Prostate cancer. Future of using quantitative magnetic resonance imaging

Background. Prostate cancer is the 2nd most common malignant neoplasms among adult males. Magnetic resonance imaging (MRI) is the method of choice in radiological diagnostics of this disease allowing to noninvasively evaluate the prostate. Currently, the PI-RADS (Prostate Imaging Reporting and Data System) system is widely used. However, its assessment is subjective, or “by eye”, therefore the possibility of enhancing qualitative analysis with quantitative should be considered an excellent prospect.

Aim. To determine correlations between the apparent diffusion coefficient (ADC) and PI-RADS score which will allow to move from subjectivity in evaluation of prostate MRI results and make them independent of radiologists’ experience.

Materials and methods. The pilot retrospective study included MRI data of 28 patients with verified prostate cancer from the period between 2020 and 2022.

Results. Total PI-RADS score showed strong statistically significant negative correlation with mean ADC (r = –0.85; p <0.001) and minimum ADC (r = –0.82; p <0.001). PI-RADS score correlation with prostate-specific antigen level did not show statistical significance (p = 0.162). Total regression was statistically significant (R² = 0.73; F (4.13) = 8.799; p = 0.001). It was found that PI-RADS score depended on mean ADC (p <0.001) and prostate-specific antigen level (p = 0.013). Additionally, linear regression models were developed to predict Gleason score but with the current dataset they did not show statistical significance, possibly due to the small sample size.

Conclusion. The use of quantitative MRI in diagnosis of prostate cancer is a promising method which allows to objectivate PI-RADS score and to decrease the number of unjustified biopsies in the future. The key reason why quantitative MRI cannot be widely implemented is that ADC values are affected by a large number of external parameters, and ADC values can significantly vary in different devices. Therefore, in the future standardization of the prostate scanning protocol with optimal selection of b-value and minimization of factors affecting ADC measurement will allow to achieve more reliable comparative metrics.

1. Prostate. World Health Organization. Cancer today. Available at: https://gco.iarc.who.int/media/globocan/factsheets/cancers/27-prostate-fact-sheet.pdf

2. Malignant tumors in Russia in 2021 (morbidity and mortality). Eds.: А.D. Kaprin, V.V. Starinskiy, A.O. Shakhzadova. Moscow: MNIOI im. P.A. Gertsena – filial FGBU “NMITS radiologii” Minzdrava Rossii, 2022. 252 p. (In Russ.).

3. Gleason D., Mellinger G. Prediction of prognosis for prostatic adenocarcinoma by combined histological grading and clinical staging. J Urology 1974;111(1):58–64. DOI: 10.1016/S0022-5347(17)59889-4

4. Lomas D., Ahmed H.U. All change in the prostate cancer diagnostic pathway. Nat Rev Clin Oncol 2020;17(6):372–81. DOI: 10.1038/s41571-020-0332-z

5. Turkbey B., Rosenkrantz A., Haider M. 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(3):340–51. DOI: 10.1016/j.eururo.2019.02.033

6. Abuladze L.R., Govorov A.V., Sinitsyn V.E. Magnetic resonance imaging evaluation of focal therapy efficacy for prostate cancer. Vestnik rentgenologii i radiologii = Journal of Radiology and Nuclear Medicine 2023;104(1):90–100. (In Russ.). DOI: 10.20862/0042-4676-2023-104-1-90-100

7. Barentsz J., Richenberg J., Clements R. et al. ESUR prostate MR guidelines 2012. Eur Radiol 2012;22(4):746–57. DOI: 10.1007/s00330-011-2377-y

8. Park S., Jung D., Oh Y. et al. Prostate cancer: PI-RADS Version 2 helps preoperatively predict clinically significant cancers. Radiology 2016;280(1):108–16. DOI: 10.1148/radiol.16151133

9. Abuladze L.R., Semenov D.S., Panina O.Yu., Vasiliev Yu.A. Optimized biparametric magnetic resonance imaging protocol for prostate cancer detection. Digital Diagnostics 2022;(3):166–77. (In Russ.). DOI: 10.17816/DD108484

10. Kızılay F., Çelik S., Sözen S. et al. Correlation of Prostate-Imaging Reporting and Data Scoring System scoring on multiparametric prostate magnetic resonance imaging with histopathological factors in radical prostatectomy material in Turkish prostate cancer patients: a multicenter study of the Urooncology Association. Prostate Int 2020;8(1):10–5. DOI: 10.1016/j.prnil.2020.01.001

11. Pripatnanont W., Opanuraks J., Prasopsanti K. et al. A correlation of PI-RADS score and pathological grading outcome post radical prostatectomy: a retrospective review. Insight Urology 2021;42(2):110–6. DOI: 10.52786/isu.a.32

12. Lipana K., Albano G., Arcinas R. et al. The correlation between PI-RADS score and the detection of prostate cancer using MRI-ultrasound fusion-guided transperineal prostate biopsy: the first Philippine report. PJU [Internet] 2019;29(1):45–53. [cited 2024 Sep. 13]. Available at: https://pjuonline.com/index.php/ pju/article/view/89

13. Katz A., Liu C., Kosinski K. Histopathologic correlation of PI-RADS V.2 lesions on 3T multiparametric prostate MRI. J Clin Oncol 2016;34(2_suppl):10. DOI: 10.1200/jco.2016.34.2_suppl.10

14. Callender T., Emberton M., Morris S. et al. Benefit, harm, and cost-effectiveness associated with magnetic resonance imaging before biopsy in age-based and risk-stratified screening for prostate cancer. JAMA Netw Open 2021;4(3):e2037657. DOI: 10.1001/jamanetworkopen.2020.37657

15. Schoots I.G. MRI in early prostate cancer detection: how to manage indeterminate or equivocal PI-RADS 3 lesions? Transl Androl Urol 2018;7(1):70–82. DOI: 10.21037/tau.2017.12.31

16. Schlenker B., Apfelbeck M., Armbruster M. et al. Comparison of PIRADS 3 lesions with histopathological findings after MRI-fusion targeted biopsy of the prostate in a real worldsetting. Clin Hemorheol Microcirc 2019;71(2):165–70. DOI: 10.3233/CH-189407

17. Liddell H., Jyoti R., Haxhimolla H.Z. MP-MRI prostate characterised PIRADS 3 lesions are associated with a low risk of clinically significant prostate cancer – a retrospective review of 92 biopsied PIRADS 3 lesions. Curr Urol 2015;8(2):96–100. DOI: 10.1159/000365697

18. Drevik J., Dalimov Z., Uzzo R. et al. Utility of PSA density in patients with PI-RADS 3 lesions across a large multi-institutional collaborative. Urol Oncol 2022;40(11):490.e1–6. DOI: 10.1016/j.urolonc.2022.08.003

19. Munjal A., Leslie S.W. Gleason Score. [Updated 2023 May 1]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing, 2024. Available at: https://www.ncbi.nlm.nih.gov/books/NBK553178/

20. Huber P., Afzal N., Arya M. et al. Focal HIFU therapy for anterior compared to posterior prostate cancer lesions. World J Urol 2021;39(4):1115–9. DOI: 10.1007/s00345-020-03297-7

21. The use of PI-RADS system in MR diagnostics of the prostate: Methodological recommendations. Contributors: A.E. Nikolaev, I.A. Blokhin, A.N. Shapiev et al. “Best practices in radiological and instrumental diagnostics” series. Issue 31. Moscow: GBUZ “NPKTS DiT DZM”, 2019. 26 p. (In Russ.).

22. Manetta R., Palumbo P., Gianneramo C. et al. Correlation between ADC values and Gleason score in evaluation of prostate cancer: multicentre experience and review of the literature. Gland Surg 2019;8(S3):S216–22. DOI: 10.21037/gs.2019.05.02

23. Tamada T., Prabhu V., Li J. et al. Assessment of prostate cancer aggressiveness using apparent diffusion coefficient values: impact of patient race and age. Abdom Radiol 2017;42(6):1744–51. DOI: 10.1007/s00261-017-1058-y

24. Alessandrino F., Taghipour M., Hassanzadeh E. et al. Predictive role of PI-RADSv2 and ADC parameters in differentiating Gleason pattern 3 + 4 and 4 + 3 prostate cancer. Abdom Radiol 2019;44(1):279–85. DOI: 10.1007/s00261-018-1718-6

25. Turkbey B., Shah V., Pang Y. et al. Is Apparent diffusion coefficient associated with clinical risk scores for prostate cancers that are visible on 3-T MR images? Radiology 2011;258(2):488–95. DOI: 10.1148/radiol.10100667

26. Wang X., Wang B., Gao Z. et al. Diffusion-weighted imaging of prostate cancer: Correlation between apparent diffusion coefficient values and tumor proliferation. J Magn Reson Imaging 2009;29(6):1360–6. DOI: 10.1002/jmri.21797

27. Tamada T., Sone T., Jo Y. et al. Apparent diffusion coefficient values in peripheral and transition zones of the prostate: comparison between normal and malignant prostatic tissues and correlation with histologic grade. J Magn Reson Imaging 2008;28(3):720–6. DOI: 10.1002/jmri.21503

28. Hambrock T., Somford D., Huisman H. et al. Relationship between apparent diffusion coefficients at 3.0-T MR imaging and Gleason grade in peripheral zone prostate cancer. Radiology 2011;259(2):453–61. DOI: 10.1148/radiol.11091409

29. Zelhof B., Pickles M., Liney G. et al. Correlation of diffusionweighted magnetic resonance data with cellularity in prostate cancer. BJU Int 2009;103(7):883–8. DOI: 10.1111/j.1464-410X.2008.08130.x

30. Jyoti R., Jain T., Haxhimolla H. et al. Correlation of apparent diffusion coefficient ratio on 3.0 T MRI with prostate cancer Gleason score. Eur J Radiol Open 2018;5:58–63. DOI: 10.1016/j.ejro.2018.03.002

31. Girometti R., Giannarini G., Greco F. et al. Interreader agreement of PI-RADS v. 2 in assessing prostate cancer with multiparametric MRI: A study using whole-mount histology as the standard of reference. J Magn Reson Imaging 2019;49(2):546–55. DOI: 10.1002/jmri.26220

32. Wei C., Zhang Y., Pan P. et al. Diagnostic accuracy and interobserver agreement of PI-RADS version 2 and version 2.1 for the detection of transition zone prostate cancers. Am J Roentgenol 2021;216(5):1247–56. DOI: 10.2214/AJR.20.23883

33. Gupta R., Mehta K., Turkbey B. et al. PI-RADS: past, present, and future. J Magn Reson Imaging 2020;52(1):33–53. DOI: 10.1002/jmri.2689

34. Jordan E., Fiske C., Zagoria R. et al. PI-RADS v2 and ADC values: is there room for improvement? Abdom Radiol 2018;43(11):3109–16. DOI: 10.1007/s00261-018-1557-5

35. Polanec S., Helbich T., Bickel H. et al. Quantitative apparent diffusion coefficient derived from diffusion-weighted imaging has the potential to avoid unnecessary MRI-guided biopsies of mpMRI-detected PI-RADS 4 and 5 lesions. Invest Radiol 2018;53(12):736–41. DOI: 10.1097/RLI.0000000000000498

36. Glazer D., Hassanzadeh E., Fedorov A. et al. Diffusion-weighted endorectal MR imaging at 3T for prostate cancer: correlation with tumor cell density and percentage Gleason pattern on whole mount pathology. Abdom Radiol 2017;42(3):918–25. DOI: 10.1007/s00261-016-0942-1

37. Fennessy F., Maier S. Quantitative diffusion MRI in prostate cancer: image quality, what we can measure and how it improves clinical assessment. Eur J Radiol 2023;167:111066. DOI: 10.1016/j.ejrad.2023.111066

38. Sergunova K.A. Study and development of methods and approaches to control characteristics of magnetic resonance and X-ray computed tomographs. Dis. … candidate of technical sciences. Saint Petersburg, 2019. 17 p. (In Russ.).