ArticleBJU international2023
A prostate biopsy risk calculator based on MRI: development and comparison of the Prospective Loyola University multiparametric MRI (PLUM) and Prostate Biopsy Collaborative Group (PBCG) risk calculators.
Article in BJU international, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.
- Clinical impact of MRI-based risk calculators for prostate cancer diagnosis: a systematic review and meta-analysis.Prostate cancer and prostatic diseases · 2026Pooled it
- The Role of Micro-Ultrasound in Prostate Cancer Detection.Current urology reports · 2026Review
- The performance of prostate-specific antigen density in long-term prostate cancer-mortality risk prediction.BJUI compass · 2026Article
- The effects of prostate volume and PI-RADS category on optimal PSA-density thresholds for biopsy decision-making.European radiology · 2026Article
- Predicting clinically significant prostate cancer with or without digital rectal exam and MRI data using ClarityDX Prostate models.NPJ digital medicine · 2026Article
- AI-enhanced micro-ultrasound improves detection of clinically significant prostate cancer at biopsy.BJUI compass · 2026Article
- Using AI to triage patients without clinically significant prostate cancer using biparametric MRI and PSA.Abdominal radiology (New York) · 2025Article
- Performance of GPT-4 for automated prostate biopsy decision-making based on mpMRI: a multi-center evidence study.Military Medical Research · 2025Article
- Prostate magnetic resonance imaging to predict grade concordance, extra prostatic extension, and biochemical recurrence after radical prostatectomy.Urologic oncology · 2025Article
- Personalised Prostate Cancer Diagnosis: Evaluating Biomarker-based Approaches to Reduce Unnecessary Magnetic Resonance Imaging and Biopsy Procedures.European urology open science · 2025Review
- Article
- The impact of integrating PRIMARY score or SUVmax with MRI-based risk models for the detection of clinically significant prostate cancer.European journal of nuclear medicine and molecular imaging · 2025Article
- Integrating shear wave elastography and multiparametric MRI for accurate prostate cancer diagnosis.American journal of cancer research · 2025Article
- Novel clinical risk calculator for improving cancer predictability of mpMRI fusion biopsy in prostates.International urology and nephrology · 2024Article
- Utility of dynamic contrast enhancement for clinically significant prostate cancer detection.BJUI compass · 2024Article
- Repeat Prostate-specific Antigen Testing Improves Risk-based Selection of Men for Prostate Biopsy After Magnetic Resonance Imaging.European urology open science · 2024Article
- Comparison of Magnetic Resonance Imaging-Based Risk Calculators to Predict Prostate Cancer Risk.JAMA network open · 2024Article
- Variability in prostate cancer detection among radiologists and urologists using MRI fusion biopsy.BJUI compass · 2024Article
Corrections and comments
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Authors and funding
11 authors at 3 institutions in 1 country.
Funding
Abstract
objectivesTo develop and validate a prostate cancer (PCa) risk calculator (RC) incorporating multiparametric magnetic resonance imaging (mpMRI) and to compare its performance with that of the Prostate Biopsy Collaborative Group (PBCG) RC. PATIENTS AND
methodsMen without a PCa diagnosis receiving mpMRI before biopsy in the Prospective Loyola University mpMRI (PLUM) Prostate Biopsy Cohort (2015-2020) were included. Data from a separate institution were used for external validation. The primary outcome was diagnosis of no cancer, grade group (GG)1 PCa, and clinically significant (cs)PCa (≥GG2). Binary logistic regression was used to explore standard clinical and mpMRI variables (prostate volume, Prostate Imaging-Reporting Data System [PI-RADS] version 2.0 lesions) with the final PLUM RC, based on a multinomial logistic regression model. Receiver-operating characteristic curve, calibration curves, and decision-curve analysis were evaluated in the training and validation cohorts.
resultsA total of 1010 patients were included for development (N = 674 training [47.8% PCa, 30.9% csPCa], N = 336 internal validation) and 371 for external validation. The PLUM RC outperformed the PBCG RC in the training (area under the curve [AUC] 85.9% vs 66.0%; P < 0.001), internal validation (AUC 88.2% vs 67.8%; P < 0.001) and external validation (AUC 83.9% vs 69.4%; P < 0.001) cohorts for csPCa detection. The PBCG RC was prone to overprediction while the PLUM RC was well calibrated. At a threshold probability of 15%, the PLUM RC vs the PBCG RC could avoid 13.8 vs 2.7 biopsies per 100 patients without missing any csPCa. At a cost level of missing 7.5% of csPCa, the PLUM RC could have avoided 41.0% (566/1381) of biopsies compared to 19.1% (264/1381) for the PBCG RC. The PLUM RC compared favourably with the Stanford Prostate Cancer Calculator (SPCC; AUC 84.1% vs 81.1%; P = 0.002) and the MRI-European Randomized Study of Screening for Prostate Cancer (ERSPC) RC (AUC 84.5% vs 82.6%; P = 0.05).
conclusionsThe mpMRI-based PLUM RC significantly outperformed the PBCG RC and compared favourably with other mpMRI-based RCs. A large proportion of biopsies could be avoided using the PLUM RC in shared decision making while maintaining optimal detection of csPCa.
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Registered trials
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.