Synthetic MRI of prostate: correlation of T1 and T2-mapping with PI-RADS v2 scores
Keywords:
quantitative MRI, synthetic MRI, functional magnetic resonance imaging, prostate cancer, T1 mapping, T2 mappingAbstract
Background and aim: There has been a drive to develop methods of quantitative Magnetic Resonance Imaging (MRI) imaging such as the calculation of T1 and T2 relaxation times and ADC values from diffusion-weighted imaging (DWI) to develop imaging biomarkers that complement subjective radiological assessment. This retrospective study aims to evaluate if T1 and T2 relaxation times are significant predictors of malignancy, correlating them with the PI-RADS v2 scores.
Methods: This is a retrospective, monocentric, observational study, which included 33 consecutive patients with clinically significant prostatic cancer subjected to prostate MRI by regular clinical practice. We used T1 MP2RAGE and T2-multi-TE FSE 2D sequences with a reconstruction of T1 and T2 maps at the dedicated workstation. Lesions were identified by a radiologist who attributed the PI-RADSv2 score and then traced the Regions-of-Interest (ROI)also in the corresponding areas of healthy tissue. Wilcoxon signed-rank test in fixed ranks was used for comparison.
Results: We found statistically significant differences between relaxation time of the tumor and healthy tissue of the peripheral zone (PZ) (T1maps: p=0.043) (T2maps: p=0.043), and the transition zone (TZ) (T1maps: p=0.018) (T2maps: p=0.062). The Spearman test shows a tendency to a correlation between relative PI-RADS scores and T2-times within the peripheral zone(p=0.060) and T1-times within the transition zone (p-value=0.053).
Conclusions: There is a significant difference between the T1 and T2-relaxation times of pathological tissue and that of healthy prostate, both for lesions in the TZ as well as in the PZ. This reflects the intrinsic physical characteristics of the analyzed tissues represented as relaxation times of transverse and longitudinal magnetization. There is also a tendency to a correlation between PIRADS scores and T1/T2 relaxation times.
References
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 Sep;76(3):340-351. doi:10.1016/j.eururo.2019.02.033
Hegde JV, Mulkern RV, Panych LP, et al. Multiparametric MRI of prostate cancer: an update on state-of-the-art techniques and their performance in detecting and localizing prostate cancer. J Magn Reson Imaging. 2013 May;37(5):1035–54. doi:10.1002/jmri.23860
Carpagnano FA, Eusebi L, Tupputi U, et al. Multiparametric MRI: Local Staging of Prostate Cancer. Curr Radiol Rep. 2020 Dec;8(12):27. doi: 10.1007/s40134-020-00374-y
Barentsz JO, Richenberg J, Clements R, et al. ESUR prostate MR guidelines 2012. Eur Radiol. 2012 Apr;22(4):746–57. doi:10.1007/s00330-011-2377-y
Payne GS, Leach MO. Applications of magnetic resonance spectroscopy in radiotherapy treatment planning. Br J Radiol. 2006 Sep;79(special_issue_1):S16–26. doi:10.1259/bjr/84072695
Weinreb JC, Barentsz JO, Choyke PL, et al. PI-RADS Prostate Imaging – Reporting and Data System: 2015, Version 2. Eur Urol. 2016 Jan;69(1):16–40. doi:10.1016/j.eururo.2015.08.052
Mottet N, Bellmunt J, Bolla M, et al. EAU-ESTRO-SIOG Guidelines on Prostate Cancer. Part 1: Screening, Diagnosis, and Local Treatment with Curative Intent. Eur Urol. 2017 Apr;71(4):618–29. doi:10.1016/j.eururo.2016.08.003
Cornford P, Bellmunt J, Bolla M, et al. EAU-ESTRO-SIOG Guidelines on Prostate Cancer. Part II: Treatment of Relapsing, Metastatic, and Castration-Resistant Prostate Cancer. Eur Urol. 2017 Apr;71(4):630–42. doi:10.1016/j.eururo.2016.08.002
Garcia-Reyes K, Passoni NM, Palmeri ML, et al. Detection of prostate cancer with multiparametric MRI (mpMRI): effect of dedicated reader education on accuracy and confidence of index and anterior cancer diagnosis. Abdom Imaging. 2015 Jan;40(1):134–42. doi:10.1007/s00261-014-0197-7
Greer MD, Brown AM, Shih JH, et al. Accuracy and agreement of PIRADSv2 for prostate cancer mpMRI: A multireader study. J Magn Reson Imaging. 2017 Feb;45(2):579–85. doi:10.1002/jmri.25372
Ventrella E, Eusebi L, Carpagnano FA, Bartelli F, Cormio L, Guglielmi G. Multiparametric MRI of Prostate Cancer: Recent Advances. Curr Radiol Rep. 2020 Oct;8(10):19. https://doi.org/10.1007/s40134-020-00363-1
Margaret Cheng H-L, Stikov N, Ghugre NR, Wright GA. Practical medical applications of quantitative MR relaxometry. J Magn Reson Imaging. 2012 Oct;36(4):805–24. doi:10.1002/jmri.23718
(ESR) ES of R. ESR statement on the stepwise development of imaging biomarkers. Insights Imaging. 2013 Apr;4(2):147–52. doi:10.1007/s13244-013-0220-5
(ESR) ES of R. Magnetic Resonance Fingerprinting - a promising new approach to obtain standardized imaging biomarkers from MRI. Insights Imaging. 2015 Apr;6(2):163–5. doi:10.1007/s13244-015-0403-3
Nöth U, Shrestha M, Schüre J-R, Deichmann R. Quantitative in vivo T2 mapping using fast spin echo techniques – A linear correction procedure. NeuroImage. 2017 Aug;157:476–85. doi:10.1016/j.neuroimage.2017.06.017
Nguyen K, Sarkar A, Jain AK. Prostate Cancer Grading: Use of Graph Cut and Spatial Arrangement of Nuclei. IEEE Trans Med Imaging. 2014 Dec;33(12):2254–70. doi:10.1109/TMI.2014.2336883
Carter HB, Albertsen PC, Barry MJ, et al. Early Detection of Prostate Cancer: AUA Guideline. J Urol. 2013 Aug;190(2):419–26. doi:10.1016/j.juro.2013.04.119
Kim PK, Hong YJ, Im DJ, et al. Myocardial T1 and T2 Mapping: Techniques and Clinical Applications. Korean J Radiol. 2017;18(1):113–31. doi:10.3348/kjr.2017.18.1.113
Knight MJ, McCann B, Tsivos D, Dillon S, Coulthard E, Kauppinen RA. Quantitative T2 mapping of white matter: applications for ageing and cognitive decline. Phys Med Biol. 2016 Aug;61(15):5587–605. doi:10.1088/0031-9155/61/15/5587
Mamisch TC, Trattnig S, Quirbach S, Marlovits S, White LM, Welsch GH. Quantitative T2 Mapping of Knee Cartilage: Differentiation of Healthy Control Cartilage and Cartilage Repair Tissue in the Knee with Unloading—Initial Results. Radiology. 2010 Mar;254(3):818–26. doi:10.1148/radiol.09090335
Lim K Bin. Epidemiology of clinical benign prostatic hyperplasia. Asian J Urol. 2017 Jul;4(3):148–51. doi:10.1016/j.ajur.2017.06.004
Guneyli S, Ward E, Thomas S, et al. Magnetic resonance imaging of benign prostatic hyperplasia. Diagn Interv Radiol. 2016 May;22(3):215–9. doi:10.5152/dir.2015.15361
Chesnais AL, Niaf E, Bratan F, et al. Differentiation of transitional zone prostate cancer from benign hyperplasia nodules: Evaluation of discriminant criteria at multiparametric MRI. Clin Radiol. 2013 Jun;68(6):e323–30. doi:10.1016/j.crad.2013.01.018
Panda A, Obmann VC, Lo W-C, et al. MR Fingerprinting and ADC Mapping for Characterization of Lesions in the Transition Zone of the Prostate Gland. Radiology. 2019 Sep;292(3):685–94. doi:10.1148/radiol.2019181705
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. Abdom Radiol. 2017 Jan;42(1):260–70. doi:10.1007/s00261-016-0848-y
Fennessy FM, Fedorov A, Gupta SN, Schmidt EJ, Tempany CM, Mulkern R V. Practical considerations in T1 mapping of prostate for dynamic contrast enhancement pharmacokinetic analyses. Magn Reson Imaging. 2012 Nov;30(9):1224–33. doi:10.1016/j.mri.2012.06.011
Yamauchi FI, Penzkofer T, Fedorov A, et al. Prostate cancer discrimination in the peripheral zone with a reduced field-of-view T(2)-mapping MRI sequence. Magn Reson Imaging. 2015 Jun;33(5):525–30. doi:10.1016/j.mri.2015.02.006
Lee CH. Quantitative T2-mapping using MRI for detection of prostate malignancy: a systematic review of the literature. Acta Radiol. 2019;60(9):1181-1189. doi:10.1177/0284185118820058
Bourne RM, Kurniawan N, Cowin G, et al. Biexponential diffusion decay in formalin-fixed prostate tissue: Preliminary findings. Magn Reson Med. 2012 Sep;68(3):954–9. oi:10.1002/mrm.23291
Hoang Dinh A, Souchon R, Melodelima C, et al. Characterization of prostate cancer using T2 mapping at 3T: A multi-scanner study. Diagn Interv Imaging. 2015 Apr;96(4):365–72. doi:10.1016/j.diii.2014.11.016
Prince MR, Lee HG, Lee C-H, et al. Safety of gadobutrol in over 23,000 patients: the GARDIAN study, a global multicentre, prospective, non-interventional study. Eur Radiol. 2017 Jan;27(1):286–95. doi:10.1007/s00330-016-4268-8
Gulani V, Calamante F, Shellock FG, Kanal E, Reeder SB. Gadolinium deposition in the brain: summary of evidence and recommendations. Lancet Neurol. 2017 Jul;16(7):564–70. doi:10.1016/S1474-4422(17)30158-8
Purysko AS, Rosenkrantz AB, Barentsz JO, Weinreb JC, Macura KJ. PI-RADS Version 2: A Pictorial Update. RadioGraphics. 2016 Sep;36(5):1354–72. doi:10.1148/rg.2016150234
Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL. Artificial intelligence in radiology. Nat Rev Cancer. 2018 Aug;18(8):500–10. doi:10.1038/s41568-018-0016-5
What the radiologist should know about artificial intelligence - an ESR white paper. Insights Imaging. 2019;10(1):44. Published 2019 Apr 4. doi:10.1186/s13244-019-0738-2ec;10(1):44.
Tanenbaum LN, Tsiouris AJ, Johnson AN, et al. Synthetic MRI for Clinical Neuroimaging: Results of the Magnetic Resonance Image Compilation (MAGiC) Prospective, Multicenter, Multireader Trial. AJNR Am J Neuroradiol. 2017 Jun;38(6):1103–10. doi:10.3174/ajnr.A5227
Cameron A, Khalvati F, Haider MA, Wong A. MAPS: A Quantitative Radiomics Approach for Prostate Cancer Detection. IEEE Trans Biomed Eng. 2016 Jun;63(6):1145–56. doi:10.1109/TBME.2015.2485779
Stoyanova R, Takhar M, Tschudi Y, et al. Prostate cancer radiomics and the promise of radiogenomics. Transl Cancer Res. 2016 Aug;5(4):432–47. doi:10.21037/tcr.2016.06.20
Sun Y, Reynolds HM, Parameswaran B, et al. Multiparametric MRI and radiomics in prostate cancer: a review. Australas Phys Eng Sci Med. 2019 Mar;42(1):3–25. doi:10.1007/s13246-019-00730-z
Goldenberg SL, Nir G, Salcudean SE. A new era: artificial intelligence and machine learning in prostate cancer. Nat Rev Urol. 2019;16(7):391-403. doi:10.1038/s41585-019-0193-3
Harmon SA, Tuncer S, Sanford T, Choyke PL, Turkbey B. Artificial intelligence at the intersection of pathology and radiology in prostate cancer. Diagn Interv Radiol. 2019 May;25(3):183–8. doi:10.5152/dir.2019.19125
Wong NC, Shayegan B. Patient centered care for prostate cancer—how can artificial intelligence and machine learning help make the right decision for the right patient? Ann Transl Med. 2019 Mar;7(S1):S1–S1. doi:10.21037/atm.2019.01.13
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