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:: Volume 22, Issue 1 (1-2024) ::
Int J Radiat Res 2024, 22(1): 111-116 Back to browse issues page
Diagnostic value of tripartite magnetic resonance imaging model based on T2-weighted imaging, diffusion-weighted imaging and dynamic contrast-enhanced imaging for prostatitis, prostatic hyperplasia and prostate cancer
F. Yang , W. Guo , S. Sun , Y. Huang
Department of Radiology, Wuhan Third Hospital (Tongren Hospital of Wuhan University), Wuhan, Hubei 430000, China , amy1230717@163.com
Abstract:   (332 Views)
Background: To assess the value of tripartite magnetic resonance imaging model based on T2-weighted imaging (T2-WI), diffusion-weighted imaging (DWI) as well as dynamic contrast-enhanced imaging (DCE) for the diagnosis of prostatitis, prostatic hyperplasia and prostate cancer. Materials and Methods: A total of 100 patients with prostatic diseases were selected from our hospital from January 2020 to December 2022. All patients underwent T2-WI, DWI and DCE examination. Results: Among the 100 suspected patients, 40 were diagnosed with prostate cancer, 30 were prostatitis, and 30 were prostate hyperplasia. Apparent diffusion coefficient (ADC) value was reduced in prostate cancer patients compared to the prostatic hyperplasia as well as prostatitis groups (P<0.05). No difference was discovered in ADC value between the prostatic prostatic hyperplasia and prostatitis groups (P>0.05). Moreover, the diagnosis efficacy of the tripartite magnetic resonance imaging model was higher compared to those of prostate imaging reporting and data system version 2 (PI-RADS V2). Conclusion: The tripartite magnetic resonance imaging model based on T2-WI, DWI, as well as DCE has high diagnostic accuracy in prostatic diseases, with high sensitivity and low misdiagnosis rate, which might be valuable in clinical application.
Keywords: Prostatic Diseases, magnetic resonance imaging, T2-WI, diffusion-weighted imaging, dynamic contrast-enhanced imaging.
Full-Text [PDF 735 kb]   (125 Downloads)    
Type of Study: Original Research | Subject: Radiation Biology
References
1. 1. Verze P, Cai T, Lorenzetti S (2016) The role of the prostate in male fertility, health and disease. Nat Rev Urol, 13(7): 379-86. [DOI:10.1038/nrurol.2016.89]
2. Khan FU, Ihsan AU, Khan HU, et al. (2017) Comprehensive overview of prostatitis. Biomed Pharmacother, 94: 1064-76. [DOI:10.1016/j.biopha.2017.08.016]
3. Devlin CM, Simms MS, Maitland NJ (2021) Benign prostatic hyperplasia - what do we know? BJU Int, 127(4): 389-99. [DOI:10.1111/bju.15229]
4. Schatten H (2018) Brief overview of prostate cancer statistics, grading, diagnosis and treatment strategies. Adv Exp Med Biol, 1095: 1-14. [DOI:10.1007/978-3-319-95693-0_1]
5. Wang G, Zhao D, Spring DJ, et al. (2018) Genetics and biology of prostate cancer. Genes Dev, 32(17-18): 1105-40. [DOI:10.1101/gad.315739.118]
6. Langan RC (2019) Benign Prostatic Hyperplasia. Prim Care, 46(2): 223-32. [DOI:10.1016/j.pop.2019.02.003]
7. Kwan ACF and Beahm NP (2020) Fosfomycin for bacterial prostatitis: a review. Int J Antimicrob Agents, 56(4): 106106. [DOI:10.1016/j.ijantimicag.2020.106106]
8. Porter CM, Shrestha E, Peiffer LB, et al. (2018) The microbiome in prostate inflammation and prostate cancer. Prostate Cancer Prostatic Dis, 21(3): 345-54. [DOI:10.1038/s41391-018-0041-1]
9. Chang AJ, Autio KA, Roach M, 3rd, et al. (2014) High-risk prostate cancer-classification and therapy. Nat Rev Clin Oncol, 11(6): 308-23. [DOI:10.1038/nrclinonc.2014.68]
10. Nordström T, Akre O, Aly M, et al. (2018) Prostate-specific antigen (PSA) density in the diagnostic algorithm of prostate cancer. Prostate Cancer Prostatic Dis, 21(1): 57-63. [DOI:10.1038/s41391-017-0024-7]
11. Salembier C, Villeirs G, De Bari B, et al. (2018) ESTRO ACROP consensus guideline on CT- and MRI-based target volume delineation for primary radiation therapy of localized prostate cancer. Radiother Oncol, 127(1): 49-61. [DOI:10.1016/j.radonc.2018.01.014]
12. Vasavada SR, Dobbs RW, Kajdacsy-Balla AA, et al. (2018) Inflammation on Prostate Needle Biopsy is Associated with Lower Prostate Cancer Risk: A Meta-Analysis. J Urol, 199(5): 1174-81. [DOI:10.1016/j.juro.2017.11.120]
13. Giannarini G, Autorino R, di Lorenzo G (2009) Saturation biopsy of the prostate: why saturation does not saturate. Eur Urol, 56(4): 619-21. [DOI:10.1016/j.eururo.2009.03.044]
14. O'Shea A and Harisinghani M (2022) PI-RADS: multiparametric MRI in prostate cancer. Magma, 35(4): 523-32. [DOI:10.1007/s10334-022-01019-1]
15. Liu Y, Wang W, Qin XB, et al. (2019) The applied research of simultaneous image acquisition of T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) in the assessment of patients with prostate cancer. Asian J Androl, 21(2): 177-82. [DOI:10.4103/aja.aja_82_18]
16. Xia X, Wen L, Zhou F, et al. (2022) Predictive value of DCE-MRI and IVIM-DWI in osteosarcoma patients with neoadjuvant chemotherapy. Front Oncol, 12: 967450. [DOI:10.3389/fonc.2022.967450]
17. Weinreb JC, Barentsz JO, Choyke PL, et al. (2016) PI-RADS Prostate Imaging - Reporting and Data System: 2015, Version 2. European Urology, 69(1): 16-40. [DOI:10.1016/j.eururo.2015.08.052]
18. Karaca L, Özdemir ZM, Kahraman A, et al. (2022) Endometrial carcinoma detection with 3.0 Tesla imaging: which sequence is more useful. Eur Rev Med Pharmacol Sci, 26(21): 8098-104.
19. Ueda T, Ohno Y, Yamamoto K, et al. (2022) Deep learning reconstruction of diffusion-weighted MRI improves image quality for prostatic imaging. Radiology, 303(2): 373-81. [DOI:10.1148/radiol.204097]
20. He D, Wang X, Fu C, et al. (2021) MRI-based radiomics models to assess prostate cancer, extracapsular extension and positive surgical margins. Cancer Imaging, 21(1): 46. [DOI:10.1186/s40644-021-00414-6]
21. Kang Z, Xu A, Wang L (2021) Predictive role of T2WI and ADC-derived texture parameters in differentiating Gleason score 3 + 4 and 4 + 3 prostate cancer. J Xray Sci Technol, 29(2): 307-15. [DOI:10.3233/XST-200785]
22. Iyama Y, Nakaura T, Katahira K, et al. (2017) Development and validation of a logistic regression model to distinguish transition zone cancers from benign prostatic hyperplasia on multi-parametric prostate MRI. Eur Radiol, 27(9): 3600-8. [DOI:10.1007/s00330-017-4775-2]
23. Lebovici A, Csutak C, Popa P, et al. (2022) Magnetic resonance imaging characteristics of chronic prostatitis in patients under the age of 50: is it more than the eye can see? Acta Radiol, 63(6): 839-46. [DOI:10.1177/02841851211010397]
24. Woo S, Suh CH, Kim SY, et al. (2018) Head-to-head comparison between high- and standard-b-value DWI for detecting prostate cancer: A systematic review and meta-analysis. AJR Am J Roentgenol, 210(1): 91-100. [DOI:10.2214/AJR.17.18480]
25. Purysko AS, Rosenkrantz AB, Barentsz JO, et al. (2016) PI-RADS Version 2: A pictorial update. Radiographics, 36(5): 1354-72. [DOI:10.1148/rg.2016150234]
26. Turkbey B, Rosenkrantz AB, Haider MA, et al. (2019) Prostate imaging reporting and data system version 2.1: 2019 update of prostate imaging reporting and data system version 2. Eur Urol, 76(3): 340-51. [DOI:10.1016/j.eururo.2019.02.033]
27. Barrett T, Rajesh A, Rosenkrantz AB, et al. (2019) PI-RADS version 2.1: one small step for prostate MRI. Clin Radiol, 74(11): 841-52. [DOI:10.1016/j.crad.2019.05.019]
28. Mayerhoefer ME, Materka A, Langs G, et al. (2020) Introduction to radiomics. J Nucl Med, 61(4): 488-95. [DOI:10.2967/jnumed.118.222893]
29. Lambin P, Leijenaar RTH, Deist TM, et al. (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol, 14(12): 749-62. [DOI:10.1038/nrclinonc.2017.141]
30. Rogers W, Thulasi Seetha S, Refaee TAG, et al. (2020) Radiomics: from qualitative to quantitative imaging. Br J Radiol, 93(1108): 20190948. [DOI:10.1259/bjr.20190948]
31. Wu G, Jochems A, Refaee T, et al. (2021) Structural and functional radiomics for lung cancer. Eur J Nucl Med Mol Imaging, 48(12): 3961-74. [DOI:10.1007/s00259-021-05242-1]
32. Ferro M, de Cobelli O, Musi G, et al. (2022) Radiomics in prostate cancer: an up-to-date review. Ther Adv Urol, 14: 17562872221109020. [DOI:10.1177/17562872221109020]
33. Xu M, Fang M, Zou J, et al. (2019) Using biparametric MRI radiomics signature to differentiate between benign and malignant prostate lesions. Eur J Radiol, 114: 38-44. [DOI:10.1016/j.ejrad.2019.02.032]
34. Bonekamp D, Kohl S, Wiesenfarth M, et al. (2018) Radiomic machine learning for characterization of prostate lesions with MRI: Comparison to ADC values. Radiology, 289(1): 128-37. [DOI:10.1148/radiol.2018173064]
35. Sun H, Du F, Liu Y, et al. (2022) DCE-MRI and DWI can differentiate benign from malignant prostate tumors when serum PSA is ≥10 ng/ml. Frontiers in Oncology, 12: 925186. [DOI:10.3389/fonc.2022.925186]
36. Gaur S, Harmon S, Gupta RT, et al. (2019) A multireader exploratory evaluation of individual pulse sequence cancer detection on prostate multiparametric magnetic resonance imaging (MRI). Academic Radiology, 26(1): 5-14. [DOI:10.1016/j.acra.2018.03.024]
37. Jambor I, Kähkönen E, Taimen P, et al. (2015) Prebiopsy multiparametric 3T prostate MRI in patients with elevated PSA, normal digital rectal examination, and no previous biopsy. J Magn Reson Imaging, 41(5): 1394-404. [DOI:10.1002/jmri.24682]
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Yang F, Guo W, Sun S, Huang Y. Diagnostic value of tripartite magnetic resonance imaging model based on T2-weighted imaging, diffusion-weighted imaging and dynamic contrast-enhanced imaging for prostatitis, prostatic hyperplasia and prostate cancer. Int J Radiat Res 2024; 22 (1) :111-116
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Volume 22, Issue 1 (1-2024) Back to browse issues page
International Journal of Radiation Research
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