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Computed diffusion-weighted magnetic resonance imaging with high b-values in the diagnosis of gallbladder lesions

  • Hepatobiliary
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Abdominal Radiology Aims and scope Submit manuscript

Abstract

Purpose

The diagnosis of gallbladder lesions remains challenging. The efficacy of computed diffusion-weighted imaging (DWI) with high b-values and apparent diffusion coefficient (ADC) for the diagnosis of gallbladder cancer remains unknown. We aimed to investigate the usefulness of computed DWI with high b-values and the combination of computed DWI and ADC in differentiating malignant and benign gallbladder lesions.

Methods

Sixty patients (comprising 30 malignant and 30 benign lesions) who underwent magnetic resonance imaging for gallbladder lesions were included in this retrospective study. Qualitative evaluations were performed using conventional DWI with b1000, computed DWI with b1500, b1000 DWI/ADC, and computed b1500 DWI/ADC, and their diagnostic performances were compared.

Results

The sensitivity, specificity, and accuracy of computed b1500 DWI/ADC were 90% (27/30), 80% (24/30), and 85% (51/60), respectively. The accuracy of computed b1500 DWI/ADC was higher than that of conventional b1000 DWI (52%, 31/60, p < 0.001), computed b1500 DWI (72%, 43/60, p = 0.008), and b1000 DWI/ADC (78%, 47/60, p = 0.125). The specificity of computed b1500 DWI/ADC was also higher than that of conventional b1000 DWI (7%, 2/30, p < 0.001), computed b1500 DWI (47%, 14/30, p = 0.002), and b1000 DWI/ADC (67%, 20/30, p = 0.125). No significant difference was observed in the sensitivity between the groups.

Conclusion

This study shows that computed DWI with high b-values combined with ADC can improve diagnostic performance when differentiating malignant and benign gallbladder lesions.

Graphical abstract

Computed diffusion-weighted magnetic resonance imaging with high b-values in the diagnosis of gallbladder lesions.

*Computed DWI with b1500 combined with ADC can improve diagnostic performance when differentiating gallbladder lesions compared with conventional methods (b1000 DWI).

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References

  1. [1] Ogawa T, Horaguchi J, Fujita N, Noda Y, Kobayashi G, Ito K, Koshita S, Kanno Y, Masu K, Sugita R (2012) High b-value diffusion-weighted magnetic resonance imaging for gallbladder lesions: differentiation between benignity and malignancy. J Gastroenterol 47:1352–1360. https://doi.org/10.1007/s00535-012-0604-1.

    Article  PubMed  Google Scholar 

  2. [2] Kalra N, Gupta P, Singhal M, Gupta R, Gupta V, Srinivasan R, Mittal BR, Dhiman RK, Khandelwal N (2019) Cross-sectional imaging of gallbladder carcinoma: an update. J Clin Exp Hepatol 9:334–344. https://doi.org/10.1016/j.jceh.2018.04.005.

    Article  PubMed  Google Scholar 

  3. [3] Yu MH, Kim YJ, Park HS, Jung SI (2020) Benign gallbladder diseases: imaging techniques and tips for differentiating with malignant gallbladder diseases. World J Gastroenterol 26:2967–2986. https://doi.org/10.3748/wjg.v26.i22.2967.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. [4] Furlan A, Ferris JV, Hosseinzadeh K, Borhani AA (2008) Gallbladder carcinoma update: multimodality imaging evaluation, staging, and treatment options. AJR Am J Roentgenol 191:1440–1447. https://doi.org/10.2214/AJR.07.3599.

    Article  PubMed  Google Scholar 

  5. [5] Gupta P, Marodia Y, Bansal A, Kalra N, Kumar-M P, Sharma V, Dutta U, Sandhu MS (2020) Imaging-based algorithmic approach to gallbladder wall thickening. World J Gastroenterol 26:6163–6181. https://doi.org/10.3748/wjg.v26.i40.6163.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. [6] Gupta P, Kumar M, Sharma V, Dutta U, Sandhu MS (2020) Evaluation of gallbladder wall thickening: a multimodality imaging approach. Expert Rev Gastroenterol Hepatol 14:463–473. https://doi.org/10.1080/17474124.2020.1760840.

    Article  CAS  PubMed  Google Scholar 

  7. [7] Kim SJ, Lee JM, Kim H, Yoon JH, Han JK, Choi BI (2013) Role of diffusion-weighted magnetic resonance imaging in the diagnosis of gallbladder cancer. J Magn Reson Imaging 38:127–137. https://doi.org/10.1002/jmri.23956.

    Article  PubMed  Google Scholar 

  8. [8] Lee NK, Kim S, Moon JI, Shin N, Kim DU, Seo HI, Kim HS, Han GJ, Kim JY, Lee JW (2016) Diffusion-weighted magnetic resonance imaging of gallbladder adenocarcinoma: analysis with emphasis on histologic grade. Clin Imaging 40:345–351. https://doi.org/10.1016/j.clinimag.2016.01.008.

    Article  PubMed  Google Scholar 

  9. [9] Kitazume Y, Taura S, Nakaminato S, Noguchi O, Masaki Y, Kasahara I, Kishino M, Tateishi U (2016) Diffusion-weighted magnetic resonance imaging to differentiate malignant from benign gallbladder disorders. Eur J Radiol 85:864–873. https://doi.org/10.1016/j.ejrad.2016.02.003.

    Article  PubMed  Google Scholar 

  10. [10] You MW, Yun SJ (2018) Diagnostic performance of diffusion-weighted imaging for differentiating benign and malignant gallbladder lesions: a systematic review and meta-analysis. J Magn Reson Imaging 48:1375–1388. https://doi.org/10.1002/jmri.26035.

    Article  PubMed  Google Scholar 

  11. [11] Kuipers H, Hoogwater FJ, Holtman GA, van der Hoorn A, de Boer MT, de Haas RJ (2021) Clinical value of diffusion-weighted MRI for differentiation between benign and malignant gallbladder disease: a systematic review and meta-analysis. Acta Radiol 62:987–996. https://doi.org/10.1177/0284185120950115.

    Article  PubMed  Google Scholar 

  12. [12] Sugita R, Yamazaki T, Furuta A, Itoh K, Fujita N, Takahashi S (2009) High b-value diffusion-weighted MRI for detecting gallbladder carcinoma: preliminary study and results. Eur Radiol 19:1794–1798. https://doi.org/10.1007/s00330-009-1322-9.

    Article  PubMed  Google Scholar 

  13. [13] Kartalis N, Lindholm TL, Aspelin P, Permert J, Albiin N (2009) Diffusion-weighted magnetic resonance imaging of pancreas tumours. Eur Radiol 19:1981–1990. https://doi.org/10.1007/s00330-009-1384-8.

    Article  PubMed  Google Scholar 

  14. [14] Kang TW, Kim SH, Park HJ, Lim S, Jang KM, Choi D, Lee SJ (2013) Differentiating xanthogranulomatous cholecystitis from wall-thickening type of gallbladder cancer: added value of diffusion-weighted MRI. Clin Radiol 68:992–1001. https://doi.org/10.1016/j.crad.2013.03.022.

    Article  CAS  PubMed  Google Scholar 

  15. [15] Maas MC, Fütterer JJ, Scheenen TW (2013) Quantitative evaluation of computed high B value diffusion-weighted magnetic resonance imaging of the prostate. Invest Radiol 48:779–786. https://doi.org/10.1097/RLI.0b013e31829705bb.

    Article  PubMed  Google Scholar 

  16. [16] Rosenkrantz AB, Chandarana H, Hindman N, Deng FM, Babb JS, Taneja SS, Geppert C (2013) Computed diffusion-weighted imaging of the prostate at 3 T: impact on image quality and tumour detection. Eur Radiol 23:3170–3177. https://doi.org/10.1007/s00330-013-2917-8.

    Article  PubMed  Google Scholar 

  17. [17] Cheng Q, Ye S, Fu C, Zhou J, He X, Miao H, Xu N, Wang M (2019) Quantitative evaluation of computed and voxelwise computed diffusion-weighted imaging in breast cancer. Br J Radiol 92:20180978. https://doi.org/10.1259/bjr.20180978.

    Article  PubMed  PubMed Central  Google Scholar 

  18. [18] Fukukura Y, Kumagae Y, Hakamada H, Shindo T, Takumi K, Kamimura K, Nakajo M, Umanodan A, Yoshiura T (2017) Computed diffusion-weighted MR imaging for visualization of pancreatic adenocarcinoma: comparison with acquired diffusion-weighted imaging. Eur J Radiol 95:39–45. https://doi.org/10.1016/j.ejrad.2017.07.022.

    Article  PubMed  Google Scholar 

  19. [19] van Breda Vriesman AC, Engelbrecht MR, Smithuis RHM, Puylaert JB (2007) Diffuse gallbladder wall thickening: differential diagnosis. AJR Am J Roentgenol 188:495-501. https://doi.org/10.2214/AJR.05.1712.

    Article  PubMed  Google Scholar 

  20. Bertero L, Massa F, Metovic J, Zanetti R, Castellano I, Ricardi U, Papotti M, Cassoni P (2018) Eighth Edition of the UICC Classification of Malignant Tumours: an overview of the changes in the pathological TNM classification criteria-What has changed and why? Virchows Arch 8th edn 472:519–531. https://doi.org/10.1007/s00428-017-2276-y.

  21. [21] Lee NK, Kim S, Kim TU, Kim DU, Seo HI, Jeon TY (2014) Diffusion-weighted MRI for differentiation of benign from malignant lesions in the gallbladder. Clin Radiol 69:e78–e85. https://doi.org/10.1016/j.crad.2013.09.017.

    Article  CAS  PubMed  Google Scholar 

  22. [22] Blackledge MD, Leach MO, Collins DJ, Koh DM (2011) Computed diffusion-weighted MR imaging may improve tumor detection. Radiology 261:573–581. https://doi.org/10.1148/radiol.11101919.

    Article  PubMed  Google Scholar 

  23. [23] Irie H, Kamochi N, Nojiri J, Egashira Y, Sasaguri K, Kudo S (2011) High b-value diffusion-weighted MRI in differentiation between benign and malignant polypoid gallbladder lesions. Acta Radiol 52:236–240. https://doi.org/10.1258/ar.2010.100234.

    Article  PubMed  Google Scholar 

  24. [24] Rosenkrantz AB, Parikh N, Kierans AS, Kong MX, Babb JS, Taneja SS, Ream JM (2016) Prostate cancer detection using computed very high b-value diffusion-weighted imaging: how high should we go? Acad Radiol 23:704–711. https://doi.org/10.1016/j.acra.2016.02.003.

    Article  PubMed  Google Scholar 

  25. [25] Gupta P, Dutta U, Rana P, Singhal M, Gulati A, Kalra N, Soundararajan R, Kalage D, Chhabra M, Sharma V, Gupta V, Yadav TD, Kaman L, Irrinki S, Singh H, Sakaray Y, Das CK, Saikia U, Nada R, Srinivasan R, Sandhu MS, Sharma R, Shetty N, Eapen A, Kaur H, Kambadakone A, de Haas R, Kapoor VK, Barreto SG, Sharma AK, Patel A, Garg P, Pal SK, Goel M, Patkar S, Behari A, Agarwal AK, Sirohi B, Javle M, Garcea G, Nervi F, Adsay V, Roa JC, Han HS (2022) Gallbladder reporting and data system (GB-RADS) for risk stratification of gallbladder wall thickening on ultrasonography: an international expert consensus. Abdom Radiol (NY) 47:554-565. https://doi.org/10.1007/s00261-021-03360-w.

    Article  Google Scholar 

  26. [26] Kim SJ, Lee JM, Lee JY, Kim SH, Han JK, Choi BI, Choi JY (2008) Analysis of enhancement pattern of flat gallbladder wall thickening on MDCT to differentiate gallbladder cancer from cholecystitis. AJR Am J Roentgenol 191:765–771. https://doi.org/10.2214/AJR.07.3331.

    Article  PubMed  Google Scholar 

  27. Bo X, Chen E, Wang J, Nan L, Xin Y, Wang C, Lu Q, Rao S, Pang L, Li M, Lu P, Zhang D, Liu H, Wang Y (2019) Diagnostic accuracy of imaging modalities in differentiating xanthogranulomatous cholecystitis from gallbladder cancer. Ann Transl Med 7:627. https://doi.org/10.21037/atm.2019.11.35.

  28. [28] Annunziata S, Pizzuto DA, Caldarella C, Galiandro F, Sadeghi R, Treglia G (2015) Diagnostic accuracy of fluorine-18-fluorodeoxyglucose positron emission tomography in gallbladder cancer: a meta-analysis. World J Gastroenterol 21:11481–11488. https://doi.org/10.3748/wjg.v21.i40.11481.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. [29] Shi R-Y, Yao QY, Wu L-M, Xu JR (2018) Breast lesions: diagnosis using diffusion weighted imaging at 1.5T and 3.0T-systematic review and meta-analysis. Clin Breast Cancer 18:e305-e320. https://doi.org/10.1016/j.clbc.2017.06.011.

    Article  PubMed  Google Scholar 

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Acknowledgements

We thank Editage for their English editing services.

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Authors and Affiliations

  1. Department of Gastroenterology, Yokohama Sakae Kyosai Hospital, Yokohama, Kanagawa, Japan

    Motokazu Sato, Eiji Sakai, Seitaro Watanabe, Haruto Sanada, Tomoki Shimizu, Kenji Kanoshima, Yusuke Takai, Koki Meguro, Yuichi Sugimoto & Yusuke Biwata

  2. Department of Gastroenterology and Hepatology, Yokohama City University School of Medicine, 3-9, Fukuura, Kanazawa Ward, Yokohama, Kanagawa, 236-0004, Japan

    Motokazu Sato, Yusuke Kurita, Kunihiro Hosono, Kensuke Kubota & Atsushi Nakajima

  3. Department of Oncology, Yokohama City University School of Medicine, Yokohama, Kanagawa, Japan

    Noritoshi Kobayashi

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  1. Motokazu Sato
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by MS. The first draft of the manuscript was written by MS, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Yusuke Kurita.

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Conflict of interest

The authors have no relevant financial or non-financial interests to disclose. No funding was received for conducting this study.

Ethical approval

This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Clinical Trial Ethics Committee of the National Federation of Civil Service Associations Yokohama Sakae Kyosai Hospital (Ethics Approval Number: 20201214-2).

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Sato, M., Kurita, Y., Sakai, E. et al. Computed diffusion-weighted magnetic resonance imaging with high b-values in the diagnosis of gallbladder lesions. Abdom Radiol 47, 3278–3289 (2022). https://doi.org/10.1007/s00261-022-03586-2

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