Skip to main content

Advertisement

SpringerLink
Log in

Role of multiparametric prostate MRI in the management of prostate cancer

  • Topic Paper
  • Published:
World Journal of Urology Aims and scope Submit manuscript

Abstract

Introduction

Prostate cancer has traditionally been diagnosed by an elevation in PSA or abnormal exam leading to a systematic transrectal ultrasound (TRUS)-guided biopsy. This diagnostic pathway underdiagnoses clinically significant disease while over diagnosing clinically insignificant disease. In this review, we aim to provide an overview of the recent literature regarding the role of multiparametric MRI (mpMRI) in the management of prostate cancer.

Materials and Methods

A thorough literature review was performed using PubMed to identify articles discussing use of mpMRI of the prostate in management of prostate cancer.

Conclusion

The incorporation of mpMRI of the prostate addresses the shortcomings of the prostate biopsy while providing several other advantages. mpMRI allows some men to avoid an immediate biopsy and permits visualization of areas likely to harbor clinically significant cancer prior to biopsy to facilitate use of MR-targeted prostate biopsies. This allows for reduction in diagnosis of clinically insignificant disease as well as improved detection and better characterization of higher risk cancers, as well as the improved selection of patients for active surveillance. In addition, mpMRI can be used for selection and monitoring of patients for active surveillance and treatment planning during surgery and focal therapy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Mayes JM, Mouraviev V, Sun L, Tsivian M, Madden JF, Polascik TJ (2011) Can the conventional sextant prostate biopsy accurately predict unilateral prostate cancer in low-risk, localized, prostate cancer? Urol Oncol 29(2):166–170

    PubMed  Google Scholar 

  2. Schulte RT, Wood DP, Daignault S, Shah RB, Wei JT (2008) Utility of extended pattern prostate biopsies for tumor localization: pathologic correlations after radical prostatectomy. Cancer 113(7):1559–1565

    PubMed  PubMed Central  Google Scholar 

  3. Ahmed HU, El-Shater Bosaily A, Brown LC, Gabe R, Kaplan R, Parmar MK et al (2017) Diagnostic accuracy of multi-parametric MRI and TRUS biopsy in prostate cancer (PROMIS): a paired validating confirmatory study. Lancet 389(10071):815–822

    PubMed  Google Scholar 

  4. Ahdoot M, Wilbur AR, Reese SE, Lebastchi AH, Mehralivand S, Gomella PT et al (2020) MRI-targeted, systematic, and combined biopsy for prostate cancer diagnosis. N Engl J Med 382(10):917–928

    PubMed  PubMed Central  Google Scholar 

  5. Siddiqui MM, Rais-Bahrami S, Turkbey B, George AK, Rothwax J, Shakir N et al (2015) Comparison of MR/ultrasound fusion-guided biopsy with ultrasound-guided biopsy for the diagnosis of prostate cancer. JAMA 313(4):390–397

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Drost FH, Osses DF, Nieboer D, Steyerberg EW, Bangma CH, Roobol MJ et al (2019) Prostate MRI, with or without MRI-targeted biopsy, and systematic biopsy for detecting prostate cancer. Cochrane Database Syst Rev 4:012663

    Google Scholar 

  7. Kasivisvanathan V, Rannikko AS, Borghi M, Panebianco V, Mynderse LA, Vaarala MH et al (2018) MRI-targeted or standard biopsy for prostate-cancer diagnosis. N Engl J Med 378(19):1767–1777

    PubMed  Google Scholar 

  8. Lebastchi AH, Pinto PA (2019) The role of multiparametric MRI in biopsy-naive prostate cancer. Nat Rev Urol. 16(5):276–277

    PubMed  Google Scholar 

  9. Wysock JS, Mendhiratta N, Zattoni F, Meng X, Bjurlin M, Huang WC et al (2016) Predictive value of negative 3T multiparametric magnetic resonance imaging of the prostate on 12-core biopsy results. BJU Int 118(4):515–520

    PubMed  Google Scholar 

  10. Rouviere O, Puech P, Renard-Penna R, Claudon M, Roy C, Mege-Lechevallier F et al (2019) 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(1):100–109

    PubMed  Google Scholar 

  11. NICE Guidance - Prostate cancer: diagnosis and management: (c) NICE (2019) Prostate cancer: diagnosis and management. BJU Int 124(1):9–26

    Google Scholar 

  12. Vourganti S, Rastinehad A, Yerram N, Nix J, Volkin D, Hoang A et al (2012) Multiparametric magnetic resonance imaging and ultrasound fusion biopsy detect prostate cancer in patients with prior negative transrectal ultrasound biopsies. J Urol 188(6):2152–2157

    PubMed  PubMed Central  Google Scholar 

  13. Sidana A, Watson MJ, George AK, Rastinehad AR, Vourganti S, Rais-Bahrami S et al (2018) Fusion prostate biopsy outperforms 12-core systematic prostate biopsy in patients with prior negative systematic biopsy: a multi-institutional analysis. Urol Oncol 36(7):341 e1–e7

    PubMed  Google Scholar 

  14. Abreu AL (2020) The pillars for sustained growth of magnetic resonance imaging pathway for prostate cancer diagnosis: quality, reproducibility, accessibility, cost effectiveness, and training. Eur Urol 77(4):491–493

    PubMed  Google Scholar 

  15. van der Leest M, Israel B, Cornel EB, Zamecnik P, Schoots IG, van der Lelij H et al (2019) High diagnostic performance of short magnetic resonance imaging protocols for prostate cancer detection in biopsy-naive men: the next step in magnetic resonance imaging accessibility. Eur Urol 76(5):574–581

    PubMed  Google Scholar 

  16. Tafuri A, Ashrafi AN, Palmer S, Shakir A, Cacciamani GE, Iwata A et al (2020) One-Stop MRI and MRI/transrectal ultrasound fusion-guided biopsy: an expedited pathway for prostate cancer diagnosis. World J Urol 38(4):949–956

    CAS  PubMed  Google Scholar 

  17. Schroder FH, Hugosson J, Roobol MJ, Tammela TL, Ciatto S, Nelen V et al (2009) Screening and prostate-cancer mortality in a randomized European study. N Engl J Med 360(13):1320–1328

    PubMed  Google Scholar 

  18. Lilja H, Ulmert D, Vickers AJ (2008) Prostate-specific antigen and prostate cancer: prediction, detection and monitoring. Nat Rev Cancer 8(4):268–278

    CAS  PubMed  Google Scholar 

  19. Davis KM, Kelly SP, Luta G, Tomko C, Miller AB, Taylor KL (2014) The association of long-term treatment-related side effects with cancer-specific and general quality of life among prostate cancer survivors. Urology. 84(2):300–306

    PubMed  PubMed Central  Google Scholar 

  20. Turkbey B, Mani H, Aras O, Ho J, Hoang A, Rastinehad AR et al (2013) Prostate cancer: can multiparametric MR imaging help identify patients who are candidates for active surveillance? Radiology 268(1):144–152

    PubMed  PubMed Central  Google Scholar 

  21. Bloom JB, Hale GR, Gold SA, Rayn KN, Smith C, Mehralivand S et al (2019) Predicting gleason group progression for men on prostate cancer active surveillance: role of a negative confirmatory magnetic resonance imaging-ultrasound fusion biopsy. J Urol 201(1):84–90

    PubMed  Google Scholar 

  22. Klotz L, Loblaw A, Sugar L, Moussa M, Berman DM, Van der Kwast T et al (2019) Active surveillance magnetic resonance imaging study (ASIST): results of a randomized multicenter prospective trial. Eur Urol 75(2):300–309

    PubMed  Google Scholar 

  23. Klotz L, Pond G, Loblaw A, Sugar L, Moussa M, Berman D et al (2019) Randomized study of systematic biopsy versus magnetic resonance imaging and targeted and systematic biopsy in men on active surveillance (ASIST): 2-year postbiopsy follow-up. Eur Urol 77(3):311–317

    PubMed  Google Scholar 

  24. Amin A, Scheltema MJ, Shnier R, Blazevski A, Moses D, Cusick T et al (2020) The magnetic resonance imaging in active surveillance (MRIAS) trial: use of baseline multiparametric magnetic resonance imaging and saturation biopsy to reduce the frequency of surveillance prostate biopsies. J Urol 203(5):910–917

    PubMed  Google Scholar 

  25. Chesnut GT, Vertosick EA, Benfante N, Sjoberg DD, Fainberg J, Lee T et al (2020) Role of randomized study of systematic biopsy versus magnetic resonance imaging and targeted and systematic biopsy in men on active surveillance (ASIST): 2-year postbiopsy follow-up. Eur Urol 77(4):501–507

    PubMed  Google Scholar 

  26. Felker ER, Wu J, Natarajan S, Margolis DJ, Raman SS, Huang J et al (2016) Serial magnetic resonance imaging in active surveillance of prostate cancer: incremental value. J Urol 195(5):1421–1427

    PubMed  Google Scholar 

  27. Frye TP, George AK, Kilchevsky A, Maruf M, Siddiqui MM, Kongnyuy M et al (2017) Magnetic resonance imaging-transrectal ultrasound guided fusion biopsy to detect progression in patients with existing lesions on active surveillance for low and intermediate risk prostate cancer. J Urol 197(3 Pt 1):640–646

    PubMed  Google Scholar 

  28. Gold SA, Shih JH, Rais-Bahrami S, Bloom JB, Vourganti S, Singla N et al (2019) When to biopsy the seminal vesicles: a validated multiparametric magnetic resonance imaging and target driven model to detect seminal vesicle invasion in prostate cancer. J Urol 201:943–949

    Google Scholar 

  29. Wang L, Mullerad M, Chen HN, Eberhardt SC, Kattan MW, Scardino PT et al (2004) Prostate cancer: incremental value of endorectal MR imaging findings for prediction of extracapsular extension. Radiology 232(1):133–139

    PubMed  Google Scholar 

  30. Mehralivand S, Shih JH, Harmon S, Smith C, Bloom J, Czarniecki M et al (2019) A grading system for the assessment of risk of extraprostatic extension of prostate cancer at multiparametric MRI. Radiology 290(3):709–719

    PubMed  PubMed Central  Google Scholar 

  31. Mungovan SF, Sandhu JS, Akin O, Smart NA, Graham PL, Patel MI (2017) Preoperative membranous urethral length measurement and continence recovery following radical prostatectomy: a systematic review and meta-analysis. Eur Urol 71(3):368–378

    PubMed  Google Scholar 

  32. Kozikowski M, Malewski W, Michalak W, Dobruch J (2019) Clinical utility of MRI in the decision-making process before radical prostatectomy: systematic review and meta-analysis. PLoS ONE 14(1):e0210194

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Schiavina R, Bianchi L, Borghesi M, Dababneh H, Chessa F, Pultrone CV et al (2018) MRI displays the prostatic cancer anatomy and improves the bundles management before robot-assisted radical prostatectomy. J Endourol 32(4):315–321

    PubMed  Google Scholar 

  34. Couñago F, Sancho G, Catalá V, Hernández D, Recio M, Montemuiño S et al (2017) Magnetic resonance imaging for prostate cancer before radical and salvage radiotherapy: what radiation oncologists need to know. World J Clin Oncol. 8(4):305–319

    PubMed  PubMed Central  Google Scholar 

  35. Calio B, Kasson M, Sugano D, Ortman M, Gaitonde K, Verma S et al (2018) Multiparametric MRI: an opportunity for focal therapy of prostate cancer. Semin Roentgenol 53(3):227–233

    PubMed  Google Scholar 

  36. van Luijtelaar A, Greenwood BM, Ahmed HU, Barqawi AB, Barret E, Bomers JGR et al (2019) Focal laser ablation as clinical treatment of prostate cancer: report from a Delphi consensus project. World J Urol 37(10):2147–2153

    PubMed  PubMed Central  Google Scholar 

  37. Eggener SE, Yousuf A, Watson S, Wang S, Oto A (2016) Phase II evaluation of magnetic resonance imaging guided focal laser ablation of prostate cancer. J Urol 196(6):1670–1675

    PubMed  Google Scholar 

  38. Guillaumier S, Peters M, Arya M, Afzal N, Charman S, Dudderidge T et al (2018) A multicentre study of 5-year outcomes following focal therapy in treating clinically significant nonmetastatic prostate cancer. Eur Urol 74(4):422–429

    PubMed  PubMed Central  Google Scholar 

  39. Mendez MH, Passoni NM, Pow-Sang J, Jones JS, Polascik TJ (2015) Comparison of outcomes between preoperatively potent men treated with focal versus whole gland cryotherapy in a matched population. J Endourol 29(10):1193–1198

    PubMed  Google Scholar 

  40. Lindner U, Lawrentschuk N, Weersink RA, Davidson SR, Raz O, Hlasny E et al (2010) Focal laser ablation for prostate cancer followed by radical prostatectomy: validation of focal therapy and imaging accuracy. Eur Urol 57(6):1111–1114

    PubMed  Google Scholar 

  41. Matsugasumi T, Baco E, Palmer S, Aron M, Sato Y, Fukuda N et al (2015) Prostate Cancer Volume Estimation by Combining magnetic resonance imaging and targeted biopsy proven cancer core length: correlation with cancer volume. J Urol 194(4):957–965

    PubMed  Google Scholar 

  42. Nassiri N, Chang E, Lieu P, Priester AM, Margolis DJA, Huang J et al (2018) Focal therapy eligibility determined by magnetic resonance imaging/ultrasound fusion biopsy. J Urol 199(2):453–458

    PubMed  Google Scholar 

  43. Tay KJ, Scheltema MJ, Ahmed HU, Barret E, Coleman JA, Dominguez-Escrig J et al (2017) Patient selection for prostate focal therapy in the era of active surveillance: an International Delphi Consensus Project. Prostate Cancer Prostatic Dis. 20(3):294–299

    CAS  PubMed  Google Scholar 

  44. Muller BG, Futterer JJ, Gupta RT, Katz A, Kirkham A, Kurhanewicz J et al (2014) The role of magnetic resonance imaging (MRI) in focal therapy for prostate cancer: recommendations from a consensus panel. BJU Int. 113(2):218–227

    PubMed  Google Scholar 

  45. Pompe RS, Kuhn-Thoma B, Nagaraj Y, Veleva V, Preisser F, Leyh-Bannurah SR et al (2018) Validation of the current eligibility criteria for focal therapy in men with localized prostate cancer and the role of MRI. World J Urol 36(5):705–712

    PubMed  Google Scholar 

  46. Turkbey B, Rosenkrantz AB, Haider MA, Padhani AR, Villeirs G, Macura KJ et al (2019) Prostate imaging reporting and data system version 2.1: 2019 update of prostate imaging reporting and data system version 2. Eur Urol 76(3):340–351

    PubMed  Google Scholar 

  47. Gaur S, Turkbey B (2018) Prostate MR imaging for posttreatment evaluation and recurrence. Urol Clin North Am 45(3):467–479

    PubMed  Google Scholar 

  48. Cirillo S, Petracchini M, Scotti L, Gallo T, Macera A, Bona MC et al (2009) Endorectal magnetic resonance imaging at 15 Tesla to assess local recurrence following radical prostatectomy using T2-weighted and contrast-enhanced imaging. Eur Radiol 19(3):761–769

    PubMed  Google Scholar 

  49. Nehra A, Parker WP, Haloi R, Park SS, Mynderse LA, Lowe VJ et al (2018) Identification of recurrence sites following post-prostatectomy treatment for prostate cancer using (11)C-choline positron emission tomography and multiparametric pelvic magnetic resonance imaging. J Urol 199(3):726–733

    PubMed  Google Scholar 

  50. Kim CK, Park BK, Park W, Kim SS (2010) Prostate MR imaging at 3T using a phased-arrayed coil in predicting locally recurrent prostate cancer after radiation therapy: preliminary experience. Abdom Imaging 35(2):246–252

    PubMed  Google Scholar 

  51. Haider MA, Chung P, Sweet J, Toi A, Jhaveri K, Menard C et al (2008) Dynamic contrast-enhanced magnetic resonance imaging for localization of recurrent prostate cancer after external beam radiotherapy. Int J Radiat Oncol Biol Phys 70(2):425–430

    PubMed  Google Scholar 

  52. Barchetti F, Panebianco V (2014) Multiparametric MRI for recurrent prostate cancer post radical prostatectomy and postradiation therapy. Biomed Res Int 2014:316272

    PubMed  PubMed Central  Google Scholar 

  53. Hara T, Inoue Y, Satoh T, Ishiyama H, Sakamoto S, Woodhams R et al (2012) Diffusion-weighted imaging of local recurrent prostate cancer after radiation therapy: comparison with 22-core three-dimensional prostate mapping biopsy. Magn Reson Imaging 30(8):1091–1098

    PubMed  Google Scholar 

  54. Kim CK, Park BK, Lee HM (2009) Prediction of locally recurrent prostate cancer after radiation therapy: incremental value of 3T diffusion-weighted MRI. J Magn Reson Imaging 29(2):391–397

    PubMed  Google Scholar 

  55. Rouviere O, Vitry T, Lyonnet D (2010) Imaging of prostate cancer local recurrences: why and how? Eur Radiol 20(5):1254–1266

    PubMed  Google Scholar 

  56. Donaldson IA, Alonzi R, Barratt D, Barret E, Berge V, Bott S et al (2015) Focal therapy: patients, interventions, and outcomes–a report from a consensus meeting. Eur Urol 67(4):771–777

    PubMed  PubMed Central  Google Scholar 

  57. Martino P, Scattoni V, Galosi AB, Consonni P, Trombetta C, Palazzo S et al (2011) Role of imaging and biopsy to assess local recurrence after definitive treatment for prostate carcinoma (surgery, radiotherapy, cryotherapy, HIFU). World J Urol 29(5):595–605

    PubMed  Google Scholar 

  58. Kirkham AP, Emberton M, Hoh IM, Illing RO, Freeman AA, Allen C (2008) MR imaging of prostate after treatment with high-intensity focused ultrasound. Radiology 246(3):833–844

    PubMed  Google Scholar 

  59. Mertan FV, Greer MD, Borofsky S, Kabakus IM, Merino MJ, Wood BJ et al (2016) Multiparametric magnetic resonance imaging of recurrent prostate cancer. Top Magn Reson Imaging 25(3):139–147

    PubMed  PubMed Central  Google Scholar 

  60. Mehralivand S, Shih JH, Rais-Bahrami S, Oto A, Bednarova S, Nix JW et al (2018) A magnetic resonance imaging-based prediction model for prostate biopsy risk stratification. JAMA Oncol. 4(5):678–685

    PubMed  PubMed Central  Google Scholar 

  61. Mortezavi A, Krauter J, Gu A, Sonderer J, Bruhin J, Reeve KA et al (2019) Extensive histological sampling following focal therapy of clinically significant prostate cancer with high intensity focused ultrasound. J Urol 202(4):717–724

    PubMed  Google Scholar 

  62. Lebastchi AH, George AK, Polascik TJ, Coleman J, de la Rosette J, Turkbey B, Wood BJ, Gorin MA, Sidana A, Ghai S, Tay KJ (2020) Standardized nomenclature and surveillance methodologies after focal therapy and partial gland ablation for localized prostate cancer: an international multidisciplinary consensus. Eur Urol 1:1. https://doi.org/10.1016/j.eururo.2020.05.018

    Article  Google Scholar 

  63. Barret E, Harvey-Bryan KA, Sanchez-Salas R, Rozet F, Galiano M, Cathelineau X (2014) How to diagnose and treat focal therapy failure and recurrence? Curr Opin Urol 24(3):241–246

    PubMed  Google Scholar 

  64. Rouviere O, Girouin N, Glas L, Ben Cheikh A, Gelet A, Mege-Lechevallier F et al (2010) Prostate cancer transrectal HIFU ablation: detection of local recurrences using T2-weighted and dynamic contrast-enhanced MRI. Eur Radiol 20(1):48–55

    PubMed  Google Scholar 

  65. Dickinson L, Ahmed HU, Hindley RG, McCartan N, Freeman A, Allen C et al (2017) Prostate-specific antigen vs. magnetic resonance imaging parameters for assessing oncological outcomes after high intensity-focused ultrasound focal therapy for localized prostate cancer. Urol Oncol 35(1):30 e9–e15

    PubMed  Google Scholar 

  66. Kenigsberg AP, Llukani E, Deng FM, Melamed J, Zhou M, Lepor H (2018) The use of magnetic resonance imaging to predict oncological control among candidates for focal ablation of prostate cancer. Urology 112:121–125

    PubMed  Google Scholar 

  67. Bjurlin MA, Carroll PR, Eggener S, Fulgham PF, Margolis DJ, Pinto PA et al (2019) Update of the AUA policy statement on the use of multiparametric magnetic resonance imaging in the diagnosis, staging and management of prostate cancer. J Urol 203(4):706–712

    PubMed  Google Scholar 

  68. Calio B, Sidana A, Sugano D, Gaur S, Jain A, Maruf M et al (2017) Changes in prostate cancer detection rate of MRI-TRUS fusion vs systematic biopsy over time: evidence of a learning curve. Prostate Cancer Prostatic Dis. 20(4):436–441

    CAS  PubMed  Google Scholar 

  69. Meng X, Rosenkrantz AB, Huang R, Deng FM, Wysock JS, Bjurlin MA et al (2018) The institutional learning curve of magnetic resonance imaging-ultrasound fusion targeted prostate biopsy: temporal improvements in cancer detection in 4 years. J Urol 200(5):1022–1029

    PubMed  Google Scholar 

  70. Schoots IG (2018) MRI in early prostate cancer detection: how to manage indeterminate or equivocal PI-RADS 3 lesions? Transl Androl Urol. 7(1):70–82

    PubMed  PubMed Central  Google Scholar 

  71. Scialpi M, Martorana E, Aisa MC, Rondoni V, D’Andrea A, Bianchi G (2017) Score 3 prostate lesions: a gray zone for PI-RADS v2. Turk J Urol. 43(3):237–240

    PubMed  PubMed Central  Google Scholar 

  72. Moldovan PC, Van den Broeck T, Sylvester R, Marconi L, Bellmunt J, van den Bergh RCN et al (2017) 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(2):250–266

    PubMed  Google Scholar 

  73. Falagario UG, Martini A, Wajswol E, Treacy PJ, Ratnani P, Jambor I et al (2019) Avoiding unnecessary magnetic resonance imaging (MRI) and biopsies: negative and positive predictive value of mri according to prostate-specific antigen density, 4K score and risk calculators. Eur Urol Oncol. https://doi.org/10.1016/j.euo.2019.08.015

    Article  PubMed  Google Scholar 

  74. Laader A, Beiderwellen K, Kraff O, Maderwald S, Wrede K, Ladd ME et al (2017) 1.5 versus 3 versus 7 tesla in abdominal MRI: a comparative study. PLoS ONE 12(11):e0187528

    PubMed  PubMed Central  Google Scholar 

  75. Vos EK, Lagemaat MW, Barentsz JO, Futterer JJ, Zamecnik P, Roozen H et al (2014) Image quality and cancer visibility of T2-weighted magnetic resonance imaging of the prostate at 7 Tesla. Eur Radiol 24(8):1950–1958

    CAS  PubMed  Google Scholar 

  76. Greer MD, Lay N, Shih JH, Barrett T, Bittencourt LK, Borofsky S et al (2018) Computer-aided diagnosis prior to conventional interpretation of prostate mpMRI: an international multi-reader study. Eur Radiol 28(10):4407–4417

    PubMed  Google Scholar 

  77. Harmon SA, Tuncer S, Sanford T, Choyke PL, Turkbey B (2019) Artificial intelligence at the intersection of pathology and radiology in prostate cancer. Diagn Interv Radiol. 25(3):183–188

    PubMed  PubMed Central  Google Scholar 

  78. Gaur S, Lay N, Harmon SA, Doddakashi S, Mehralivand S, Argun B et al (2018) Can computer-aided diagnosis assist in the identification of prostate cancer on prostate MRI? a multi-center, multi-reader investigation. Oncotarget. 9(73):33804–33817

    PubMed  PubMed Central  Google Scholar 

  79. Goldenberg SL, Nir G, Salcudean SE (2019) A new era: artificial intelligence and machine learning in prostate cancer. Nat Rev Urol. 16(7):391–403

    PubMed  Google Scholar 

  80. Song Y, Zhang YD, Yan X, Liu H, Zhou M, Hu B et al (2018) Computer-aided diagnosis of prostate cancer using a deep convolutional neural network from multiparametric MRI. J Magn Reson Imaging 48(6):1570–1577

    PubMed  Google Scholar 

  81. Hectors SJ, Cherny M, Yadav KK, Beksac AT, Thulasidass H, Lewis S et al (2019) Radiomics Features Measured with Multiparametric Magnetic Resonance Imaging Predict Prostate Cancer Aggressiveness. J Urol 202(3):498–505

    PubMed  Google Scholar 

Download references

Funding

This research was made possible through the National Institutes of Health (NIH) Medical Research Scholars Program, a public–private partnership supported jointly by the NIH and generous contributions to the Foundation for the NIH from the Doris Duke Charitable Foundation, the American Association for Dental Research, the Colgate-Palmolive Company, Genentech, alumni of student research programs, and other individual supporters via contributions to the Foundation for the National Institutes of Health.

Author information

Author notes

  1. Baris Turkbey

    Present address: , 10 Center Drive Room B3B85, Bethesda, MD, 20814, USA

  2. Luke P. O’Connor and Amir H. Lebastchi have contributed equally to this work.

Authors and Affiliations

  1. Urologic Oncology Branch, National Cancer Institute, NIH, Bethesda, MD, USA

    Luke P. O’Connor, Amir H. Lebastchi & Peter A. Pinto

  2. Department of Urology, Istanbul Medipol University, Istanbul, Turkey

    Rahim Horuz

  3. Department of Urology, Lenox Hill Hospital Northwell Health, New York, NY, USA

    Ardeshir R. Rastinehad

  4. Division of Urology, University of Maryland School of Medicine, Baltimore, MD, USA

    M. Minhaj Siddiqui

  5. Department of Surgery, Central Clinical School, Monash University, Melbourne, VIC, Australia

    Jeremy Grummet

  6. Department of Urology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK

    Christof Kastner

  7. Imperial Prostate, Division of Surgery, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK

    Hashim U. Ahmed

  8. Molecular Imaging Program, National Cancer Institute, NIH, 10 Center Drive Room B3B85, Bethesda, MD, USA

    Baris Turkbey

Authors
  1. Luke P. O’Connor
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Amir H. Lebastchi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rahim Horuz
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ardeshir R. Rastinehad
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. M. Minhaj Siddiqui
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jeremy Grummet
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Christof Kastner
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Hashim U. Ahmed
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Peter A. Pinto
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Baris Turkbey
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

LPOC: Data collection, manuscript writing. AHL: Project development, data collection, manuscript writing. RH, ARR, MMS, JG, CK, HUA: Manuscipt writing. PAP, BT: Project development, manuscript writing.

Corresponding author

Correspondence to Baris Turkbey.

Ethics declarations

Conflict of interest

NIH and Philips have a Cooperative Research and Development Agreement. NIH has intellectual property in the field, including among other patents and patent applications, Patent: “System, methods, and instrumentation for image guided prostate treatment” US Patent number: 8948845, with inventor/author PP. NIH and Philips (InVivo Inc) have a licensing agreement. NIH and author PP receive royalties for a licensing agreement with Philips/InVivo Inc. NIH does not endorse or recommend any commercial products, processes, or services. The views and personal opinions of authors expressed herein do not necessarily reflect those of the US Government, nor reflect any official recommendation nor opinion of the NIH nor NCI.

Research involving human participants and/or animals

N/a.

Informed consent

N/a.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

O’Connor, L.P., Lebastchi, A.H., Horuz, R. et al. Role of multiparametric prostate MRI in the management of prostate cancer. World J Urol 39, 651–659 (2021). https://doi.org/10.1007/s00345-020-03310-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00345-020-03310-z

Keywords

Search

Navigation