The real-life challenges in prebiopsy prostate mp-MRI: Experiences from a Middle Eastern Country

Main Article Content

Reza Habibi Tirtashi Mohammad Hadi Gharib, MD Narges Tamaskani


Prostate Imaging Reporting and Data System (PI-RADS) has brought a standardized framework for the acquisition and interpretation of prostate multiparametric magnetic resonance imaging. To date, the most of studies implementing PI-RADS v2.1 in clinical practice have been conducted in developed Western countries. Our real-life experience from a developing country within the Middle East revealed that implementing PI-RADS v2.1 in prebiopsy multiparametric magnetic resonance imaging among 88 biopsy-naïve patients who underwent 12-core standard systematic biopsy, combined with magnetic resonance cognitive targeted biopsy, resulted in relatively lower cancer detection rates compared to developed countries. Therefore, we have discussed the limitations and challenges that might have influenced our results, including factors such as our equipment and technological capabilities, the experience and expertise of experts, and our biopsy methodology. Our lower cancer detection rates could be attributed to several factors, including the magnetic field strength of our scanner (1.5T), the shortage of expert and trained magnetic resonance imaging technologists in developing countries, the level of experience of our radiologist, the location and size of our index lesions, and inherent limitations of magnetic resonance cognitive targeted biopsy, particularly for lesions located at the apex and base of the prostate, as well as the number of biopsy cores obtained. Considering the challenges faced by radiologists in developing countries, incorporating artificial intelligence into the acquisition and interpretation of prostate multiparametric magnetic resonance imaging, and combining the PI-RADS scoring system with parameters with predictive value for prostate cancer diagnosis, like prostate-specific antigen density, prostate health index, and apparent diffusion coefficient value, could result in a significant improvement in prostate cancer detection and risk stratifications.

Keywords: prebiopsy prostate mp-MRI, real-life challenges in prebiopsy prostate mp-MRI

Article Details

How to Cite
TIRTASHI, Reza Habibi; GHARIB, Mohammad Hadi; TAMASKANI, Narges. The real-life challenges in prebiopsy prostate mp-MRI: Experiences from a Middle Eastern Country. Medical Research Archives, [S.l.], v. 12, n. 5, may 2024. ISSN 2375-1924. Available at: <>. Date accessed: 19 june 2024. doi:
Research Articles


1. Turkbey B, Rosenkrantz AB, Haider MA, Padhani AR, Villeirs G, Macura KJ, et al. 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.

2. Oerther B, Engel H, Bamberg F, Sigle A, Gratzke C, Benndorf M. Cancer detection rates of the PI-RADSv2. 1 assessment categories: systematic review and meta-analysis on lesion level and patient level. Prostate cancer and prostatic diseases. 2022;25(2):256-263.

3. Abbasi-Kangevari M, Ghamari S, Azangou-Khyavy M, Malekpour M, Rezaei N, Kolahi A, et al. The burden of prostate cancer in North Africa and Middle East, 1990-2019: Findings from the global burden of disease study. Frontiers in Oncology. 2022;12:961086-961086.

4. Weinreb JC, Barentsz JO, Choyke PL, Cornud F, Haider MA, Macura KJ, et al. PI-RADS prostate imaging–reporting and data system: 2015, version 2. European urology. 2016;69(1):16-40.

5. Tirtashi RH, Tamaskani N, Salamat F, Ghazimoghaddam B, Niakan B, Nematollahi N, et al. Clinical implementation PI-RADS v2. 1: Detection rate of clinically significant prostate cancer in PI-RADS 4 and 5 categories–The Real-life data. 2023;

6. Jalloul M, Miranda‐Schaeubinger M, Noor AM, Stein JM, Amiruddin R, Derbew HM, et al. MRI scarcity in low‐and middle‐income countries. NMR in Biomedicine. 2023; 36(12):e5022.

7. Engels RR, Israël B, Padhani AR, Barentsz JO. Multiparametric magnetic resonance imaging for the detection of clinically significant prostate cancer: what urologists need to know. Part 1: acquisition. European urology. 2020;77(4):457-468.

8. Ullrich T, Quentin M, Oelers C, Dietzel F, Sawicki L, Arsov C, et al. Magnetic resonance imaging of the prostate at 1.5 versus 3.0 T: A prospective comparison study of image quality. European journal of radiology. 2017;90:192-197.

9. Virarkar M, Szklaruk J, Diab R, Bassett Jr R, Bhosale P. Diagnostic value of 3.0 T versus 1.5 T MRI in staging prostate cancer: systematic review and meta-analysis. Polish Journal of Radiology. 2022;87(1):421-429.

10. Fütterer JJ, Tempany C. Prostate MRI and image quality: The radiologist's perspective. European journal of radiology. 2023; 165:110930.

11. Tamada T, Kido A, Takeuchi M, Yamamoto A, Miyaji Y, Kanomata N, et al. Comparison of PI-RADS version 2 and PI-RADS version 2.1 for the detection of transition zone prostate cancer. European journal of radiology. 2019;121:108704.

12. Byun J, Park KJ, Kim Mh, Kim JK. Direct comparison of PI‐RADS version 2 and 2.1 in transition zone lesions for detection of prostate cancer: Preliminary experience. Journal of Magnetic Resonance Imaging. 2020;52(2):577-586.

13. Wei C-g, Zhang Y-y, Pan P, Chen T, Yu H-c, Dai G-c, et al. Diagnostic accuracy and interobserver agreement of PI-RADS version 2 and version 2.1 for the detection of transition zone prostate cancers. American Journal of Roentgenology. 2021;216(5):1247-1256.

14. Salka BR, Shankar PR, Troost JP, Khalatbari S, Davenport MS. Effect of prostate MRI interpretation experience on PPV using PI-RADS version 2: a 6-year assessment among eight fellowship-trained radiologists. American Journal of Roentgenology. 2022;219(3):453-460.

15. Lee CH, Vellayappan B, Tan CH. Comparison of diagnostic performance and inter-reader agreement between PI-RADS v2. 1 and PI-RADS v2: systematic review and meta-analysis. The British Journal of Radiology. 2022;95(1131):20210509.

16. Lim CS, Abreu-Gomez J, Carrion I, Schieda N. Prevalence of prostate cancer in PI-RADS version 2.1 transition zone atypical nodules upgraded by abnormal DWI: Correlation with MRI-directed TRUS-guided targeted biopsy. American Journal of Roentgenology. 2021;216(3):683-690.

17. Zhang KS, Mayer P, Glemser PA, Tavakoli AA, Keymling M, Rotkopf LT, et al. Are T2WI PI-RADS sub-scores of transition zone prostate lesions biased by DWI information? A multi-reader, single-center study. European Journal of Radiology. 2023;167:111026.

18. Bhayana R, O'Shea A, Anderson MA, Bradley WR, Gottumukkala RV, Mojtahed A, et al. PI-RADS versions 2 and 2.1: interobserver agreement and diagnostic performance in peripheral and transition zone lesions among six radiologists. American Journal of Roentgenology. 2021;217(1):141-151.

19. Park SY, Park BK. Necessity of differentiating small (< 10 mm) and large (≥ 10 mm) PI-RADS 4. World Journal of Urology. 2020;38:1473-1479.

20. Kilic M, Madendere S, Vural M, Koseoglu E, Balbay MD, Esen T. The role of the size and number of index lesion in the diagnosis of clinically significant prostate cancer in patients with PI-RADS 4 lesions who underwent in-bore MRI-guided prostate biopsy. World Journal of Urology. 2023;41(2):449-454.

21. Venderink W, Bomers JG, Overduin CG, Padhani AR, de Lauw GR, Sedelaar MJ, et al. Multiparametric magnetic resonance imaging for the detection of clinically significant prostate cancer: what urologists need to know. Part 3: targeted biopsy. European Urology. 2020;77(4):481-490.

22. Wegelin O, Exterkate L, van der Leest M, Kummer JA, Vreuls W, de Bruin PC, et al. The FUTURE trial: a multicenter randomised controlled trial on target biopsy techniques based on magnetic resonance imaging in the diagnosis of prostate cancer in patients with prior negative biopsies. European urology. 2019;75(4):582-590.

23. Yamada Y, Ukimura O, Kaneko M, Matsugasumi T, Fujihara A, Vourganti S, et al. Moving away from systematic biopsies: image-guided prostate biopsy (in-bore biopsy, cognitive fusion biopsy, MRUS fusion biopsy)—literature review. World Journal of Urology. 2021;39:677-686.

24. Schouten MG, van der Leest M, Pokorny M, Hoogenboom M, Barentsz JO, Thompson LC, 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? European urology. 2017;71(6):896-903.

25. Patel HD, Halgrimson WR, Sweigert SE, Shea SM, Turk TM, Quek ML, et al. Variability in prostate cancer detection among radiologists and urologists using MRI fusion biopsy. BJUI compass. 2024;5(2):304-312.

26. Lu AJ, Syed JS, Ghabili K, Hsiang WR, Nguyen KA, Leapman MS, et al. Role of core number and location in targeted magnetic resonance imaging-ultrasound fusion prostate biopsy. European urology. 2019;76(1):14-17.

27. Kenigsberg AP, Renson A, Rosenkrantz AB, Huang R, Wysock JS, Taneja SS, et al. Optimizing the number of cores targeted during prostate magnetic resonance imaging fusion target biopsy. European urology oncology. 2018;1(5):418-425.

28. Zhang M, Milot L, Khalvati F, Sugar L, Downes M, Baig SM, et al. Value of increasing biopsy cores per target with cognitive MRI-targeted transrectal US prostate biopsy. Radiology. 2019;291(1):83-89.

29. Wang S, Kozarek J, Russell R, Drescher M, Khan A, Kundra V, et al. Diagnostic Performance of Prostate-specific Antigen Density for Detecting Clinically Significant Prostate Cancer in the Era of Magnetic Resonance Imaging: A Systematic Review and Meta-analysis. European Urology Oncology. 2023;

30. Haj-Mirzaian A, Burk KS, Lacson R, Glazer DI, Saini S, Kibel AS, et al. Magnetic Resonance Imaging, Clinical, and Biopsy Findings in Suspected Prostate Cancer: A Systematic Review and Meta-Analysis. JAMA Network Open. 2024;7(3):e244258-e244258.

31. Fan Y-H, Pan P-H, Cheng W-M, Wang H-K, Shen S-H, Liu H-T, et al. The Prostate Health Index aids multi-parametric MRI in diagnosing significant prostate cancer. Scientific reports. 2021;11(1):1286.

32. Zhou Y, Fu Q, Shao Z, Zhang K, Qi W, Geng S, et al. Nomograms Combining PHI and PI-RADS in Detecting Prostate Cancer: A Multicenter Prospective Study. Journal of Clinical Medicine. 2023;12(1):339.

33. Shaish H, Kang SK, Rosenkrantz AB. The utility of quantitative ADC values for differentiating high-risk from low-risk prostate cancer: a systematic review and meta-analysis. Abdominal Radiology. 2017;42:260-270.

34. Meyer H-J, Wienke A, Surov A. Discrimination between clinical significant and insignificant prostate cancer with apparent diffusion coefficient–a systematic review and meta analysis. BMC cancer. 2020;20:1-11.

35. Kim H, Kang SW, Kim J-H, Nagar H, Sabuncu M, Margolis DJ, et al. The role of AI in prostate MRI quality and interpretation: Opportunities and challenges. European Journal of Radiology. 2023:110887.

36. Roest C, Fransen SJ, Kwee TC, Yakar D. Comparative performance of deep learning and radiologists for the diagnosis and localization of clinically significant prostate cancer at MRI: a systematic review. Life. 2022;12(10):1490.

37. Zhao L-T, Liu Z-Y, Xie W-F, Shao L-Z, Lu J, Tian J, et al. What benefit can be obtained from magnetic resonance imaging diagnosis with artificial intelligence in prostate cancer compared with clinical assessments? Military Medical Research. 2023;10(1):29.