Evidence map›Paper›PMID 39438187›Full record

ArticleEuropean urology2025

Evaluating Biparametric Versus Multiparametric Magnetic Resonance Imaging for Diagnosing Clinically Significant Prostate Cancer: An International, Paired, Noninferiority, Confirmatory Observer Study.

Jasper J Twilt, Anindo Saha, Joeran S Bosma, Bram van Ginneken, Anders Bjartell, Anwar R Padhani, David Bonekamp, Geert Villeirs, Georg Salomon, Gianluca Giannarini and 16 more

Abstract readMulticenter StudyComparative Study
In one paragraph

Article in European urology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.

0numbers the graph read from it
0cells of the map it votes in
35citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

35 citing papers in PubMed.

  1. Combined ADC and TMagnetic resonance in medicine · 2026
    Article
  2. Article
  3. Successes and challenges in developing a magnetic resonance imaging-transrectal ultrasound machine fusion prostate biopsy service.Canadian Urological Association journal = Journal de l'Association des urologues du Canada · 2026
    Review
  4. Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. Review
  10. Review
  11. Article
  12. Review
  13. The evolving PI-RADS paradigm.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026
    Review
  14. Article
  15. Article
  16. Observational
  17. Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

26 authors.

Jasper J TwiltMinimally Invasive Image-Guided Intervention Center, Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands. Electronic address: jasper.twilt@radboudumc.nl.
Anindo SahaMinimally Invasive Image-Guided Intervention Center, Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands; Diagnostic Image Analysis Group, Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands.
Joeran S BosmaDiagnostic Image Analysis Group, Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands.
Bram van GinnekenDiagnostic Image Analysis Group, Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands.
Anders BjartellDepartment of Urology, Skåne University Hospital, Lund, Sweden; Division of Translational Cancer Research, Lund University Cancer Centre, Lund, Sweden.
Anwar R PadhaniPaul Strickland Scanner Centre, Mount Vernon Cancer Centre, Northwood, UK.
David BonekampDivision of Radiology, Deutsches Krebsforschungszentrum, Heidelberg, Germany.
Geert VilleirsDepartment of Diagnostic Sciences, Ghent University Hospital, Ghent, Belgium.
Georg SalomonMartini Clinic, Prostate Cancer Center, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany.
Gianluca GiannariniUrology Unit, Santa Maria della Misericordia University Hospital, Udine, Italy.
Jayashree Kalpathy-CramerDivision of Artificial Medical Intelligence in Ophthalmology, University of Colorado, Boulder, CO, USA.
Jelle BarentszDepartment of Medical Imaging, Andros Clinics, Amsterdam, The Netherlands.
Klaus H Maier-HeinDivision of Medical Image Computing, Deutsches Krebsforschungszentrum, Heidelberg, Germany; Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany.
Mirabela RusuDepartments of Radiology, Urology and Biomedical Data Science, Stanford University, Stanford, CA, USA.
Olivier RouvièreDepartment of Urinary and Vascular Imaging, Hôpital Edouard Herriot, Hospices Civils de Lyon, Lyon, France; Faculté de Médecine Lyon-Est, Université Lyon 1, Université de Lyon, Lyon, France.
Roderick van den BerghDepartment of Urology, Erasmus Medical Center, Rotterdam, The Netherlands.
Valeria PanebiancoDepartment of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome, Rome, Italy.
Veeru KasivisvanathanDivision of Surgery and Interventional Sciences, University College London and University College London Hospital, London, UK.
Nancy A ObuchowskiDepartment of Quantitative Health Sciences, Cleveland Clinic Foundation, Cleveland, OH, USA; Department of Diagnostic Radiology, Cleveland Clinic Foundation, Cleveland, OH, USA.
Derya YakarDepartment of Radiology, University Medical Center Groningen, Groningen, The Netherlands; Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands.
Mattijs ElschotDepartment of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway; Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway.
Jeroen VeltmanDepartment of Radiology, Ziekenhuisgroep Twente, Almelo, The Netherland; Department of Multi-Modality Medical Imaging, Technical Medical Centre, University of Twente, Enschede, The Netherlands.
Jurgen J FüttererMinimally Invasive Image-Guided Intervention Center, Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands; Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands.
Henkjan HuismanDiagnostic Image Analysis Group, Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands; Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway.
Maarten de RooijDepartment of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands.
PI-CAI Consortium / list of collaborators

Funding

Rad-pathomic deep learning models to assist radiologists in differentiating aggressive from indolent prostate cancer on MRIR37CA260346 · NCI · STANFORD UNIVERSITY · PI Mirabela Rusu · 2022 to 2026
$2.8M
NCI NIH HHS R37 CA260346
6 · The paper itself

Abstract

BACKGROUND AND

objectiveBiparametric magnetic resonance imaging (bpMRI), excluding dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI), is a potential replacement for multiparametric MRI (mpMRI) in diagnosing clinically significant prostate cancer (csPCa). An extensive international multireader multicase observer study was conducted to assess the noninferiority of bpMRI to mpMRI in csPCa diagnosis.

methodsAn observer study was conducted with 400 mpMRI examinations from four European centers, excluding examinations with prior prostate treatment or csPCa (Gleason grade [GG] ≥2) findings. Readers assessed bpMRI and mpMRI sequentially, assigning lesion-specific Prostate Imaging Reporting and Data System (PI-RADS) scores (3-5) and a patient-level suspicion score (0-100). The noninferiority of patient-level bpMRI versus mpMRI csPCa diagnosis was evaluated using the area under the receiver operating curve (AUROC) alongside the sensitivity and specificity at PI-RADS ≥3 with a 5% margin. The secondary outcomes included insignificant prostate cancer (GG1) diagnosis, diagnostic evaluations at alternative risk thresholds, decision curve analyses (DCAs), and subgroup analyses considering reader expertise. Histopathology and ≥3 yr of follow-up were used for the reference standard. KEY FINDINGS AND LIMITATIONS: Sixty-two readers (45 centers and 20 countries) participated. The prevalence of csPCa was 33% (133/400); bpMRI and mpMRI showed similar AUROC values of 0.853 (95% confidence interval [CI], 0.819-0.887) and 0.859 (95% CI, 0.826-0.893), respectively, with a noninferior difference of -0.6% (95% CI, -1.2% to 0.1%, p < 0.001). At PI-RADS ≥3, bpMRI and mpMRI had sensitivities of 88.6% (95% CI, 84.8-92.3%) and 89.4% (95% CI, 85.8-93.1%), respectively, with a noninferior difference of -0.9% (95% CI, -1.7% to 0.0%, p < 0.001), and specificities of 58.6% (95% CI, 52.3-63.1%) and 57.7% (95% CI, 52.3-63.1%), respectively, with a noninferior difference of 0.9% (95% CI, 0.0-1.8%, p < 0.001). At alternative risk thresholds, mpMRI increased sensitivity at the expense of reduced specificity. DCA demonstrated the highest net benefit for an mpMRI pathway in cancer-averse scenarios, whereas a bpMRI pathway showed greater benefit for biopsy-averse scenarios. A subgroup analysis indicated limited additional benefit of DCE MRI for nonexperts. Limitations included that biopsies were conducted based on mpMRI imaging, and reading was performed in a sequential order. CONCLUSIONS AND CLINICAL IMPLICATIONS: It has been found that bpMRI is noninferior to mpMRI in csPCa diagnosis at AUROC, along with the sensitivity and specificity at PI-RADS ≥3, showing its value in individuals without prior csPCa findings and prostate treatment. Additional randomized prospective studies are required to investigate the generalizability of outcomes.

Indexed as

Magnetic Resonance ImagingMultiparametric Magnetic Resonance ImagingProstatic NeoplasmsAgedHumansMaleMiddle AgedObserver VariationPredictive Value of TestsBiparametric magnetic resonance imagingDynamic contrast-enhanced imagingMultiparametric magnetic resonance imagingNoninferiorityObserver studyProstate cancer

Identifiers

PMID39438187
PMCPMC11769734

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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.