ArticleBMC cancer2023
A novel clinically significant prostate cancer prediction system with multiparametric MRI and PSA: P.Z.A. score.
Article in BMC cancer, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 9 citations in OpenAlex.
- A comprehensive scoring system integrating clinical and radiological variables for the detection of clinically significant prostate cancer on bi-parameter MRI: multi-center comparison with multi-parametric MRI.Abdominal radiology (New York) · 2026Article
- The combination of prostate health index and multiparametric magnetic resonance imaging in prostate cancer diagnosis: efficacy analysis in different PSA ranges and its clinical decision-guiding value.World journal of urology · 2025Article
- A nomogram using transition zone PSA density for detecting clinically significant prostate cancer in PI-RADS 3 lesions.Scientific reports · 2025Article
- Transition zone-based prostate-specific antigen density for differentiating clinically significant prostate cancer in PI-RADS score 3 lesions.Scientific reports · 2025Article
- Precision in prostate cancer detection: integrating prostate-specific antigen density (PSAD) and the Prostate Imaging Reporting and Data System (PI-RADS) to provide additional risk stratification for a more accurate diagnostic decision.Irish journal of medical science · 2024Article
- The value of adjusted PSAD in prostate cancer detection in the Chinese population.Frontiers in oncology · 2024Article
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Authors and funding
11 authors at 2 institutions in 1 country.
Funding
Abstract
purposeThis study aims to establish and validate a new diagnosis model called P.Z.A. score for clinically significant prostate cancer (csPCa).
methodsThe demographic and clinical characteristics of 956 patients were recorded. Age, prostate-specific antigen (PSA), free/total PSA (f/tPSA), PSA density (PSAD), peripheral zone volume ratio (PZ-ratio), and adjusted PSAD of PZ (aPSADPZ) were calculated and subjected to receiver operating characteristic (ROC) curve analysis. The nomogram was established, and discrimination abilities of the new nomogram were verified with a calibration curve and area under the ROC curve (AUC). The clinical benefits of P.Z.A. score were evaluated by decision curve analysis and clinical impact curves. External validation of the model using the validation set was also performed.
resultsThe AUCs of aPSADPZ, age, PSA, f/tPSA, PSAD and PZ-ratio were 0.824, 0.672, 0.684, 0.715, 0.792 and 0.717, respectively. The optimal threshold of P.Z.A. score was 0.41. The nomogram displayed excellent net benefit and better overall calibration for predicting the occurrence of csPCa. In addition, the number of patients with csPCa predicted by P.Z.A. score was in good agreement with the actual number of patients with csPCa in the high-risk threshold. The validation set provided better validation of the model.
conclusionP.Z.A. score (including PIRADS(P), aPSADPZ(Z) and age(A)) can increase the detection rate of csPCa, which may decrease the risk of misdiagnosis and reduce the number of unnecessary biopsies. P.Z.A. score contains data that is easy to obtain and is worthy of clinical replication.
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