ArticleFrontiers in oncology2022
Development of a novel nomogram for predicting clinically significant prostate cancer with the prostate health index and multiparametric MRI.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 12 citations in OpenAlex.
- Evolving role of biomarkers beyond prostate‑specific antigen in refining prostate cancer screening: From blood‑based tests to urinary and genetic markers (Review).Molecular medicine reports · 2026Review
- Positive predictive value of the prostate imaging reporting and data system combined with single related indicators in prostate cancer across different prostate zones.Frontiers in oncology · 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
- Evaluation of blood and urine based biomarkers for detection of clinically-significant prostate cancer.Prostate cancer and prostatic diseases · 2025Review
- Development and validation of biopsy free nomograms for predicting clinically significant prostate cancer in men with PI-RADS 4 and 5 lesions.Scientific reports · 2025Article
- Incorporating PHI in decision making: external validation of the Rotterdam risk calculators for detection of prostate cancer.World journal of urology · 2024Article
- Article
- Advances in multiparametric magnetic resonance imaging combined with biomarkers for the diagnosis of high-grade prostate cancer.Frontiers in surgery · 2024Review
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: On prostate biopsy, multiparametric magnetic resonance imaging (mpMRI) and the Prostate Health Index (PHI) have allowed prediction of clinically significant prostate cancer (csPCa). Methods: To predict the likelihood of csPCa, we created a nomogram based on a multivariate model that included PHI and mpMRI. We assessed 315 males who were scheduled for prostate biopsies. Results: We used the Prostate Imaging Reporting and Data System version 2 (PI-RADS V2) to assess mpMRI and optimize PHI testing prior to biopsy. Univariate analysis showed that csPCa may be identified by PHI with a cut-off value of 77.77, PHID with 2.36, and PI-RADS with 3 as the best threshold. Multivariable logistic models for predicting csPCa were developed using PI-RADS, free PSA (fPSA), PHI, and prostate volume. A multivariate model that included PI-RADS, fPSA, PHI, and prostate volume had the best accuracy (AUC: 0.882). Decision curve analysis (DCA), which was carried out to verify the nomogram's clinical applicability, showed an ideal advantage (13.35% higher than the model that include PI-RADS only). Discussion: In conclusion, the nomogram based on PHI and mpMRI is a valuable tool for predicting csPCa while avoiding unnecessary biopsy as much as possible.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.