12. Hu W, Guo S, Meng X, et al. Development and validation of a visual nomogram for predicting clinically significant prostate cancer in negative mpMRI using 68Ga-PSMA PET/CT. Sci Rep. 2025;15:27453.
13. Oerther B, Engel H, Wilpert C, et al. Multi-center benchmarking of a commercially available artificial intelligence algorithm for Prostate Imaging Reporting and Data System (PI-RADS) score assignment and lesion detection in prostate MRI. Cancers (Basel). 2025;17:815.
14. Karami H, Ghafoori M, Dashti R. The prevalence of prostate cancer in biopsy samples of lesions with PI-RADS 2 score in multiparametric magnetic resonance imaging: a cross-sectional study. Int J Cancer Manag. 2023;16:e132340.
15. Chai JG, Li YH, Ke CX. Development of novel nomograms for predicting prostate cancer in biopsy-naive patients with PSA < 10 ng/mL and PI-RADS ≤ 3 lesions. Front Oncol. 2025;14:1500010.
16. Deniffel D, Healy GM, Dong X, et al. Avoiding unnecessary biopsy: MRI-based risk models versus a PI-RADS and PSA density strategy for clinically significant prostate cancer. Radiology. 2021;300:369-379.
17. Wen J, Liu W, Shen X, Hu W. PI-RADS v2.1 and PSAD for the prediction of clinically significant prostate cancer among patients with PSA levels of 4-10 ng/mL. Sci Rep. 2024;14:6570.
18. Wang Z, Liu H, Zhu Q, Chen J, Zhao J, Zeng H. Analysis of the immune-inflammatory indices for patients with metastatic hormone-sensitive and castration-resistant prostate cancer. BMC Cancer. 2024;24:817.
19. Rajendran I, Lee KL, Thavaraja L, Barrett T. Risk stratification of prostate cancer with MRI and prostate-specific antigen density-based tool for personalized decision making. Br J Radiol. 2024;97:113-119.
20. Ferro M, De Cobelli O, Lucarelli G, et al. Beyond PSA: the role of prostate health index (phi). Int J Mol Sci. 2020;21:1184.
21. Turkbey B, Haider MA. Artificial intelligence for automated cancer detection on prostate MRI: opportunities and ongoing challenges, from the AJR special series on AI applications. AJR Am J Roentgenol. 2022;219:188-194.
22. Alqahtani S. Systematic review of AI-assisted MRI in prostate cancer diagnosis: enhancing accuracy through second opinion tools. Diagnostics (Basel). 2022;14:2576.
23. Yamaya N, Kimura K, Ichikawa R, et al. Prospective evaluation of PI-RADSv2.1 using multiparametric and biparametric MRI for detecting clinically significant prostate cancer based on MRI/US fusion-guided biopsy. Jpn J Radiol. 2025;43:472-482.
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