Abdominal Radiology

, Volume 44, Issue 1, pp 95–103 | Cite as

Skewness of apparent diffusion coefficient (ADC) histogram helps predict the invasive potential of intraductal papillary neoplasms of the bile ducts (IPNBs)

  • Kai-pu Jin
  • Sheng-xiang Rao
  • Ruo-fan Sheng
  • Meng-su ZengEmail author



This retrospective study was to explore the value of whole lesion apparent diffusion coefficient (ADC) histogram in distinguishing invasive and noninvasive intraductal papillary neoplasms of the bile ducts (IPNBs).

Method and materials

Fifty-two patients of IPNB underwent MRI at 1.5T with diffusion-weighted imaging (DWI, b = 500 s/mm2) before surgical resections. ADC histogram metrics were generated by using the software MR OncoTreat. The mean, standard deviation, median, skewness, kurtosis as well as the 10th, 25th, 75th, and 90th percentiles were compared between pathologically defined invasive (n = 35) and noninvasive (n = 17) IPNBs. Such conventional imaging characters as lesion location, bile duct wall dilation, and mural nodularity were also assessed. Multivariate regression analysis as well as receiver operating characteristics (ROC) analysis were then conducted to determine the predictive factors and to evaluate potential diagnostic performances.


The inter-operator reliability was good to excellent (ICC: 0.693–979). Mean median, kurtosis, and the 10th, 25th, 75th, 90th percentiles were all greater in noninvasive group than invasive ones (P: 0.00–002). Skewness was lower in noninvasive group than invasive ones (− 1.0 ± 0.6 vs. − 0.3 ± 0.6, P = 0.00). After multivariate regression, skewness (AUC = 0.822, 95%CI 0.70–0.91) and mural nodularity (accuracy = 0.808) were the only two independent factors in predicting invasive IPNBs. The diagnostic performance improved (AUC = 0.867, 95%CI 0.742–0.946) when combining skewness and mural nodularity, however, the difference did not reach statistical significance (P = 0.16).


The ADC histogram has capability of distinguishing invasive and noninvasive IPNBs, in which skewness was an independent predictive factor.


Bile duct neoplasms Cholangiocarcinoma Neoplasm invasiveness Diffusion magnetic resonance imaging Comparative study 


Compliance with ethical standards

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.


The authors state that this work has not received any funding.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional Review Board and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors.

Informed consent

Written informed consent was waived by the Institutional.


  1. 1.
    Nakanuma Y, Miyata T, Uchida T (2016) Latest advances in the pathological understanding of cholangiocarcinomas. Expert Rev Gastroenterol Hepatol 10(1):113–127CrossRefGoogle Scholar
  2. 2.
    Nakanuma Y, Kakuda Y (2015) Pathologic classification of cholangiocarcinoma: new concepts. Best Pract Res Clin Gastroenterol 29(2):277–293CrossRefGoogle Scholar
  3. 3.
    Nakanuma Y, Tsutsui A, Ren XS, et al. (2014) What are the precursor and early lesions of peripheral intrahepatic cholangiocarcinoma? Int J Hepatol 2014:805973CrossRefGoogle Scholar
  4. 4.
    Mondal D, Silva MA, Soonawalla Z, Wang LM, Bungay HK (2016) Intraductal papillary neoplasm of the bile duct (IPN-B): also a disease of western Caucasian patients. A literature review and case series. Clin Radiol 71(1):e79–e87CrossRefGoogle Scholar
  5. 5.
    Luvira V, Pugkhem A, Bhudhisawasdi V, et al. (2016) Long-term outcome of surgical resection for intraductal papillary neoplasm of the bile duct. J Gastroenterol Hepatol. 32(2):527–533CrossRefGoogle Scholar
  6. 6.
    Nakanuma Y, Miyata T, Uchida T, Uesaka K (2016) Intraductal papillary neoplasm of bile duct is associated with a unique intraepithelial spreading neoplasm. Int J Clin Exp Pathol 9:11129–11138Google Scholar
  7. 7.
    Perez Saborido B, Bailon Cuadrado M, Rodriguez Lopez M, et al. (2017) Intraductal papillary neoplasia of the bile duct with malignancy: a differentiated entity of cholangiocarcinoma with a better prognosis. A review of three new cases. Revista espanola de enfermedades digestivas 109(8):592–595Google Scholar
  8. 8.
    Yoon HJ, Kim YK, Jang KT, et al. (2013) Intraductal papillary neoplasm of the bile ducts: description of MRI and added value of diffusion-weighted MRI. Abdom Imaging 38(5):1082–1090CrossRefGoogle Scholar
  9. 9.
    Zen Y, Fujii T, Itatsu K, et al. (2006) Biliary papillary tumors share pathological features with intraductal papillary mucinous neoplasm of the pancreas. Hepatology 44(5):1333–1343CrossRefGoogle Scholar
  10. 10.
    Rocha FG, Lee H, Katabi N, et al. (2012) Intraductal papillary neoplasm of the bile duct: a biliary equivalent to intraductal papillary mucinous neoplasm of the pancreas? Hepatology 56(4):1352–1360CrossRefGoogle Scholar
  11. 11.
    Nakanuma Y, Kakuda Y, Uesaka K, et al. (2016) Characterization of intraductal papillary neoplasm of bile duct with respect to histopathologic similarities to pancreatic intraductal papillary mucinous neoplasm. Hum Pathol 51:103–113CrossRefGoogle Scholar
  12. 12.
    Jang KM, Kim SH, Min JH, et al. (2014) Value of diffusion-weighted MRI for differentiating malignant from benign intraductal papillary mucinous neoplasms of the pancreas. AJR Am J Roentgenol 203(5):992–1000CrossRefGoogle Scholar
  13. 13.
    Kang KM, Lee JM, Shin CI, et al. (2013) Added value of diffusion-weighted imaging to MR cholangiopancreatography with unenhanced mr imaging for predicting malignancy or invasiveness of intraductal papillary mucinous neoplasm of the pancreas. J Magn Reson Imaging 38(3):555–563CrossRefGoogle Scholar
  14. 14.
    Curvo-Semedo L, Lambregts DM, Maas M, et al. (2012) Diffusion-weighted MRI in rectal cancer: apparent diffusion coefficient as a potential noninvasive marker of tumor aggressiveness. J Magn Reson Imaging 35(6):1365–1371CrossRefGoogle Scholar
  15. 15.
    Just N (2014) Improving tumour heterogeneity MRI assessment with histograms. Br J Cancer 111(12):2205–2213CrossRefGoogle Scholar
  16. 16.
    Shukla-Dave A, Lee NY, Jansen JF, et al. (2012) Dynamic contrast-enhanced magnetic resonance imaging as a predictor of outcome in head-and-neck squamous cell carcinoma patients with nodal metastases. Int J Radiat Oncol Biol Phys 82(5):1837–1844CrossRefGoogle Scholar
  17. 17.
    Tozer DJ, Jäger HR, Danchaivijitr N, et al. (2007) Apparent diffusion coefficient histograms may predict low-grade glioma subtype. NMR Biomed 20(1):49–57CrossRefGoogle Scholar
  18. 18.
    Hoffman DH, Ream JM, Hajdu CH, et al. (2017) Utility of whole-lesion ADC histogram metrics for assessing the malignant potential of pancreatic intraductal papillary mucinous neoplasms (IPMNs). Abdominal radiology. 42(4):1222–1228CrossRefGoogle Scholar
  19. 19.
    Rosenkrantz AB (2013) Histogram-based apparent diffusion coefficient analysis: an emerging tool for cervical cancer characterization? AJR Am J Roentgenol 200(2):311–313CrossRefGoogle Scholar
  20. 20.
    Downey K, Riches SF, Morgan VA (2013) Relationship between imaging biomarkers of stage I cervical cancer and poor-prognosis histologic features: quantitative histogram analysis of diffusion-weighted MR images. AJR Am J Roentgenol 200:314–320CrossRefGoogle Scholar
  21. 21.
    Zolal A, Juratli TA, Linn J, et al. (2016) Enhancing tumor apparent diffusion coefficient histogram skewness stratifies the postoperative survival in recurrent glioblastoma multiforme patients undergoing salvage surgery. J Neuro-oncol 127(3):551–557CrossRefGoogle Scholar
  22. 22.
    Cicchetti Domenic V (1994) Guidelines, criteria, and rules of thumb for evaluating normed and standardized assessment instruments in psychology. Psychol Assess 6(4):284–290CrossRefGoogle Scholar
  23. 23.
    Kyriazi S, Collins DJ, Messiou C, et al. (2011) Metastatic ovarian and primary peritoneal cancer: assessing chemotherapy response with diffusion-weighted MR imaging—value of histogram analysis of apparent diffusion coefficients. Radiology 261(1):182–192CrossRefGoogle Scholar
  24. 24.
    Kamiya A, Murayama S, Kamiya H, et al. (2014) Kurtosis and skewness assessments of solid lung nodule density histograms: differentiating malignant from benign nodules on CT. Jpn J Radiol 32(1):14–21CrossRefGoogle Scholar
  25. 25.
    Fukumura Y, Nakanuma Y, Kakuda Y, et al. (2017) Clinicopathological features of intraductal papillary neoplasms of the bile duct: a comparison with intraductal papillary mucinous neoplasm of the pancreas with reference to subtypes. Virchows Archiv 471(1):65–76CrossRefGoogle Scholar
  26. 26.
    Jin KP, Sheng RF, Rao SH, et al. (2017) Value of MRI in assessing the invasiveness of intraductal papillary neoplasms of the bile duct. Chin J Radiol 51(8):592–596Google Scholar
  27. 27.
    Liu Y, Zhong X, Yan L, et al. (2015) Diagnostic performance of CT and MRI in distinguishing intraductal papillary neoplasm of the bile duct from cholangiocarcinoma with intraductal papillary growth. Eur Radiol 25(7):1967–1974CrossRefGoogle Scholar
  28. 28.
    Ogawa H, Itoh S, Nagasaka T, et al. (2012) CT findings of intraductal papillary neoplasm of the bile duct: assessment with multiphase contrast-enhanced examination using multi-detector CT. Clin Radiol 67(3):224–231CrossRefGoogle Scholar
  29. 29.
    Kim JE, Lee JM, Kim SH, et al. (2010) Differentiation of intraductal growing-type cholangiocarcinomas from nodular-type cholangiocarcinomas at biliary MR imaging with MR cholangiography. Radiology 257(2):364–372CrossRefGoogle Scholar
  30. 30.
    Kim SH, Lee JM, Lee ES, et al. (2015) Intraductal papillary mucinous neoplasms of the pancreas: evaluation of malignant potential and surgical resectability by using MR imaging with MR cholangiography. Radiology 274(3):723–733CrossRefGoogle Scholar
  31. 31.
    Kang HJ, Lee JM, Joo I, Hur BY, et al. (2016) Assessment of malignant potential in intraductal papillary mucinous neoplasms of the pancreas: comparison between multidetector CT and MR imaging with MR cholangiopancreatography. Radiology 279(1):128–139CrossRefGoogle Scholar
  32. 32.
    Soares KC, Arnaoutakis DJ, Kamel I, et al. (2014) Cystic neoplasms of the liver: biliary cystadenoma and cystadenocarcinoma. J Am Coll Surg 218(1):119–128CrossRefGoogle Scholar
  33. 33.
    Wood CG 3rd, Stromberg LJ 3rd, Harmath CB, et al. (2015) CT and MR imaging for evaluation of cystic renal lesions and diseases. Radiographics 35(1):125–141CrossRefGoogle Scholar
  34. 34.
    Park SB, Lee JB (2014) MRI features of ovarian cystic lesions. J Magn Reson Imaging 40(3):503–515CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Kai-pu Jin
    • 1
    • 2
    • 3
  • Sheng-xiang Rao
    • 1
    • 2
    • 3
  • Ruo-fan Sheng
    • 1
    • 2
    • 3
  • Meng-su Zeng
    • 1
    • 2
    • 3
    Email author
  1. 1.Department of Radiology, Zhongshan HospitalFudan UniversityShanghaiChina
  2. 2.Shanghai Institute of Medical ImagingShanghaiChina
  3. 3.Department of Medical Imaging, Shanghai Medical CollegeFudan UniversityShanghaiChina

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