Radiologie up2date 2025; 25(02): 107-113
DOI: 10.1055/a-2452-6978
How I do it

How I do it - KI-Support in der Prostata-MRT-Befundung

Hüdanur Bayraktaroglu
,
Clemens C. Cyran
,
Philipp M. Kazmierczak

Der Prostata-MRT kommt angesichts ihrer hervorragenden Evidenz eine bedeutende Rolle in der Diagnostik des Prostatakarzinoms zu. KI-Systeme haben das Potenzial, im Rahmen einer differenzierten Befundungsunterstützung Qualität und Effizienz weiter zu erhöhen und Radiologen im diagnostischen Prozess zu unterstützen bzw. den Workflow zu beschleunigen.



Publikationsverlauf

Artikel online veröffentlicht:
05. Juni 2025

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  • Literatur

  • 1 Hamm CA, Asbach P, Pohlmann A. et al. Oncological Safety of MRI-Informed Biopsy Decision-Making in Men With Suspected Prostate Cancer. JAMA Oncol 2025; 11: 145-153
  • 2 Hugosson J, Godtman RA, Wallstrom J. et al. Results after Four Years of Screening for Prostate Cancer with PSA and MRI. N Engl J Med 2024; 391: 1083-1095
  • 3 Drost FH, Osses DF, Nieboer D. et al. Prostate MRI, with or without MRI-targeted biopsy, and systematic biopsy for detecting prostate cancer. Cochrane Database Syst Rev 2019; 2019 (04) CD012663
  • 4 Deutsche Gesellschaft für Urologie e. V. (DGU). S3-Leitlinie Prostatakarzinom, Evidenztabellen 7.0; AWMF-Registernummer: 043–022OL. 2024 Zugriff am 26. Februar 2025 unter: https://register.awmf.org/de/leitlinien/detail/043-022OL
  • 5 American College of Radiology. PI-RADS Prostate Imaging – Reporting and Data System Version 2.1. 2019 Zugriff am 26. März 2025 unter: https://www.acr.org/Clinical-Resources/Clinical-Tools-and-Reference/Reporting-and-Data-Systems/PI-RADS
  • 6 Turkbey B, Rosenkrantz AB, Haider MA. et al. Prostate Imaging Reporting and Data System Version 2.1: 2019 Update of Prostate Imaging Reporting and Data System Version 2. Eur Urol 2019; 76: 340-351
  • 7 Boschheidgen M, Albers P, Schlemmer HP. et al. Multiparametric Magnetic Resonance Imaging in Prostate Cancer Screening at the Age of 45 Years: Results from the First Screening Round of the PROBASE Trial. Eur Urol 2024; 85: 105-111
  • 8 Ahmed HU, El-Shater Bosaily A, Brown LC. et al. Diagnostic accuracy of multi-parametric MRI and TRUS biopsy in prostate cancer (PROMIS): a paired validating confirmatory study. Lancet 2017; 389: 815-822
  • 9 Kasivisvanathan V, Rannikko AS, Borghi M. et al. MRI-Targeted or Standard Biopsy for Prostate-Cancer Diagnosis. N Engl J Med 2018; 378: 1767-1777
  • 10 Filson CP, Natarajan S, Margolis DJ. et al. Prostate cancer detection with magnetic resonance-ultrasound fusion biopsy: The role of systematic and targeted biopsies. Cancer 2016; 122: 884-892
  • 11 Esteva A, Kuprel B, Novoa RA. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017; 542: 115-118
  • 12 Gulshan V, Peng L, Coram M. et al. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA 2016; 316: 2402-2410
  • 13 Rudolph J, Huemmer C, Preuhs A. et al. Nonradiology Health Care Professionals Significantly Benefit From AI Assistance in Emergency-Related Chest Radiography Interpretation. Chest 2024; 166: 157-170
  • 14 Salim M, Liu Y, Sorkhei M. et al. AI-based selection of individuals for supplemental MRI in population-based breast cancer screening: the randomized ScreenTrustMRI trial. Nat Med 2024; 30: 2623-2630
  • 15 Saha A, Bosma JS, Twilt JJ. et al. Artificial intelligence and radiologists in prostate cancer detection on MRI (PI-CAI): an international, paired, non-inferiority, confirmatory study. Lancet Oncol 2024; 25: 879-887
  • 16 Cai JC, Nakai H, Kuanar S. et al. Fully Automated Deep Learning Model to Detect Clinically Significant Prostate Cancer at MRI. Radiology 2024; 312: e232635
  • 17 Franiel T, Asbach P, Beyersdorff D. et al. mpMRI of the Prostate (MR-Prostatography): Updated Recommendations of the DRG and BDR on Patient Preparation and Scanning Protocol. Rofo 2021; 193: 763-777
  • 18 Zelhof B, Pickles M, Liney G. et al. Correlation of diffusion-weighted magnetic resonance data with cellularity in prostate cancer. BJU Int 2009; 103: 883-888
  • 19 Asif A, Nathan A, Ng A. et al. Comparing biparametric to multiparametric MRI in the diagnosis of clinically significant prostate cancer in biopsy-naive men (PRIME): a prospective, international, multicentre, non-inferiority within-patient, diagnostic yield trial protocol. BMJ Open 2023; 13: e070280
  • 20 Twilt JJ, Saha A, Bosma JS. et al. Evaluating Biparametric Versus Multiparametric Magnetic Resonance Imaging for Diagnosing Clinically Significant Prostate Cancer: An International, Paired, Noninferiority, Confirmatory Observer Study. Eur Urol 2025; 87: 240-250
  • 21 de Rooij M, Allen C, Twilt JJ. et al. PI-QUAL version 2: an update of a standardised scoring system for the assessment of image quality of prostate MRI. Eur Radiol 2024; 34: 7068-7079
  • 22 Maggi M, Panebianco V, Mosca A. et al. Prostate Imaging Reporting and Data System 3 Category Cases at Multiparametric Magnetic Resonance for Prostate Cancer: A Systematic Review and Meta-analysis. Eur Urol Focus 2020; 6: 463-478