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Quality checkpoints in the MRI-directed prostate cancer diagnostic pathway

  • Review Article
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From Nature Reviews Urology

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Abstract

Multiparametric MRI of the prostate is now recommended as the initial diagnostic test for men presenting with suspected prostate cancer, with a negative MRI enabling safe avoidance of biopsy and a positive result enabling MRI-directed sampling of lesions. The diagnostic pathway consists of several steps, from initial patient presentation and preparation to performing and interpreting MRI, communicating the imaging findings, outlining the prostate and intra-prostatic target lesions, performing the biopsy and assessing the cores. Each component of this pathway requires experienced clinicians, optimized equipment, good inter-disciplinary communication between specialists, and standardized workflows in order to achieve the expected outcomes. Assessment of quality and mitigation measures are essential for the success of the MRI-directed prostate cancer diagnostic pathway. Quality assurance processes including Prostate Imaging-Reporting and Data System, template biopsy, and pathology guidelines help to minimize variation and ensure optimization of the diagnostic pathway. Quality control systems including the Prostate Imaging Quality scoring system, patient-level outcomes (such as Prostate Imaging-Reporting and Data System MRI score assignment and cancer detection rates), multidisciplinary meeting review and audits might also be used to provide consistency of outcomes and ensure that all the benefits of the MRI-directed pathway are achieved.

Key points

  • Multiparametric MRI is now recommended as the initial diagnostic test for men presenting with suspected prostate cancer.

  • A negative MRI enables patients to safely avoid biopsy, whereas a positive MRI prompts targeted biopsy and pathologically accurate tissue sampling.

  • The MRI-directed prostate cancer diagnostic pathway involves several steps including acquiring and interpreting MRI, communicating MRI findings, outlining suspicious target lesions, performing biopsy and evaluating the cores.

  • All steps in the pathway are prone to variation; assessment and mitigation of poor quality and variance are essential for a successful delivery of the MRI-directed pathway.

  • Quality assurance systems to minimize variation in performance and prevent poor quality include Prostate Imaging-Reporting and Data System (PI-RADS) imaging guidelines for radiologists, prostate biopsy templates and International Society of Urological Pathology guidelines for histopathologists.

  • Quality control measures include checking MRI compliance with PI-RADS, image quality assessment with the Prostate Imaging Quality scoring system, radiologist certification, multidisciplinary team meeting review and pathology re-review of images, as well as audits of cancer detection rates and biopsy core quality.

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Fig. 1: Improved image quality using deep-learning reconstruction algorithms.
Fig. 2: Possibility of ruling in the presence of clinically significant prostate cancer despite poor MRI quality.
Fig. 3: Inadequate DWI technique results in PI-RADS 3 lesion assessment.
Fig. 4: Improved quality T2 with repeated acquisition.
Fig. 5: Effect of axial orientation on prostate anatomical division.
Fig. 6: Inter-relationships between different steps of the MRI-directed prostate biopsy pathway.

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References

  1. Bjurlin, M. A. et al. Update of the standard operating procedure on the use of multiparametric magnetic resonance imaging for the diagnosis, staging and management of prostate cancer. J. Urol. 203, 706–712 (2020).

    Article  Google Scholar 

  2. Mottet, N. et al. EAU-EANM-ESTRO-ESUR-SIOG Guidelines on Prostate Cancer — 2020 update. Part 1: screening, diagnosis, and local treatment with curative intent. Eur. Urol. 79, 243–262 (2021).

    Article  Google Scholar 

  3. Mason, B. R. et al. Current status of MRI and PET in the NCCN guidelines for prostate cancer. J. Natl. Compr. Cancer Netw. 17, 506–513 (2019).

    Article  Google Scholar 

  4. NICE. Prostate cancer: diagnosis and management. NICE https://www.nice.org.uk/guidance/ng131 (2019).

  5. Le Bihan, D. et al. Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging. Radiology 168, 497–505 (1988).

    Article  Google Scholar 

  6. Bowden, D. & Barrett, T. Angiogenesis imaging in Neoplasia. J. Clin. Imaging Sci. 1, 38 (2011).

    Article  Google Scholar 

  7. Kasivisvanathan, V. et al. MRI-targeted or standard biopsy for prostate-cancer diagnosis. N. Engl. J. Med. 378, 1767–1777 (2018).

    Article  Google Scholar 

  8. van der Leest, M. et al. Head-to-head comparison of transrectal ultrasound-guided prostate biopsy versus multiparametric prostate resonance imaging with subsequent magnetic resonance-guided biopsy in biopsy-naïve men with elevated prostate-specific antigen: a large prospective Mu. Eur. Urol. 75, 570–578 (2019).

    Article  Google Scholar 

  9. Ahmed, H. U. et al. Diagnostic accuracy of multi-parametric MRI and TRUS biopsy in prostate cancer (PROMIS): a paired validating confirmatory study. Lancet 389, 815–822 (2017).

    Article  Google Scholar 

  10. Rouvière, O. et al. Use of prostate systematic and targeted biopsy on the basis of multiparametric MRI in biopsy-naive patients (MRI-FIRST): a prospective, multicentre, paired diagnostic study. Lancet Oncol. 20, 100–109 (2019).

    Article  Google Scholar 

  11. Turkbey, B. et al. Prostate imaging reporting and data system version 2.1: 2019 update of prostate imaging reporting and data system version 2. Eur. Urol. 76, 340–351 (2019).

    Article  Google Scholar 

  12. Venderink, W. et al. Multiparametric magnetic resonance imaging for the detection of clinically significant prostate cancer: what urologists need to know. Part 3: targeted biopsy. Eur. Urol. 77, 481–490 (2020).

    Article  Google Scholar 

  13. Padhani, A. R. et al. PI-RADS steering committee: the PI-RADS multiparametric MRI and MRI-directed biopsy pathway. Radiology 292, 464–474 (2019).

    Article  Google Scholar 

  14. Schoots, I. G. & Padhani, A. R. Risk-adapted biopsy decision based on prostate magnetic resonance imaging and prostate-specific antigen density for enhanced biopsy avoidance in first prostate cancer diagnostic evaluation. BJU Int. 127, 175–178 (2021).

    Article  Google Scholar 

  15. Sathianathen, N. J. et al. Negative predictive value of multiparametric magnetic resonance imaging in the detection of clinically significant prostate cancer in the prostate imaging reporting and data system era: a systematic review and meta-analysis. Eur. Urol. 78, 402–414 (2020).

    Article  Google Scholar 

  16. Park, K. J., Choi, S. H., Kim, M. H., Kim, J. K. & Jeong, I. G. Performance of prostate imaging reporting and data system version 2.1 for diagnosis of prostate cancer: a systematic review and meta-analysis. J. Magn. Reson. Imaging 54, 103–112 (2021).

    Article  Google Scholar 

  17. Westphalen, A. C. et al. Variability of the positive predictive value of PI-RADS for prostate MRI across 26 centers: experience of the society of abdominal radiology prostate cancer disease-focused panel. Radiology 296, 76–84 (2020).

    Article  Google Scholar 

  18. Radtke, J. P. et al. Multiparametric magnetic resonance imaging (MRI) and MRI–transrectal ultrasound fusion biopsy for index tumor detection: correlation with radical prostatectomy specimen. Eur. Urol. 70, 846–853 (2016).

    Article  Google Scholar 

  19. Tan, N. et al. Characteristics of detected and missed prostate cancer foci on 3-T multiparametric MRI using an endorectal coil correlated with whole-mount thin-section histopathology. Am. J. Roentgenol. 205, W87–W92 (2015).

    Article  Google Scholar 

  20. Langer, D. L. et al. Intermixed normal tissue within prostate cancer: effect on MR imaging measurements of apparent diffusion coefficient and T2-sparse versus dense cancers. Radiology 249, 900–908 (2008).

    Article  Google Scholar 

  21. Serrao, E. M. et al. Investigating the ability of multiparametric MRI to exclude significant prostate cancer prior to transperineal biopsy. J. Can. Urol. Assoc. 9, E853–E858 (2015).

    Article  Google Scholar 

  22. Salami, S. S. et al. Biologic significance of magnetic resonance imaging invisibility in localized prostate cancer. JCO Precis. Oncol. https://doi.org/10.1200/po.19.00054 (2019).

    Article  Google Scholar 

  23. Esses, S. J., Taneja, S. S. & Rosenkrantz, A. B. Imaging facilities’ adherence to PI-RADS v2 minimum technical standards for the performance of prostate MRI. Acad. Radiol. 25, 188–195 (2018).

    Article  Google Scholar 

  24. Burn, P. R. et al. A multicentre assessment of prostate MRI quality and compliance with UK and international standards. Clin. Radiol. 74, 894.e19–894.e25 (2019).

    Article  Google Scholar 

  25. Rouvière, O., Souchon, R. & Melodelima, C. Pitfalls in interpreting positive and negative predictive values: application to prostate multiparametric magnetic resonance imaging. Diagn. Interv. Imaging 99, 515–518 (2018).

    Article  Google Scholar 

  26. Barentsz, J. O. et al. ESUR prostate MR guidelines 2012. Eur. Radiol. 22, 746–757 (2012).

    Article  Google Scholar 

  27. Weinreb, J. C. et al. PI-RADS prostate imaging — reporting and data system: 2015, version 2. Eur. Urol. 69, 16–40 (2016).

    Article  Google Scholar 

  28. Sackett, J. et al. Quality of prostate MRI: is the PI-RADS standard sufficient? Acad. Radiol. 28, 199–207 (2021).

    Article  Google Scholar 

  29. van der Leest, M., Israël, B., Engels, R. R. M. & Barentsz, J. O. Reply to Arnaldo Stanzione, Massimo Imbriaco, and Renato Cuocolo’s Letter to the Editor re: Marloes van der Leest, Bas Israël, Eric Bastiaan Cornel, et al. High diagnostic performance of short magnetic resonance imaging protocols for prostate cancer detection in biopsy-naïve men: the next step in magnetic resonance imaging accessibility. Eur. Urol. 2019;76:574-81. Are we meeting our standards? Stringent prostate imaging reporting and data system acquisition requirements might be limiting prostate accessibility. Eur. Urol. 77, e58–e59 (2020).

    Article  Google Scholar 

  30. Stabile, A. et al. Factors influencing variability in the performance of multiparametric magnetic resonance imaging in detecting clinically significant prostate cancer: a systematic literature review. Eur. Urol. Oncol. 3, 145–167 (2020).

    Article  Google Scholar 

  31. Akin, O. et al. Interactive dedicated training curriculum improves accuracy in the interpretation of MR imaging of prostate cancer. Eur. Radiol. 20, 995–1002 (2010).

    Article  Google Scholar 

  32. Gaziev, G. et al. Defining the learning curve for multiparametric magnetic resonance imaging (MRI) of the prostate using MRI-transrectal ultrasonography (TRUS) fusion-guided transperineal prostate biopsies as a validation tool. BJU Int. 117, 80–86 (2016).

    Article  Google Scholar 

  33. Stolk, T. T. et al. False positives in PIRADS (V2) 3, 4, and 5 lesions: relationship with reader experience and zonal location. Abdom. Radiol. 44, 1044–1051 (2019).

    Article  Google Scholar 

  34. Hansen, N. L. et al. Comparison of initial and tertiary centre second opinion reads of multiparametric magnetic resonance imaging of the prostate prior to repeat biopsy. Eur. Radiol. 27, 2259–2266 (2017).

    Article  Google Scholar 

  35. Wibmer, A. et al. Diagnosis of extracapsular extension of prostate cancer on prostate MRI: Impact of second-opinion readings by subspecialized genitourinary oncologic radiologists. Am. J. Roentgenol. 205, W73–W78 (2015).

    Article  Google Scholar 

  36. Ecke, T. H. et al. Comparison of initial and second opinion reads of multiparametric magnetic resonance imaging of the prostate for transperineal template-guided biopsies with MRI-Ultrasound fusion. Urol. Oncol. Semin. Orig. Investig. 39, 781.e1–781.e7 (2021).

    Google Scholar 

  37. de Rooij, M. et al. ESUR/ESUI consensus statements on multi-parametric MRI for the detection of clinically significant prostate cancer: quality requirements for image acquisition, interpretation and radiologists’ training. Eur. Radiol. 30, 5404–5416 (2020).

    Article  Google Scholar 

  38. Barrett, T. et al. Certification in reporting multiparametric magnetic resonance imaging of the prostate: recommendations of a UK consensus meeting. BJU Int. 127, 304–306 (2021).

    Article  Google Scholar 

  39. Sung, H. et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 71, 209–249 (2021).

    Article  Google Scholar 

  40. NHS. Cancer referral to treatment period start date. NHS https://www.datadictionary.nhs.uk/data_elements/cancer_referral_to_treatment_period_start_date.html (2022).

  41. Redaniel, M. T., Martin, R. M., Gillatt, D., Wade, J. & Jeffreys, M. Time from diagnosis to surgery and prostate cancer survival: a retrospective cohort study. BMC Cancer 13, 559 (2013).

    Article  Google Scholar 

  42. Panebianco, V. et al. Clinical utility of multiparametric magnetic resonance imaging as the first-line tool for men with high clinical suspicion of prostate cancer. Eur. Urol. Oncol. 1, 208–214 (2018).

    Article  Google Scholar 

  43. van der Leest, M. et al. High diagnostic performance of short magnetic resonance imaging protocols for prostate cancer detection in biopsy-naïve men: the next step in magnetic resonance imaging accessibility. Eur. Urol. 76, 574–581 (2019).

    Article  Google Scholar 

  44. Kuhl, C. K. et al. Abbreviated biparametric prostate MR imaging in men with elevated prostate-specific antigen. Radiology 285, 493–505 (2017).

    Article  Google Scholar 

  45. Sushentsev, N. et al. The effect of capped biparametric magnetic resonance imaging slots on weekly prostate cancer imaging workload. Br. J. Radiol. 93, 20190929 (2020).

    Article  Google Scholar 

  46. Zawaideh, J. P. et al. Diagnostic accuracy of biparametric versus multiparametric prostate MRI: assessment of contrast benefit in clinical practice. Eur. Radiol. 30, 4039–4049 (2020).

    Article  Google Scholar 

  47. Bass, E. J. et al. Prostate cancer diagnostic pathway: Is a one-stop cognitive MRI targeted biopsy service a realistic goal in everyday practice? A pilot cohort in a tertiary referral centre in the UK. BMJ Open 8, 24941 (2018).

    Article  Google Scholar 

  48. Purysko, A. S. & Rosenkrantz, A. B. Technique of multiparametric MR imaging of the prostate. Urol. Clin. North. Am. 45, 427–438 (2018).

    Article  Google Scholar 

  49. Franiel, T. et al. MpMRI of the prostate (MR-prostatography): updated recommendations of the DRG and BDR on patient preparation and scanning protocol. Rofo 193, 763–776 (2021).

    Google Scholar 

  50. Schoots, I. G. et al. PI-RADS committee position on MRI without contrast medium in biopsy-naive men with suspected prostate cancer: narrative review. Am. J. Roentgenol. 216, 3–19 (2021).

    Article  Google Scholar 

  51. Ippoliti, S. et al. Optimal biopsy approach for detection of clinically significant prostate cancer. Br. J. Radiol. 95, 20210413 (2021).

    Article  Google Scholar 

  52. Hansen, N. et al. Magnetic resonance and ultrasound image fusion supported transperineal prostate biopsy using the Ginsburg protocol: technique, learning points, and biopsy results. Eur. Urol. 70, 332–340 (2016).

    Article  Google Scholar 

  53. Immerzeel, J. et al. Multiparametric magnetic resonance imaging for the detection of clinically significant prostate cancer: what urologists need to know. Part 4: transperineal magnetic resonance–ultrasound fusion guided biopsy using local anesthesia. Eur. Urol. 81, 110–117 (2022).

    Article  Google Scholar 

  54. Brisbane, W. G. et al. Targeted prostate biopsy: umbra, penumbra, and value of perilesional sampling. Eur. Urol. https://doi.org/10.1016/j.eururo.2022.01.008 (2022).

    Article  Google Scholar 

  55. Hansen, N. L. et al. Optimising the number of cores for magnetic resonance imaging-guided targeted and systematic transperineal prostate biopsy. BJU Int. 125, 260–269 (2020).

    Article  Google Scholar 

  56. Epstein, J. I. et al. The 2014 International Society of Urological Pathology (ISUP) consensus conference on Gleason grading of prostatic carcinoma: definition of grading patterns and proposal for a new grading system. Am. J. Surg. Pathol. 40, 244–252 (2016).

    Article  Google Scholar 

  57. Drost, F.-J. H. et al. Prostate MRI, with or without MRI-targeted biopsy, and systematic biopsy for detecting prostate cancer. Cochrane Database Syst. Rev. https://doi.org/10.1002/14651858.cd012663.pub2 (2019).

    Article  Google Scholar 

  58. Caglic, I. & Barrett, T. Optimising prostate mpMRI: prepare for success. Clin. Radiol. 74, 831–840 (2019).

    Article  Google Scholar 

  59. Coskun, M. et al. Impact of bowel preparation with Fleet’sTM enema on prostate MRI quality. Abdom. Radiol. 45, 4252–4259 (2020).

    Article  Google Scholar 

  60. Czarniecki, M. et al. Role of PROPELLER-DWI of the prostate in reducing distortion and artefact from total hip replacement metalwork. Eur. J. Radiol. 102, 213–219 (2018).

    Article  Google Scholar 

  61. Caglic, I., Hansen, N. L., Slough, R. A., Patterson, A. J. & Barrett, T. Evaluating the effect of rectal distension on prostate multiparametric MRI image quality. Eur. J. Radiol. 90, 174–180 (2017).

    Article  Google Scholar 

  62. Engels, R. R. M., Israël, B., Padhani, A. R. & Barentsz, J. O. Multiparametric magnetic resonance imaging for the detection of clinically significant prostate cancer: what urologists need to know. part 1: acquisition. Eur. Urol. 77, 457–468 (2020).

    Article  Google Scholar 

  63. Slough, R. A., Caglic, I., Hansen, N. L., Patterson, A. J. & Barrett, T. Effect of hyoscine butylbromide on prostate multiparametric MRI anatomical and functional image quality. Clin. Radiol. 73, 216.e9–216.e14 (2018).

    Article  Google Scholar 

  64. Ullrich, T. et al. Hyoscine butylbromide significantly decreases motion artefacts and allows better delineation of anatomic structures in mp-MRI of the prostate. Eur. Radiol. 28, 17–23 (2018).

    Article  Google Scholar 

  65. Purysko, A. S. et al. Influence of enema and dietary restrictions on prostate MR image quality: a multireader study. Acad. Radiol. 29, 4–14 (2022).

    Article  Google Scholar 

  66. Reischauer, C., Cancelli, T., Malekzadeh, S., Froehlich, J. M. & Thoeny, H. C. How to improve image quality of DWI of the prostate — enema or catheter preparation? Eur. Radiol. 31, 6708–6716 (2021).

    Article  Google Scholar 

  67. Lim, C. et al. Does a cleansing enema improve image quality of 3T surface coil multiparametric prostate MRI? J. Magn. Reson. Imaging 42, 689–697 (2015).

    Article  Google Scholar 

  68. Czyzewska, D., Sushentsev, N., Latoch, E., Slough, R. A. & Barrett, T. T2-PROPELLER compared to T2-FRFSE for image quality and lesion detection at prostate MRI. Can. Assoc. Radiol. J. https://doi.org/10.1177/08465371211030206 (2021).

    Article  Google Scholar 

  69. Meier-Schroers, M. et al. Revised PROPELLER for T2-weighted imaging of the prostate at 3 Tesla: impact on lesion detection and PI-RADS classification. Eur. Radiol. 28, 24–30 (2018).

    Article  Google Scholar 

  70. Koch, K. M. et al. Analysis and evaluation of a deep learning reconstruction approach with denoising for orthopedic MRI. Radiol. Artif. Intell. 3, e200278 (2021).

    Article  Google Scholar 

  71. Gassenmaier, S. et al. Deep learning — accelerated T2-weighted imaging of the prostate: reduction of acquisition time and improvement of image quality. Eur. J. Radiol. 137, 109600 (2021).

    Article  Google Scholar 

  72. Ueda, T. et al. Deep learning reconstruction of diffusion-weighted MRI improves image quality for prostatic imaging. Radiology https://doi.org/10.1148/radiol.204097 (2022).

    Article  Google Scholar 

  73. Moldovan, P. C. et al. What is the negative predictive value of multiparametric magnetic resonance imaging in excluding prostate cancer at biopsy? A systematic review and meta-analysis from the European Association of Urology Prostate Cancer Guidelines Panel. Eur. Urol. 72, 250–266 (2017).

    Article  Google Scholar 

  74. Leeflang, M. M. G., Rutjes, A. W. S., Reitsma, J. B., Hooft, L. & Bossuyt, P. M. M. Variation of a test’s sensitivity and specificity with disease prevalence. CMAJ 185, E537–E544 (2013).

    Article  Google Scholar 

  75. Tan, N., Lakshmi, M., Hernandez, D. & Scuderi, A. Upcoming American College of Radiology prostate MRI designation launching: what to expect. Abdom. Radiol. 45, 4109–4111 (2020).

    Article  Google Scholar 

  76. Belue, M. J., Yilmaz, E. C., Daryanani, A. & Turkbey, B. Current status of biparametric MRI in prostate cancer diagnosis: literature analysis. Life 12, 804 (2022).

    Article  Google Scholar 

  77. Barrett, T., Rajesh, A., Rosenkrantz, A. B., Choyke, P. L. & Turkbey, B. PI-RADS version 2.1: one small step for prostate MRI. Clin. Radiol. 74, 841–852 (2019).

    Article  Google Scholar 

  78. Barrett, T., Turkbey, B. & Choyke, P. L. PI-RADS version 2: what you need to know. Clin. Radiol. 70, 1165–1176 (2015).

    Article  Google Scholar 

  79. Papoutsaki, M. V. et al. Standardisation of prostate multiparametric MRI across a hospital network: a London experience. Insights Imaging 12, 52 (2021).

    Article  Google Scholar 

  80. Giganti, F., Allen, C., Emberton, M., Moore, C. M. & Kasivisvanathan, V. Prostate imaging quality (PI-QUAL): a new quality control scoring system for multiparametric magnetic resonance imaging of the prostate from the PRECISION trial. Eur. Urol. Oncol. 3, 615–619 (2020).

    Article  Google Scholar 

  81. Giganti, F. et al. Understanding PI-QUAL for prostate MRI quality: a practical primer for radiologists. Insights Imaging 12, 59 (2021).

    Article  Google Scholar 

  82. Giganti, F. et al. Prostate MRI quality: a critical review of the last 5 years and the role of the PI-QUAL score. Br. J. Radiol. 95, 20210415 (2021).

    Article  Google Scholar 

  83. Giganti, F. et al. Inter-reader agreement of the PI-QUAL score for prostate MRI quality in the NeuroSAFE PROOF trial. Eur. Radiol. https://doi.org/10.1007/s00330-021-08169-1 (2021).

    Article  Google Scholar 

  84. Boschheidgen, M. et al. Comparison and prediction of artefact severity due to total hip replacement in 1.5 T versus 3 T MRI of the prostate. Eur. J. Radiol. 144, 109949 (2021).

    Article  Google Scholar 

  85. Karanasios, E., Caglic, I., Zawaideh, J. P. & Barrett, T. Prostate MRI quality: clinical impact of the PI-QUAL score in prostate cancer diagnostic work-up. Br. J. Radiol. https://doi.org/10.1259/bjr.20211372 (2022).

    Article  Google Scholar 

  86. Arnoldner, M. A. et al. Rectal preparation significantly improves prostate imaging quality: assessment of the PI-QUAL score with visual grading characteristics. Eur. J. Radiol. 147, 110145 (2022).

    Article  Google Scholar 

  87. Turkbey, B. Better image quality for diffusion-weighted MRI of the prostate using deep learning. Radiology https://doi.org/10.1148/radiol.212078 (2022).

    Article  Google Scholar 

  88. de Rooij, M. & Barentsz, J. O. PI-QUAL v.1: the first step towards good-quality prostate MRI. Eur. Radiol. 32, 876–878 (2022).

    Article  Google Scholar 

  89. Cipollari, S. et al. Convolutional neural networks for automated classification of prostate multiparametric magnetic resonance imaging based on image quality. J. Magn. Reson. Imaging 55, 480–490 (2022).

    Article  Google Scholar 

  90. Brizmohun Appayya, M. et al. National implementation of multi-parametric magnetic resonance imaging for prostate cancer detection–recommendations from a UK consensus meeting. BJU Int. 122, 13–25 (2018).

    Article  Google Scholar 

  91. Puech, P. et al. How are we going to train a generation of radiologists (and urologists) to read prostate MRI? Curr. Opin. Urol. 25, 522–535 (2015).

    Article  Google Scholar 

  92. Rosenkrantz, A. B. et al. The learning curve in prostate MRI interpretation: self-directed learning versus continual reader feedback. Am. J. Roentgenol. 208, W92–W100 (2017).

    Article  Google Scholar 

  93. Greer, M. D. et al. Interreader variability of prostate imaging reporting and data system version 2 in detecting and assessing prostate cancer lesions at prostate MRI. Am. J. Roentgenol. 212, 1197–1205 (2019).

    Article  Google Scholar 

  94. Bhayana, R. et al. PI-RADS versions 2 and 2.1: interobserver agreement and diagnostic performance in peripheral and transition zone lesions among six radiologists. Am. J. Roentgenol. 217, 141–151 (2021).

    Article  Google Scholar 

  95. Smith, C. P. et al. Intra- and interreader reproducibility of PI-RADSv2: a multireader study. J. Magn. Reson. Imaging 49, 1694–1703 (2019).

    Article  Google Scholar 

  96. Park, K. J. et al. Risk stratification of prostate cancer according to PI-RADS® version 2 categories: meta-analysis for prospective studies. J. Urol. 204, 1141–1149 (2020).

    Article  Google Scholar 

  97. de Rooij, M. et al. Focus on the quality of prostate multiparametric magnetic resonance imaging: synopsis of the ESUR/ESUI recommendations on quality assessment and interpretation of images and radiologists’ training. Eur. Urol. 78, 483–485 (2020).

    Article  Google Scholar 

  98. Barrett, T. et al. Prostate MRI qualification: AJR expert panel narrative review. Am. J. Roentgenol. https://doi.org/10.2214/ajr.22.27615 (2022).

    Article  Google Scholar 

  99. Butler, P. F. MQSA (Mammography Quality Standards Act) update-focusing on quality assurance. Radiol. Manag. 20, 40–50 (1998).

    Google Scholar 

  100. Reis, C., Pascoal, A., Sakellaris, T. & Koutalonis, M. Quality assurance and quality control in mammography: a review of available guidance worldwide. Insights Imaging 4, 539–553 (2013).

    Article  Google Scholar 

  101. Pontone, G. et al. Training in cardiac computed tomography: EACVI certification process. Eur. Heart J. Cardiovasc. Imaging 19, 123–126 (2018).

    Article  Google Scholar 

  102. Caglic, I. et al. Integration of prostate biopsy results with pre-biopsy multiparametric magnetic resonance imaging findings improves local staging of prostate cancer. Can. Assoc. Radiol. J. https://doi.org/10.1177/08465371211073158 (2022).

    Article  Google Scholar 

  103. Wassberg, C. et al. The incremental value of contrast-enhanced MRI in the detection of biopsy-proven local recurrence of prostate cancer after radical prostatectomy: effect of reader experience. Am. J. Roentgenol. 199, 360–366 (2012).

    Article  Google Scholar 

  104. Gatti, M. et al. Prostate cancer detection with biparametric magnetic resonance imaging (bpMRI) by readers with different experience: performance and comparison with multiparametric (mpMRI). Abdom. Radiol. 44, 1883–1893 (2019).

    Article  Google Scholar 

  105. Greer, M. D. et al. Validation of the dominant sequence paradigm and role of dynamic contrast-enhanced imaging in Pi-RADS version 2. Radiology 285, 859–869 (2017).

    Article  Google Scholar 

  106. Rothschild, J., Lourenco, A. P. & Mainiero, M. B. Screening mammography recall rate: does practice site matter? Radiology 269, 348–353 (2013).

    Article  Google Scholar 

  107. Greer, M. D. et al. All over the map: an interobserver agreement study of tumor location based on the PI-RADSv2 sector map. J. Magn. Reson. Imaging 48, 482–490 (2018).

    Article  Google Scholar 

  108. Shaish, H. et al. Impact of a structured reporting template on adherence to prostate imaging reporting and data system version 2 and on the diagnostic performance of prostate MRI for clinically significant prostate cancer. J. Am. Coll. Radiol. 15, 749–754 (2018).

    Article  Google Scholar 

  109. Rudolph, M. M. et al. Validation of the PI-RADS language: predictive values of PI-RADS lexicon descriptors for detection of prostate cancer. Eur. Radiol. 30, 4262–4271 (2020).

    Article  Google Scholar 

  110. Purysko, A. S. et al. PI-RADS version 2.1: a critical review, from the AJR special series on radiology reporting and data systems. Am. J. Roentgenol. 216, 20–32 (2021).

    Article  Google Scholar 

  111. Snoj, Ž., Rundo, L., Gill, A. B. & Barrett, T. Quantifying the effect of biopsy lateral decubitus patient positioning compared to supine prostate MRI scanning on prostate translocation and distortion. Can. Urol. Assoc. J. 14, E445–E452 (2020).

    Google Scholar 

  112. Zawaideh, J. P. et al. Comparison of Likert and PI-RA DS version 2 MRI scoring systems for the detection of clinically significant prostate cancer. Br. J. Radiol. 93, 20200298 (2020).

    Article  Google Scholar 

  113. Khoo, C. C. et al. Likert vs PI-RADS v2: a comparison of two radiological scoring systems for detection of clinically significant prostate cancer. BJU Int. 125, 49–55 (2020).

    Article  Google Scholar 

  114. Latifoltojar, A., Appayya, M. B., Barrett, T. & Punwani, S. Similarities and differences between Likert and PIRADS v2.1 scores of prostate multiparametric MRI: a pictorial review of histology-validated cases. Clin. Radiol. 74, 895.e1–895.e15 (2019).

    Article  Google Scholar 

  115. Hansen, N. L. et al. Multiparametric prostate magnetic resonance imaging and cognitively targeted transperineal biopsy in patients with previous abdominoperineal resection and suspicion of prostate cancer. Urology 96, 8–14 (2016).

    Article  Google Scholar 

  116. Puech, P. et al. Multiparametric MRI-targeted TRUS prostate biopsies using visual registration. Biomed Res. Int. 2014, (2014).

  117. Beyersdorff, D. et al. MR imaging-guided prostate biopsy with a closed MR unit at 1.5 T: initial results. Radiology 234, 576–581 (2005).

    Article  Google Scholar 

  118. Wegelin, O. et al. Comparing three different techniques for magnetic resonance imaging-targeted prostate biopsies: a systematic review of in-bore versus magnetic resonance imaging-transrectal ultrasound fusion versus cognitive registration. Is there a preferred technique? Eur. Urol. 71, 517–531 (2017).

    Article  Google Scholar 

  119. Simmons, L. A. M. et al. Accuracy of transperineal targeted prostate biopsies, visual estimation and image fusion in men needing repeat biopsy in the PICTURE trial. J. Urol. 200, 1227–1234 (2018).

    Article  Google Scholar 

  120. Hamid, S. et al. The SmartTarget biopsy trial: a prospective, within-person randomised, blinded trial comparing the accuracy of visual-registration and magnetic resonance imaging/ultrasound image-fusion targeted biopsies for prostate cancer risk stratification. Eur. Urol. 75, 733–740 (2019).

    Article  Google Scholar 

  121. Watts, K. L. et al. Systematic review and meta-analysis comparing cognitive vs. image-guided fusion prostate biopsy for the detection of prostate cancer. Urol. Oncol. Semin. Orig. Investig. 38, 734.e19–734.e25 (2020).

    Google Scholar 

  122. Venderink, W., Govers, T. M., De Rooij, M., Futterer, J. J. & Sedelaar, J. P. M. Cost-effectiveness comparison of imaging-guided prostate biopsy techniques: systematic transrectal ultrasound, direct in-bore MRI, and image fusion. Am. J. Roentgenol. 208, 1058–1063 (2017).

    Article  Google Scholar 

  123. Hale, G. R. et al. Comparison of elastic and rigid registration during magnetic resonance imaging/ultrasound fusion-guided prostate biopsy: a multi-operator phantom study. J. Urol. 200, 1114–1121 (2018).

    Article  Google Scholar 

  124. Ukimura, O. et al. 3-Dimensional elastic registration system of prostate biopsy location by real-time 3-dimensional transrectal ultrasound guidance with magnetic resonance/transrectal ultrasound image fusion. J. Urol. 187, 1080–1086 (2012).

    Article  Google Scholar 

  125. Valerio, M. et al. Detection of clinically significant prostate cancer using magnetic resonance imaging-ultrasound fusion targeted biopsy: a systematic review. Eur. Urol. 68, 8–19 (2015).

    Article  Google Scholar 

  126. Tamhankar, A. S. et al. The clinical and financial implications of a decade of prostate biopsies in the NHS: analysis of hospital episode statistics data 2008–2019. BJU Int. 126, 133–141 (2020).

    Article  Google Scholar 

  127. Gorin, M. A. et al. Transperineal prostate biopsy with cognitive magnetic resonance imaging/biplanar ultrasound fusion: description of technique and early results. World J. Urol. 38, 1943–1949 (2020).

    Article  Google Scholar 

  128. Pepdjonovic, L. et al. Zero hospital admissions for infection after 577 transperineal prostate biopsies using single-dose cephazolin prophylaxis. World J. Urol. 35, 1199–1203 (2017).

    Article  Google Scholar 

  129. Hossack, T. et al. Location and pathological characteristics of cancers in radical prostatectomy specimens identified by transperineal biopsy compared to transrectal biopsy. J. Urol. 188, 781–785 (2012).

    Article  Google Scholar 

  130. Israël, B. et al. Clinical implementation of pre-biopsy magnetic resonance imaging pathways for the diagnosis of prostate cancer. BJU Int. https://doi.org/10.1111/BJU.15562 (2021).

    Article  Google Scholar 

  131. Xiang, J. et al. Transperineal versus transrectal prostate biopsy in the diagnosis of prostate cancer: a systematic review and meta-analysis. World J. Surg. Oncol. 17, 31 (2019).

    Article  Google Scholar 

  132. Kuru, T. H. et al. Definitions of terms, processes and a minimum dataset for transperineal prostate biopsies: a standardization approach of the Ginsburg Study Group for enhanced prostate diagnostics. BJU Int. 112, 568–577 (2013).

    Article  Google Scholar 

  133. Onik, G. & Barzell, W. Transperineal 3D mapping biopsy of the prostate: an essential tool in selecting patients for focal prostate cancer therapy. Urol. Oncol. Semin. Orig. Investig. 26, 506–510 (2008).

    Google Scholar 

  134. Hansen, N. L. et al. Multicentre evaluation of targeted and systematic biopsies using magnetic resonance and ultrasound image-fusion guided transperineal prostate biopsy in patients with a previous negative biopsy. BJU Int. 120, 631–638 (2017).

    Article  Google Scholar 

  135. Hansen, N. L. et al. Multicentre evaluation of magnetic resonance imaging supported transperineal prostate biopsy in biopsy-naïve men with suspicion of prostate cancer. BJU Int. 122, 40–49 (2018).

    Article  Google Scholar 

  136. Das, C. J., Razik, A., Netaji, A. & Verma, S. Prostate MRI–TRUS fusion biopsy: a review of the state of the art procedure. Abdom. Radiol. 45, 2176–2183 (2020).

    Article  Google Scholar 

  137. Moore, C. M. et al. Standards of reporting for MRI-targeted biopsy studies (START) of the prostate: recommendations from an international working group. Eur. Urol. 64, 544–552 (2013).

    Article  Google Scholar 

  138. Schouten, M. G. et al. Why and where do we miss significant prostate cancer with multi-parametric magnetic resonance imaging followed by magnetic resonance-guided and transrectal ultrasound-guided biopsy in biopsy-naïve men? Eur. Urol. 71, 896–903 (2017).

    Article  Google Scholar 

  139. Tracy, C. R. et al. Optimizing MRI-targeted prostate biopsy: the diagnostic benefit of additional targeted biopsy cores. Urol. Oncol. Semin. Orig. Investig. 39, 193.e1–193.e6 (2021).

    Google Scholar 

  140. Ploussard, G. et al. Assessment of the minimal targeted biopsy core number per MRI lesion for improving prostate cancer grading prediction. J. Clin. Med. 9, 225 (2020).

    Article  Google Scholar 

  141. Lu, A. J. et al. Role of core number and location in targeted magnetic resonance imaging-ultrasound fusion prostate biopsy. Eur. Urol. 76, 14–17 (2019).

    Article  Google Scholar 

  142. Meng, X. et al. The institutional learning curve of magnetic resonance imaging-ultrasound fusion targeted prostate biopsy: temporal improvements in cancer detection in 4 years. J. Urol. 200, 1022–1029 (2018).

    Article  Google Scholar 

  143. Bevill, M. D. et al. Number of cores needed to diagnose prostate cancer during MRI targeted biopsy decreases after the learning curve. Urol. Oncol. Semin. Orig. Investig. https://doi.org/10.1016/j.urolonc.2021.05.029 (2021).

    Article  Google Scholar 

  144. Costa, D. N. et al. Gleason grade group concordance between preoperative targeted biopsy and radical prostatectomy histopathologic analysis: a comparison between in-bore MRI-guided and MRI–transrectal US fusion prostate biopsies. Radiol. Imaging Cancer 3, e200123 (2021).

    Article  Google Scholar 

  145. Gnanapragasam, V. J. et al. Using prognosis to guide inclusion criteria, define standardised endpoints and stratify follow-up in active surveillance for prostate cancer. BJU Int. 124, 758–767 (2019).

    Article  Google Scholar 

  146. Kench, J. G. et al. Dataset for the reporting of prostate carcinoma in radical prostatectomy specimens: updated recommendations from the International Collaboration on Cancer Reporting. Virchows Arch. 475, 263–277 (2019).

    Article  Google Scholar 

  147. Egevad, L. et al. Standardization of Gleason grading among 337 European pathologists. Histopathology 62, 247–256 (2013).

    Article  Google Scholar 

  148. Chen, S. D., Fava, J. L. & Amin, A. Gleason grading challenges in the diagnosis of prostate adenocarcinoma: experience of a single institution. Virchows Arch. 468, 213–218 (2016).

    Article  Google Scholar 

  149. Siedow, M. et al. Impact of prostate biopsy secondary pathology review on radiotherapy management. Prostate 82, 210–215 (2022).

    Article  Google Scholar 

  150. Smith, E. B., Frierson, H. F., Mills, S. E., Boyd, J. C. & Theodorescu, D. Gleason scores of prostate biopsy and radical prostatectomy specimens over the past 10 years: is there evidence for systematic upgrading? Cancer 94, 2282–2287 (2002).

    Article  Google Scholar 

  151. Allsbrook, W. C. et al. Interobserver reproducibility of Gleason grading of prostatic carcinoma: urologic pathologists. Hum. Pathol. 32, 74–80 (2001).

    Article  Google Scholar 

  152. Short, E., Warren, A. Y. & Varma, M. Gleason grading of prostate cancer: a pragmatic approach. Diagn. Histopathol. 25, 371–378 (2019).

    Article  Google Scholar 

  153. Egevad, L., Delahunt, B., Yaxley, J. & Samaratunga, H. Evolution, controversies and the future of prostate cancer grading. Pathol. Int. 69, 55–66 (2019).

    Article  Google Scholar 

  154. Epstein, J. I., Feng, Z., Trock, B. J. & Pierorazio, P. M. Upgrading and downgrading of prostate cancer from biopsy to radical prostatectomy: incidence and predictive factors using the modified Gleason grading system and factoring in tertiary grades. Eur. Urol. 61, 1019–1024 (2012).

    Article  Google Scholar 

  155. Ozkan, T. A. et al. Interobserver variability in Gleason histological grading of prostate cancer. Scand. J. Urol. 50, 420–424 (2016).

    Article  Google Scholar 

  156. Kweldam, C. F., van Leenders, G. J. & van der Kwast, T. Grading of prostate cancer: a work in progress. Histopathology 74, 146–160 (2019).

    Article  Google Scholar 

  157. Egevad, L. et al. Utility of pathology imagebase for standardisation of prostate cancer grading. Histopathology 73, 8–18 (2018).

    Article  Google Scholar 

  158. Harnden, P. et al. Evaluation of the use of digital images for a national prostate core external quality assurance scheme. Histopathology 59, 703–709 (2011).

    Article  Google Scholar 

  159. Bulten, W. et al. Artificial intelligence assistance significantly improves Gleason grading of prostate biopsies by pathologists. Mod. Pathol. 34, 660–671 (2021).

    Article  Google Scholar 

  160. Nagpal, K. et al. Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer. npj Digit. Med. 2, 1–10 (2019).

    Google Scholar 

  161. Bulten, W. et al. Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study. Lancet Oncol. 21, 233–241 (2020).

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Radiology, Addenbrooke’s Hospital and University of Cambridge, Cambridge, UK

    Tristan Barrett

  2. Department of Medical Imaging, Radboud University Medical Center, Nijmegen, Netherlands

    Maarten de Rooij & Jelle O. Barentsz

  3. Department of Radiology, University College London Hospital NHS Foundation Trust, London, UK

    Francesco Giganti & Clare Allen

  4. Division of Surgery and Interventional Science, University College London, London, UK

    Francesco Giganti

  5. Paul Strickland Scanner Centre, Mount Vernon Hospital, Middlesex, UK

    Anwar R. Padhani

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T.B., M.D.R., F.G. and C.A. researched data for the article. All authors contributed substantially to discussion of the content. T.B., M.D.R. and F.G. wrote the article. All authors reviewed and edited the manuscript before submission.

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Nature Reviews Urology thanks C.K. Kim; P. Pinto, who co-reviewed with M. Rothberg; V. Panebianco; and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Barrett, T., de Rooij, M., Giganti, F. et al. Quality checkpoints in the MRI-directed prostate cancer diagnostic pathway. Nat Rev Urol 20, 9–22 (2023). https://doi.org/10.1038/s41585-022-00648-4

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