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Current status of whole-body magnetic resonance imaging in prostate cancer
ZHANG Peipei  MIN Xiangde  WANG Liang 

Cite this article as: Zhang PP, Min XD, Wang L. Current status of whole-body magnetic resonance imaging in prostate cancer[J]. Chin J Magn Reson Imaging, 2021, 12(7): 121-124. DOI:10.12015/issn.1674-8034.2021.07.029.


[Abstract] The incidence of prostate cancer in China is increasing year by year. Metastatic prostate cancer accounts for a large proportion of newly diagnosed prostate cancer patients. Determining the metastatic burden of prostate cancer is critical for the selection of treatment methods and predicting prostate cancer prognosis. Whole-body magnetic resonance imaging (WB-MRI) can accurately assess the metastatic burden of prostate cancer and the treatment efficacy. Compared with positron emission tomography and computed tomography (PET-CT), WB-MRI has the advantages of low price, non-invasive, non-radiation, and no need for contrast agent. This article mainly reviews the application status of WB-MRI in prostate cancer and provides references for the clinical application and further research of WB-MRI.
[Keywords] magnetic resonance imaging；whole-body magnetic resonance imaging；prostate cancer；fast imaging；deep learning

ZHANG Peipei   MIN Xiangde   WANG Liang*  

Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China

Wang L, E-mail: wang6@tjh.tjmu.edu.cn

Conflicts of interest   None.

Received  2021-01-12
Accepted  2021-02-02
DOI: 10.12015/issn.1674-8034.2021.07.029
Cite this article as: Zhang PP, Min XD, Wang L. Current status of whole-body magnetic resonance imaging in prostate cancer[J]. Chin J Magn Reson Imaging, 2021, 12(7): 121-124. DOI:10.12015/issn.1674-8034.2021.07.029.

1
Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2018, 68(6): 394-424. DOI: 10.3322/caac.21492.
2
Siegel RL, Miller KD, Jemal A. Cancer statistics, 2020[J]. CA Cancer J Clin, 2020, 70(1): 7-30. DOI: 10.3322/caac.21590.
3
Yang YJ, Dai B, Ye DW, et al. The predictive value of CTCs characterization for time to castration resistance of high-volume metastatic castration sensitive prostate cancer[J]. Chin J Urol, 2019, 40(9): 661-667. DOI: 10.3760/cma.j.issn.1000-6702.2019.09.005.
4
Sun BX, Zhang H, Zhang XX, et al. Advances in magnetic resonance imaging in the diagnosis of prostate cancer[J]. Chin J Magn Reson Imaging, 2019, 10(12): 947-950. DOI: 10.12015/issn.1674-8034.2019.12.017.
5
Stabile A, Giganti F, Rosenkrantz AB, et al. Multiparametric MRI for prostate cancer diagnosis: current status and future directions[J]. Nat Rev Urol, 2020, 17(1): 41-61. DOI: 10.1038/s41585-019-0212-4.
6
Futterer JJ, Briganti A, De Visschere P, et al. Can clinically significant prostate cancer be detected with multiparametric magnetic resonance imaging? A systematic review of the literature[J]. Eur Urol, 2015, 68(6): 1045-1053. DOI: 10.1016/j.eururo.2015.01.013.
7
Robertson NL, Sala E, Benz M, et al. Combined whole body and multiparametric prostate magnetic resonance imaging as a 1-step approach to the simultaneous assessment of local recurrence and metastatic disease after radical prostatectomy[J]. J Urol, 2017, 198(1): 65-70. DOI: 10.1016/j.juro.2017.02.071.
8
Albano D, Bruno A, Patti C, et al. Whole-body magnetic resonance imaging (WB-MRI) in lymphoma: state of the art[J]. Hematol Oncol, 2020, 38(1): 12-21. DOI: 10.1002/hon.2676.
9
Hynes JP, Hughes N, Cunningham P, et al. Whole-body MRI of bone marrow: a review[J]. J Magn Reson Imaging, 2019, 50(6): 1687-1701. DOI: 10.1002/jmri.26759.
10
Taylor SA, Mallett S, Miles A, et al. Whole-body MRI compared with standard pathways for staging metastatic disease in lung and colorectal cancer: the Streamline diagnostic accuracy studies[J]. Health Technol Assess, 2019, 23(66): 1-270. DOI: 10.3310/hta23660.
11
Petralia G, Padhani A, Summers P, et al. Whole-body diffusion-weighted imaging: is it all we need for detecting metastases in melanoma patients?[J]. Eur Radiol, 2013, 23(12): 3466-3476. DOI: 10.1007/s00330-013-2968-x.
12
Petralia G, Padhani AR, Pricolo P, et al. Whole-body magnetic resonance imaging (WB-MRI) in oncology: recommendations and key uses[J]. Radiol Med, 2019, 124(3): 218-233. DOI: 10.1007/s11547-018-0955-7.
13
Lecouvet FE. Whole-Body MR imaging: musculoskeletal applications[J]. Radiology, 2016, 279(2): 345-365. DOI: 10.1148/radiol.2016142084.
14
Padhani AR, Lecouvet FE, Tunariu N, et al. METastasis reporting and data system for prostate cancer: practical guidelines for acquisition, interpretation, and reporting of whole-body magnetic resonance imaging-based evaluations of multiorgan involvement in advanced prostate cancer[J]. Eur Urol, 2017, 71(1): 81-92. DOI: 10.1016/j.eururo.2016.05.033.
15
Gillessen S, Attard G, Beer TM, et al. Management of patients with advanced prostate cancer: the report of the advanced prostate cancer consensus conference APCCC 2017[J]. Eur Urol, 2018, 73(2): 178-211. DOI: 10.1016/j.eururo.2017.06.002.
16
Padhani AR, Lecouvet FE, Tunariu N, et al. Rationale for modernising imaging in advanced prostate cancer[J]. Eur Urol Focus, 2017, 3(2-3): 223-239. DOI: 10.1016/j.euf.2016.06.018.
17
Sun YY, Guo Z, Yang XL, et al. Meta-analysis of positron emission tomography-CT and diffusion weighted imaging in lymph node staging of prostate cancer patients[J]. Chin J Radiol, 2015, 49(4): 301-305. DOI: 10.3760/cma.j.issn.1005-1201.2015.04.015.
18
Lecouvet FE, Oprea-Lager DE, Liu Y, et al. Use of modern imaging methods to facilitate trials of metastasis-directed therapy for oligometastatic disease in prostate cancer: a consensus recommendation from the EORTC imaging group[J]. Lancet Oncol, 2018, 19(10): e534-e545. DOI: 10.1016/S1470-2045(18)30571-0.
19
Johnston EW, Latifoltojar A, Sidhu HS, et al. Multiparametric whole-body 3.0-T MRI in newly diagnosed intermediate- and high-risk prostate cancer: diagnostic accuracy and interobserver agreement for nodal and metastatic staging[J]. Eur Radiol, 2019, 29(6): 3159-3169. DOI: 10.1007/s00330-018-5813-4.
20
Metser U, Chan R, Veit-Haibach P, et al. Comparison of MRI sequences in whole-body PET/MRI for staging of patients with high-risk prostate cancer[J]. AJR Am J Roentgenol, 2019, 212(2): 377-381. DOI: 10.2214/AJR.18.20495.
21
Perez-Lopez R, Mateo J, Mossop H, et al. Diffusion-weighted imaging as a treatment response biomarker for evaluating bone metastases in prostate cancer: a pilot study[J]. Radiology, 2017, 283(1): 168-177. DOI: 10.1148/radiol.2016160646.
22
Razek AA, Tawfik A, Rahman MA, et al. Whole-body diffusion-weighted imaging with background body signal suppression in the detection of osseous and extra-osseous metastases[J]. Pol J Radiol, 2019, 84: e453-e458. DOI: 10.5114/pjr.2019.90057.
23
Adeleke S, Latifoltojar A, Sidhu H, et al. Localising occult prostate cancer metastasis with advanced imaging techniques (LOCATE trial): a prospective cohort, observational diagnostic accuracy trial investigating whole-body magnetic resonance imaging in radio-recurrent prostate cancer[J]. BMC Med Imaging, 2019, 19(1): 90. DOI: 10.1186/s12880-019-0380-y.
24
Sawicki LM, Kirchner J, Buddensieck C, et al. Prospective comparison of whole-body MRI and (68) Ga-PSMA PET/CT for the detection of biochemical recurrence of prostate cancer after radical prostatectomy[J]. Eur J Nucl Med Mol Imaging, 2019, 46(7): 1542-1550. DOI: 10.1007/s00259-019-04308-5.
25
Iwamura H, Kaiho Y, Ito J, et al. Evaluation of tumor viability for primary and bone metastases in metastatic castration-resistant prostate cancer using whole-body magnetic resonance imaging[J]. Case Rep Urol, 2018, 2018: 4074378. DOI: 10.1155/2018/4074378.
26
Jacobs MA, Macura KJ, Zaheer A, et al. Multiparametric whole-body MRI with diffusion-weighted imaging and ADC mapping for the identification of visceral and osseous metastases from solid tumors[J]. Acad Radiol, 2018, 25(11): 1405-1414. DOI: 10.1016/j.acra.2018.02.010.
27
Wu C, Huang J, Xu WB, et al. Discriminating depth of response to therapy in multiple myeloma using whole-body diffusion-weighted MRI with apparent diffusion coefficient: preliminary results from a single-center study[J]. Acad Radiol, 2018, 25(7): 904-914. DOI: 10.1016/j.acra.2017.12.008.
28
Zhang Y, Xiong X, Fu Z, et al. Whole-body diffusion-weighted MRI for evaluation of response in multiple myeloma patients following bortezomib-based therapy: a large single-center cohort study[J]. Eur J Radiol, 2019, 120: 108695. DOI: 10.1016/j.ejrad.2019.108695.
29
Lecouvet FE, Pasoglou V, Van Nieuwenhove S, et al. Shortening the acquisition time of whole-body MRI: 3D T1 gradient echo dixon vs fast spin echo for metastatic screening in prostate cancer[J]. Eur Radiol, 2020, 30(6): 3083-3093. DOI: 10.1007/s00330-019-06515-y.
30
Griswold MA, Jakob PM, Heidemann RM, et al. Generalized autocalibrating partially parallel acquisitions (GRAPPA)[J]. Magn Reson Med, 2002, 47(6): 1202-1210. DOI: 10.1002/mrm.10171.
31
Pruessmann KP, Weiger M, Scheidegger MB, et al. SENSE: sensitivity encoding for fast MRI[J]. Magn Reson Med, 1999, 42(5): 952-962.
32
Lustig M, Donoho D, Pauly JM. Sparse MRI: the application of compressed sensing for rapid MR imaging[J]. Magn Reson Med, 2007, 58(6): 1182-1195. DOI: 10.1002/mrm.21391.
33
Chen C, Liu Y, Schniter P, et al. Sparsity adaptive reconstruction for highly accelerated cardiac MRI[J]. Magn Reson Med, 2019, 81(6): 3875-3887. DOI: 10.1002/mrm.27671.
34
Wang S, Su Z, Ying L, et al. Accelerating magnetic resonance imaging via deep learning[J]. Proc IEEE Int Symp Biomed Imaging, 2016, 2016: 514-517. DOI: 10.1109/ISBI.2016.7493320.
35
Yang G, Yu S, Dong H, et al. DAGAN: deep de-aliasing generative adversarial networks for fast compressed sensing MRI reconstruction[J]. IEEE Trans Med Imaging, 2018, 37(6): 1310-1321. DOI: 10.1109/TMI.2017.2785879.
36
Schlemper J, Caballero J, Hajnal JV, et al. A deep cascade of convolutional neural networks for dynamic MR image reconstruction[J]. IEEE Trans Med Imaging, 2018, 37(2): 491-503. DOI: 10.1109/TMI.2017.2760978.
37
Hong Y, Chen G, Yap PT, et al. Multifold Acceleration of Diffusion MRI via Deep Learning Reconstruction from Slice-Undersampled Data[C]. Information Processing in Medical Imaging. IPMI 2019. Lecture Notes in Computer Science. Hong Kong: Springer, 2019, 11492: 530-541. DOI: 10.1007/978-3-030-20351-1_41.DOI:10.1007/978-3-030-20351-1_41.
38
Zhu B, Liu JZ, Cauley SF, et al. Image reconstruction by domain-transform manifold learning[J]. Nature, 2018, 555(7697): 487-492. DOI: 10.1038/nature25988.
39
Dong C, Loy CC, He K, et al. Image super-resolution using deep convolutional networks[J]. IEEE Trans Pattern Anal Mach Intell, 2016, 38(2): 295-307. DOI: 10.1109/TPAMI.2015.2439281.
40
Liu PF, Zhao HC, Liu MD. Image super-resolution based on convolutional neural network[J]. Comput Eng Appl, 2019, 55(09): 197-202. DOI: 10.3778/j.issn.1002-8331.1801-0072.

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