1. Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN estimates of ıncidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209-249. doi:10. 3322/caac.21660
2. Schröder FH, Hugosson J, Roobol MJ, et al. Screening and prostate-cancer mortality in a randomized European study. N Engl J Med. 2009; 360(13):1320-1328. doi:10.1056/NEJMoa0810084
3. Turkbey B, Pinto PA, Choyke PL. Imaging techniques for prostate cancer: implications for focal therapy. Nat Rev Urol. 2009;6(4):191-203. doi:10.1038/nrurol.2009.27
4. Turkbey B, Rosenkrantz AB, Haider MA, 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):340-351. doi: 10.1016/j.eururo.2019.02.033
5. Hambrock T, Somford DM, Huisman HJ, 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):453-461. doi: 10.1148/radiol.11091409
6. Donati OF, Mazaheri Y, Afaq A, et al. Prostate cancer aggressiveness: assessment with whole-lesion histogram analysis of the apparent diffusion coefficient. Radiology. 2014;271(1):143-152. doi:10.1148/radiol. 13130973
7. Vargas HA, Akin O, Franiel T, et al. Diffusion-weighted endorectal MR imaging at 3 T for prostate cancer: tumor detection and assessment of aggressiveness. Radiology. 2011;259(3):775-784. doi:10.1148/radiol. 11102066
8. Meyer HJ, 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):482. doi: 10.1186/s12885-020-06942-x
9. Barrett T, Priest AN, Lawrence EM, et al. Ratio of tumor to normal prostate tissue apparent diffusion coefficient as a method for quantifying DWI of the prostate. AJR Am J Roentgenol. 2015;205(6):W585-W593. doi:10.2214/AJR.15.14338
10. Woo S, Kim SY, Cho JY, Kim SH. Preoperative evaluation of prostate cancer aggressiveness: using ADC and ADC ratio in determining Gleason score. AJR Am J Roentgenol. 2016;207(1):114-120. doi:10.2214/AJR.15.15894
11. Gaur S, Harmon S, Rosenblum L, et al. Can apparent diffusion coefficient values assist PI-RADS version 2 DWI scoring? A correlation study using the PI-RADSv2 and International Society of Urological Pathology systems. AJR Am J Roentgenol. 2018;211(1):W33-W41. doi:10. 2214/AJR.17.18702
12. Bonekamp D, Kohl S, Wiesenfarth M, et al. Radiomic machine learning for characterization of prostate lesions with MRI: comparison to ADC values. Radiology. 2018;289(1):128-137. doi:10.1148/radiol.2018173064
13. Lucarelli NM, Villanova I, Maggialetti N, et al. Quantitative ADC: an additional tool in the evaluation of prostate cancer? J Pers Med. 2023; 13(9):1378. doi:10.3390/jpm13091378
14. Agrotis G, Pooch E, Abdelatty M, et al. Diagnostic performance of ADC and ADCratio in MRI-based prostate cancer assessment: a systematic review and meta-analysis. Eur Radiol. 2025;35(1):404-416. doi:10.1007/s00330-024-10890-6
15. Donati OF, Afaq A, Vargas HA, et al. Prostate MRI: evaluating tumor volume and apparent diffusion coefficient as surrogate biomarkers for predicting tumor Gleason score. Clin Cancer Res. 2014;20(14):3705-3711. doi:10.1158/1078-0432.CCR-14-0044
16. Scialpi M, Martorana E, Scialpi P, et al. MRI apparent diffusion coefficient (ADC): a biomarker for prostate cancer after radiation therapy. Turk J Urol. 2021;47(6):448-451. doi:10.5152/tud.2021.21274
17. Haider MA, Chung P, Sweet J, et al. Dynamic contrast-enhanced magnetic resonance imaging for localization of recurrent prostate cancer after external beam radiotherapy. Int J Radiat Oncol Biol Phys. 2008;70(2):425-430. doi:10.1016/j.ijrobp.2007.06.029
18. Wen J, Ji Y, Han J, Shen X, Qiu Y. Inter-reader agreement of the prostate imaging reporting and data system version v2.1 for detection of prostate cancer: a systematic review and meta-analysis. Front Oncol. 2022;12:1013941. doi:10.3389/fonc.2022.1013941
19. Park KJ, Choi SH, Kim MH, Kim JK, Jeong IG. 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. 2021;54(1):103-112. doi:10.1002/jmri.27546
20. Peng Y, Jiang Y, Yang C, et al. Quantitative analysis of multiparametric prostate MR images: differentiation between prostate cancer and normal tissue and correlation with Gleason score-a computer-aided diagnosis development study. Radiology. 2013;267(3):787-796. doi:10. 1148/radiol.13121454
21. Woodfield CA, Tung GA, Grand DJ, Pezzullo JA, Machan JT, Renzulli JF 2nd. Diffusion-weighted MRI of peripheral zone prostate cancer: comparison of tumor apparent diffusion coefficient with Gleason score and percentage of tumor on core biopsy. AJR Am J Roentgenol. 2010; 194(4):W316-W322. doi:10.2214/AJR.09.2651
22. Verma S, Rajesh A, Morales H, et al. Assessment of aggressiveness of prostate cancer: correlation of apparent diffusion coefficient with histologic grade after radical prostatectomy. AJR Am J Roentgenol. 2011; 196(2):374-381. doi:10.2214/AJR.10.4441
23. Washino S, Okochi T, Saito K, et al. 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 Int. 2017;119(2):225-233. doi:10.1111/bju.13465
24. Spadarotto N, Sauck A, Hainc N, Keller I, John H, Hohmann J. Quantitative evaluation of apparent diffusion coefficient values, ISUP grades and prostate-specific antigen density values of potentially malignant PI-RADS lesions. Cancers (Basel). 2023;15(21):5183. doi:10. 3390/cancers15215183
25. Karaarslan E, Kus AA, Alis D et al. Performance of apparent diffusion coefficient values and ratios for the prediction of prostate cancer aggressiveness across different MRI acquisition settings. Diagn Interv Radiol. 2022;28(1):12-20. doi:10.5152/dir.2022.20732
26. He D, Wang X, Fu C, et al. MRI-based radiomics models to assess prostate cancer, extracapsular extension and positive surgical margins. Cancer Imaging. 2021;21(1):46. doi:10.1186/s40644-021-00414-6
27. Akin O, Gultekin DH, Vargas HA, et al. Incremental value of diffusion weighted and dynamic contrast enhanced MRI in the detection of locally recurrent prostate cancer after radiation treatment: preliminary results. Eur Radiol. 2011;21(9):1970-1978. doi:10.1007/s00330-011-2130-6
28. Kwon D, Reis IM, Breto AL, et al. Classification of suspicious lesions on prostate multiparametric MRI using machine learning. J Med Imaging (Bellingham). 2018;5(3):034502. doi:10.1117/1.JMI.5.3.034502
29. Zhang L, Tang M, Chen S, Lei X, Zhang X, Huan Y. A meta-analysis of use of Prostate Imaging Reporting and Data System Version 2 (PI-RADS V2) with multiparametric MR imaging for the detection of prostate cancer. Eur Radiol. 2017;27(12):5204-5214. doi:10.1007/s00330-017-4843-7
30. Park SY, Kim CK, Park BK, Lee HM, Lee KS. Prediction of biochemical recurrence following radical prostatectomy in men with prostate cancer by diffusion-weighted magnetic resonance imaging: initial results. Eur Radiol. 2011;21(5):1111-1118. doi:10.1007/s00330-010-1999-9