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A comparative study of monoexponential versus biexponential models of diffusion-weighted imaging in differentiating histologic grades of hepatitis B virus-related hepatocellular carcinoma

  • Hepatobiliary
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Abstract

Purpose

To compare the diagnostic value of apparent diffusion coefficient (ADC) and intravoxel incoherent motion metrics in discriminating histologic grades of hepatocellular carcinoma (HCC) in patients with hepatitis B virus (HBV) infection.

Methods

117 chronic HBV patients with 120 pathologically confirmed HCCs after surgical resection or liver transplantation were enrolled in this retrospective study. Diffusion-weighted imaging was performed using eleven b values (0–1500 s/mm2) and two b values (0, 800 s/mm2) successively on a 3.0 T system. ADC0, 800, ADCtotal, diffusion coefficient (D), pseudodiffusion coefficient (D*), and perfusion fraction (f) were calculated. The parameters of three histologically differentiated subtypes were investigated using Kruskal–Wallis test, Spearman rank correlation, and receiver-operating characteristic analysis. Interobserver agreement was assessed using the intraclass correlation coefficient.

Results

There was excellent agreement for ADCtotal/D/f, good agreement for ADC0,800, and moderate agreement for D*. ADCtotal, ADC0, 800,D, and f were significantly different for well, moderately, and poorly differentiated HCCs (P < 0.001), and they were all inversely correlated with histologic grades: r = − 0.633, − 0.394, − 0.435, and − 0.358, respectively (P < 0.001). ADCtotal demonstrated higher performance than ADC0,800 in diagnosing both well and poorly differentiated HCCs (P < 0.001 and P = 0.04, respectively). ADCtotal showed higher performance than D and f in diagnosing well differentiated HCCs (P < 0.001) and similar performance in diagnosing poorly differentiated HCCs (P = 0.06 and 0.13, respectively).

Conclusions

ADCtotal showed better diagnostic performance than ADC0,800, D, and f to discriminate histologic grades of HCC.

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References

  1. Torre LA, Bray F, Siegel RL, et al. (2015) Global Cancer Statistics, 2012. CA CANCER J CLIN 65:87-108.

    PubMed  Google Scholar 

  2. Yang JD, Roberts LR (2010) Epidemiology and Management of Hepatocellular Carcinoma. Infect Dis Clin North Am 24:899-919.

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Maluccio M, Covey A (2012) Recent progress in understanding, diagnosing, and treating hepatocellular carcinoma. CA CANCER J CLIN 62:394-399.

    PubMed  Google Scholar 

  4. Oishi K, Itamoto T, Amano H, et al. (2007) Clinicopathologic features of poorly differentiated hepatocellular carcinoma. J Surg Oncol 95:311-316.

    PubMed  Google Scholar 

  5. Villanueva A, Hoshida Y, Battiston C, et al. (2011) Combining Clinical, Pathology, and Gene Expression Data to Predict Recurrence of Hepatocellular Carcinoma. GASTROENTEROLOGY 140:1501-1512.

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Okusaka T, Okada S, Ueno H, et al. (2002) Satellite lesions in patients with small hepatocellular carcinoma with reference to clinicopathologic features. CANCER-AM CANCER SOC 95:1931-1937.

    Google Scholar 

  7. Robert M, Sofair AN, Thomas A, et al. (2009) A Comparison of Hepatopathologists’ and Community Pathologists’ Review of Liver Biopsy Specimens From Patients With Hepatitis C. CLIN GASTROENTEROL H 7:335-338.

    Google Scholar 

  8. Taouli B, Koh D (2010) Diffusion-weighted MR Imaging of the Liver. RADIOLOGY 254:47-66.

    PubMed  Google Scholar 

  9. Koh D, Collins DJ (2007) Diffusion-Weighted MRI in the Body: Applications and Challenges in Oncology. AM J Roentgenol 188:1622-1635.

    Google Scholar 

  10. Lewis S, Dyvorne H, Cui Y, et al. (2014) Diffusion-Weighted Imaging of the Liver-Techniques and Applications. MAGN RESON IMAGING C 22:373-395.

    Google Scholar 

  11. Li X, Zhang K, Shi Y, et al. (2016) Correlations between the minimum and mean apparent diffusion coefficient values of hepatocellular carcinoma and tumor grade. J Magn Reson Imaging. 44:1442-1447.

    PubMed  Google Scholar 

  12. Muhi A, Ichikawa T, Motosugi U, et al. (2009) High-b-value diffusion-weighted MR imaging of hepatocellular lesions: Estimation of grade of malignancy of hepatocellular carcinoma. J Magn Reson Imaging 30:1005-1011.

    PubMed  Google Scholar 

  13. An C, Park M, Jeon H, et al. (2012) Prediction of the histopathological grade of hepatocellular carcinoma using qualitative diffusion-weighted, dynamic, and hepatobiliary phase MRI. EUR RADIOL 22:1701-1708.

    PubMed  Google Scholar 

  14. Jiang T, Xu JH, Zou Y, et al. (2017) Diffusion-weighted imaging (DWI) of hepatocellular carcinomas: a retrospective analysis of the correlation between qualitative and quantitative DWI and tumour grade. CLIN RADIOL 72:465-472.

    CAS  PubMed  Google Scholar 

  15. Tang Y, Wang H, Ma L, et al. (2016) Diffusion-weighted imaging of hepatocellular carcinomas: a retrospective analysis of correlation between apparent diffusion coefficients and histological grade. ABDOM RADIOL 41:1539-1545.

    Google Scholar 

  16. Nasu K, Kuroki Y, Tsukamoto T, et al. (2009) Diffusion-Weighted Imaging of Surgically Resected Hepatocellular Carcinoma: Imaging Characteristics and Relationship Among Signal Intensity, Apparent Diffusion Coefficient, and Histopathologic Grade. AM J Roentgenol 193:438-444.

    Google Scholar 

  17. Chang W, Chen R, Chou C, et al. (2014) Histological grade of hepatocellular carcinoma correlates with arterial enhancement on gadoxetic acid-enhanced and diffusion-weighted MR images. ABDOM IMAGING 39:1202-1212.

    PubMed  Google Scholar 

  18. Saito K, Moriyasu F, Sugimoto K, et al. (2012) Histological grade of differentiation of hepatocellular carcinoma: comparison of the efficacy of diffusion-weighted MRI with T2-weighted imaging and angiography-assisted CT. J Med Imaging Radiat Oncol 56:261-269.

    CAS  PubMed  Google Scholar 

  19. Koh D, Collins DJ, Orton MR (2011) Intravoxel Incoherent Motion in Body Diffusion-Weighted MRI: Reality and Challenges. AM J ROENTGENOL 196:1351-1361.

    Google Scholar 

  20. Le Bihan D, Breton E, Lallemand D, et al. (1988) Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging. RADIOLOGY 168:497-505.

    PubMed  Google Scholar 

  21. Woo S, Lee JM, Yoon JH, et al. (2014) Intravoxel incoherent motion diffusion-weighted MR imaging of hepatocellular carcinoma: correlation with enhancement degree and histologic grade. RADIOLOGY 270:758-767.

    PubMed  Google Scholar 

  22. Granata V, Fusco R, Catalano O, et al. (2016) Intravoxel incoherent motion (IVIM) in diffusion-weighted imaging (DWI) for Hepatocellular carcinoma: correlation with histologic grade. Oncotarget 7:79357–79364.

    PubMed  PubMed Central  Google Scholar 

  23. Shan Q, Chen J, Zhang T, et al. (2017) Evaluating histologic differentiation of hepatitis B virus-related hepatocellular carcinoma using intravoxel incoherent motion and AFP levels alone and in combination. ABDOM RADIOL 42:2079-2088.

    Google Scholar 

  24. Zhang Y, Kuang S, Shan Q, et al. (2019) Can IVIM help predict HCC recurrence after hepatectomy? EUR RADIOL.https://doi.org/10.1007/s00330-019-06180-1. [Epub ahead of print].

    PubMed  Google Scholar 

  25. Wurnig MC, Donati OF, Ulbrich E, et al. (2015) Systematic analysis of the intravoxel incoherent motion threshold separating perfusion and diffusion effects: Proposal of a standardized algorithm. MAGN RESON MED 74:1414-1422.

    PubMed  Google Scholar 

  26. Kim SY, Lee SS, Byun JH, et al. (2010) Malignant Hepatic Tumors: Short- term Reproducibility of Apparent Diffusion Coeffi cients with Breath-hold and Respiratory-triggered Diffusion- weighted MR Imaging. RADIOLOGY 255:815-823.

    PubMed  Google Scholar 

  27. (1995) Terminology of nodular hepatocellular lesions. International Working Group. HEPATOLOGY 22:983-993.

  28. DeLong E, DeLong D, Clarke-Pearson D (1988) Comparing the Areas Under Two or More Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach. BIOMETRICS 44:837-845.

    CAS  PubMed  Google Scholar 

  29. de Martel C, Maucort-Boulch D, Plummer M, et al. (2015) World-wide relative contribution of hepatitis B and C viruses in hepatocellular carcinoma. HEPATOLOGY 62:1190-1200.

    PubMed  PubMed Central  Google Scholar 

  30. Kao W, Su C, Chau G, et al. (2011) A Comparison of Prognosis between Patients with Hepatitis B and C Virus-related Hepatocellular Carcinoma Undergoing Resection Surgery. WORLD J SURG 35:858-867.

    PubMed  Google Scholar 

  31. Zhou Y, Si X, Wu L, et al. (2011) Influence of viral hepatitis status on prognosis in patients undergoing hepatic resection for hepatocellular carcinoma: a meta-analysis of observational studies. WORLD J SURG ONCOL 9:108.

    PubMed  PubMed Central  Google Scholar 

  32. Lewin M, Fartoux L, Vignaud A, et al. (2011) The diffusion-weighted imaging perfusion fraction f is a potential marker of sorafenib treatment in advanced hepatocellular carcinoma: a pilot study. EUR RADIOL 21:281-290.

    PubMed  Google Scholar 

  33. Kakite S, Dyvorne H, Besa C, et al. (2015) Hepatocellular carcinoma: short-term reproducibility of apparent diffusion coefficient and intravoxel incoherent motion parameters at 3.0T. J MAGN RESON IMAGING 41:149-156.

    PubMed  Google Scholar 

  34. Watanabe H, Kanematsu M, Goshima S, et al. (2013) Characterizing focal hepatic lesions by free-breathing intravoxel incoherent motion MRI at 3.0 T. ACTA RADIOL 55:1166-1173.

    PubMed  Google Scholar 

  35. Doblas S, Wagner M, Leitao HS, et al. (2013) Determination of malignancy and characterization of hepatic tumor type with diffusion-weighted magnetic resonance imaging: comparison of apparent diffusion coefficient and intravoxel incoherent motion-derived measurements. INVEST RADIOL 48:722-728.

    PubMed  Google Scholar 

  36. Colagrande S, Regini F, Pasquinelli F, et al. (2013) Focal liver lesion classification and characterization in noncirrhotic liver: a prospective comparison of diffusion-weighted magnetic resonance-related parameters. J Comput Assist Tomogr 37:560-567.

    PubMed  Google Scholar 

  37. Zhu L, Cheng Q, Luo W, et al. (2015) A comparative study of apparent diffusion coefficient and intravoxel incoherent motion-derived parameters for the characterization of common solid hepatic tumors. ACTA RADIOL 56:1411-1418.

    PubMed  Google Scholar 

  38. Luo M, Zhang L, Jiang XH, et al. (2017) Intravoxel incoherent motion: application in differentiation of hepatocellular carcinoma and focal nodular hyperplasia. DIAGN INTERV RADIOL 23:263-271.

    PubMed  PubMed Central  Google Scholar 

  39. Yoon JH, Lee JM, Yu MH, et al. (2014) Evaluation of hepatic focal lesions using diffusion-weighted MR imaging: Comparison of apparent diffusion coefficient and intravoxel incoherent motion-derived parameters. J MAGN RESON IMAGING 39:276-285.

    PubMed  Google Scholar 

  40. Goshima S, Kanematsu M, Kondo H, et al. (2008) Diffusion-weighted imaging of the liver: Optimizing b value for the detection and characterization of benign and malignant hepatic lesions. J MAGN RESON IMAGING 28:691-697.

    PubMed  Google Scholar 

  41. Kim SY, Lee SS, Park B, et al. (2012) Reproducibility of Measurement of Apparent Diffusion Coefficients of Malignant Hepatic Tumors: Effect of DWI Techniques and Calculation Methods. J MAGN RESON IMAGING 36:1131-1138.

    PubMed  Google Scholar 

  42. Kaya B, Koc Z (2014) Diffusion-weighted MRI and optimal b-value for characterization of liver lesions. ACTA RADIOL 55:532-542.

    PubMed  Google Scholar 

  43. Tang L, Zhou XJ (2019) Diffusion MRI of cancer: From low to high b-values. J MAGN RESON IMAGING 49:23-40.

    PubMed  Google Scholar 

  44. Wei Y, Gao F, Wang M, et al. (2019) Intravoxel incoherent motion diffusion-weighted imaging for assessment of histologic grade of hepatocellular carcinoma: comparison of three methods for positioning region of interest. EUR RADIOL 29:535-544.

    PubMed  Google Scholar 

  45. Lemke A, Laun FB, Klauss M, et al. (2009) Differentiation of pancreas carcinoma from healthy pancreatic tissue using multiple b-values: comparison of apparent diffusion coefficient and intravoxel incoherent motion derived parameters. INVEST RADIOL 44:769-775.

    PubMed  Google Scholar 

  46. ter Voert EEGW, Delso G, Porto M, et al. (2016) Intravoxel Incoherent Motion Protocol Evaluation and Data Quality in Normal and Malignant Liver Tissue and Comparison to the Literature. INVEST RADIOL 51:90-99.

    PubMed  Google Scholar 

  47. Ni P, Lin Y, Zhong Q, et al. (2016) Technical advancements and protocol optimization of diffusion-weighted imaging (DWI) in liver. ABDOM RADIOL 41:189-202.

    Google Scholar 

  48. Wagner M, Doblas S, Daire J, et al. (2012) Diffusion-weighted MR Imaging for the Regional Characterization of Liver Tumors. RADIOLOGY 264:464-472.

    PubMed  Google Scholar 

  49. Xu Y, Liu H, Xi D, et al. (2019) Whole-lesion histogram analysis metrics of the apparent diffusion coefficient: a correlation study with histological grade of hepatocellular carcinoma. ABDOM RADIOL 44:3089-3098.

    Google Scholar 

  50. Han X, Suo S, Sun Y, et al. (2017) Apparent diffusion coefficient measurement in glioma: Influence of region-of-interest determination methods on apparent diffusion coefficient values, interobserver variability, time efficiency, and diagnostic ability. J MAGN RESON IMAGING 45:722-730.

    PubMed  Google Scholar 

  51. Lee Y, Lee SS, Kim N, et al. (2015) Intravoxel incoherent motion diffusion-weighted MR imaging of the liver: effect of triggering methods on regional variability and measurement repeatability of quantitative parameters. RADIOLOGY 274:405-415.

    PubMed  Google Scholar 

  52. Iima M, Le Bihan D (2016) Clinical Intravoxel Incoherent Motion and Diffusion MR Imaging: Past, Present, and Future. RADIOLOGY 278:13-32.

    PubMed  Google Scholar 

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Funding

The scientific guarantor of this publication is Jin Wang. The authors of this manuscript declare no relationships with any companies, products or services of which may be related to the subject matter of the article. This study has received funding from Science and Technology Program of Guangzhou, China (No. 201704020016) and the National Natural Science Foundation of China (81271562).

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Correspondence to Jin Wang.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. For this type of study formal consent is not required.

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Shan, Q., Kuang, S., Zhang, Y. et al. A comparative study of monoexponential versus biexponential models of diffusion-weighted imaging in differentiating histologic grades of hepatitis B virus-related hepatocellular carcinoma. Abdom Radiol 45, 90–100 (2020). https://doi.org/10.1007/s00261-019-02253-3

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