Assessment of prostate imaging reporting and data system version 2.1 false-positive category 4 and 5 lesions in clinically significant prostate cancer
Xiangyu Wang, Weizong Liu, Fan Lin
Химия и современные технологии
https://doi.org/10.1007/s00261-021-03023-w
Purpose
To determine the incidence and false-positive rates of clinically significant prostate cancer (CSPC) in prostate imaging reporting and data system (PI-RADS) category 4 and 5 lesions using PI-RADS v2.1.
Methods
One hundred and eighty-two lesions in 169 subjects with a PI-RADS score of 4 or 5 were included in our study. Lesions with clinically insignificant prostate cancer (CIPC) or benign pathologic findings were reviewed and categorized by a radiologist. The initial comparison of demographic and clinical data was performed by t-test and χ2 test, and then the logistic regression model was used to determine factors associated with CIPC or benign pathological findings.
Results
Of the 182 PI-RADS category 4 and 5 lesions, 84.6% (154/182) were prostate cancer (PCa), 73.1% (133/182) were CSPC, and 26.9% (49/182) were CIPC or benign pathologic findings. The false-positive cases included 44.9% (22/49) with inflammation, 42.9% (21/49) with CIPC, 8.2% (4/49) with BPH nodules and 4.1% (2/49) with normal anatomy cases. In multivariate analysis, factors associated with CIPC or benign features included those in both the peripheral zone (PZ) and central gland (CG) (odds ratio [OR] 0.062; p = 0.003) and a low prostate-specific antigen density (PSAD) (OR 0.34; p = 0.012).
Conclusion
The integration of clinical information (PSAD and lesion location) into mpMRI to identify lesions helps with obtaining a clinically significant diagnosis and decision-making.
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People’s Hospital, 3002 SunGangXi Road, Shenzhen, 518035, ChinaXiangyu Wang, Yi Lei & Fan Lin
- Department of Ultrasonography, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People’s Hospital, 3002 SunGangXi Road, Shenzhen, 518035, ChinaWeizong Liu
- Department of Radiology, Shenzhen University General Hospital, 1098 XueYuan Road, Shenzhen, 518055, ChinaGuangyao Wu
- Barentsz J, Richenberg J, Clements R, Choyke P, Verma S, Villeirs G, Rouviere O, Logager V, Fütterer J. ESUR prostate MR guidelines 2012. European radiology 2012;22(4):746-757. https://doi.org/10.1007/s00330-011-2377-y
- Weinreb J, Barentsz J, Choyke P, Cornud F, Haider M, Macura K, Margolis D, Schnall M, Shtern F, Tempany C, Thoeny H, Verma S. PI-RADS Prostate Imaging - Reporting and Data System: 2015, Version 2. European urology 2016;69(1):16-40. https://doi.org/10.1016/j.eururo.2015.08.052
- Turkbey B, Rosenkrantz A, Haider M, Padhani A, Villeirs G, Macura K, Tempany C, Choyke P, Cornud F, Margolis D, Thoeny H, Verma S, Barentsz J, Weinreb J. Prostate Imaging Reporting and Data System Version 2.1: 2019 Update of Prostate Imaging Reporting and Data System Version 2. European urology 2019;76(3):340–351. https://doi.org/10.1016/j.eururo.2019.02.033
- Dugan J, Bostwick D, Myers R, Qian J, Bergstralh E, Oesterling J. The definition and preoperative prediction of clinically insignificant prostate cancer. JAMA 1996;275(4):288-294. https://doi.org/10.1001/jama.275.4.288
- Chamie K, Sonn G, Finley D, Tan N, Margolis D, Raman S, Natarajan S, Huang J, Reiter R. The role of magnetic resonance imaging in delineating clinically significant prostate cancer. Urology 2014;83(2):369-375. https://doi.org/10.1016/j.urology.2013.09.045
- Loffroy R, Chevallier O, Moulin M, Favelier S, Genson P, Pottecher P, Crehange G, Cochet A, Cormier L. Current role of multiparametric magnetic resonance imaging for prostate cancer. Quantitative imaging in medicine and surgery 2015;5(5):754-764. https://doi.org/10.3978/j.issn.2223-4292.2015.10.08
- Greer M, Brown A, Shih J, Summers R, Marko J, Law Y, Sankineni S, George A, Merino M, Pinto P, Choyke P, Turkbey B. Accuracy and agreement of PIRADSv2 for prostate cancer mpMRI: A multireader study. Journal of magnetic resonance imaging : JMRI 2017;45(2):579-585. https://doi.org/10.1002/jmri.25372
- Ahmed H, El-Shater Bosaily A, Brown L, Gabe R, Kaplan R, Parmar M, Collaco-Moraes Y, Ward K, Hindley R, Freeman A, Kirkham A, Oldroyd R, Parker C, Emberton M. Diagnostic accuracy of multi-parametric MRI and TRUS biopsy in prostate cancer (PROMIS): a paired validating confirmatory study. Lancet (London, England) 2017;389(10071):815-822. https://doi.org/10.1016/s0140-6736(16)32401-1
- Mehralivand S, Bednarova S, Shih J, Mertan F, Gaur S, Merino M, Wood B, Pinto P, Choyke P, Turkbey B. Prospective Evaluation of PI-RADS™ Version 2 Using the International Society of Urological Pathology Prostate Cancer Grade Group System. The Journal of urology 2017;198(3):583-590. https://doi.org/10.1016/j.juro.2017.03.131
- Tan N, Lin W, Khoshnoodi P, Asvadi N, Yoshida J, Margolis D, Lu D, Wu H, Sung K, Lu D, Huang J, Raman S. In-Bore 3-T MR-guided Transrectal Targeted Prostate Biopsy: Prostate Imaging Reporting and Data System Version 2-based Diagnostic Performance for Detection of Prostate Cancer. Radiology 2017;283(1):130-139. https://doi.org/10.1148/radiol.2016152827
- Rosenkrantz A, Taneja S. Radiologist, be aware: ten pitfalls that confound the interpretation of multiparametric prostate MRI. AJR American journal of roentgenology 2014;202(1):109-120. https://doi.org/10.2214/ajr.13.10699
- Noworolski S, Vigneron D, Chen A, Kurhanewicz J. Dynamic contrast-enhanced MRI and MR diffusion imaging to distinguish between glandular and stromal prostatic tissues. Magnetic resonance imaging 2008;26(8):1071-1080. https://doi.org/10.1016/j.mri.2008.01.033
- Bour L, Schull A, Delongchamps N, Beuvon F, Muradyan N, Legmann P, Cornud F. Multiparametric MRI features of granulomatous prostatitis and tubercular prostate abscess. Diagnostic and interventional imaging 2013;94(1):84-90. https://doi.org/10.1016/j.diii.2012.09.001
- Distler F, Radtke J, Bonekamp D, Kesch C, Schlemmer H, Wieczorek K, Kirchner M, Pahernik S, Hohenfellner M, Hadaschik B. The Value of PSA Density in Combination with PI-RADS™ for the Accuracy of Prostate Cancer Prediction. The Journal of urology 2017;198(3):575-582. https://doi.org/10.1016/j.juro.2017.03.130
- Radtke J, Wiesenfarth M, Kesch C, Freitag M, Alt C, Celik K, Distler F, Roth W, Wieczorek K, Stock C, Duensing S, Roethke M, Teber D, Schlemmer H, Hohenfellner M, Bonekamp D, Hadaschik B. Combined Clinical Parameters and Multiparametric Magnetic Resonance Imaging for Advanced Risk Modeling of Prostate Cancer-Patient-tailored Risk Stratification Can Reduce Unnecessary Biopsies. European urology 2017;72(6):888-896. https://doi.org/10.1016/j.eururo.2017.03.039
- Washino S, Okochi T, Saito K, Konishi T, Hirai M, Kobayashi Y, Miyagawa T. Combination of prostate imaging reporting and data system (PI-RADS) score and prostate-specific antigen (PSA) density predicts biopsy outcome in prostate biopsy naïve patients. BJU international 2017;119(2):225-233. https://doi.org/10.1111/bju.13465
- StataCorp. Stata Statistical Software: Release 15. College Station, TX: StataCorp LLC. 2017
- Akin OThai J, Narayanan H, George A, Siddiqui M, Shah P, Mertan F, Merino M, Pinto P, Choyke P, Wood B, Turkbey B. Validation of PI-RADS Version 2 in Transition Zone Lesions for the Detection of Prostate Cancer. Radiology 2018;288(2):485-491. https://doi.org/10.1148/radiol.2018170425
- Truong M, Wang B, Gordetsky J, Nix J, Frye T, Messing E, Thomas J, Feng C, Rais-Bahrami S. Multi-institutional nomogram predicting benign prostate pathology on magnetic resonance/ultrasound fusion biopsy in men with a prior negative 12-core systematic biopsy. Cancer 2018;124(2):278-285. https://doi.org/10.1002/cncr.31051
- Sheridan A, Nath S, Aneja S, Syed J, Pahade J, Mathur M, Sprenkle P, Weinreb J, Spektor M. MRI-Ultrasound Fusion Targeted Biopsy of Prostate Imaging Reporting and Data System Version 2 Category 5 Lesions Found False-Positive at Multiparametric Prostate MRI. AJR American journal of roentgenology 2018;210(5):W218-W225. https://doi.org/10.2214/ajr.17.18680
- Rais-Bahrami S, Nix J, Turkbey B, Pietryga J, Sanyal R, Thomas J, Gordetsky J. Clinical and multiparametric MRI signatures of granulomatous prostatitis. Abdominal radiology (New York) 2017;42(7):1956-1962. https://doi.org/10.1007/s00261-017-1080-0
- Nagel K, Schouten M, Hambrock T, Litjens G, Hoeks C, ten Haken B, Barentsz J, Fütterer J. Differentiation of prostatitis and prostate cancer by using diffusion-weighted MR imaging and MR-guided biopsy at 3 T. Radiology 2013;267(1):164-172. https://doi.org/10.1148/radiol.12111683
- Esen M, Onur M, Akpolat N, Orhan I, Kocakoc E. Utility of ADC measurement on diffusion-weighted MRI in differentiation of prostate cancer, normal prostate and prostatitis. Quantitative imaging in medicine and surgery 2013;3(4):210-216. https://doi.org/10.3978/j.issn.2223-4292.2013.08.06
- Akin O, Sala E, Moskowitz C, Kuroiwa K, Ishill N, Pucar D, Scardino P, Hricak H. Transition zone prostate cancers: features, detection, localization, and staging at endorectal MR imaging. Radiology 2006;239(3):784-792. https://doi.org/10.1148/radiol.2392050949
- Gordetsky J, Ullman D, Schultz L, Porter K, Del Carmen Rodriguez Pena M, Calderone C, Nix J, Ullman M, Bae S, Rais-Bahrami S. Histologic findings associated with false-positive multiparametric magnetic resonance imaging performed for prostate cancer detection. Human pathology 2019;83:159–165. https://doi.org/10.1016/j.humpath.2018.08.021