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Classification of hepatocellular carcinoma and intrahepatic cholangiocarcinoma based on multi-phase CT scans

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Abstract

Liver and bile duct cancers are leading causes of worldwide cancer death. The most common ones are hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC). Influencing factors and prognosis of HCC and ICC are different. Precise classification of these two liver cancers is essential for treatment and prevention plans. The aim of this study is to develop a machine-based method that differentiates between the two types of liver cancers from multi-phase abdominal computerized tomography (CT) scans. The proposed method consists of two major steps. In the first step, the liver is segmented from the original images using a convolutional neural network model, together with task-specific pre-processing and post-processing techniques. In the second step, by looking at the intensity histograms of the segmented images, we extract features from regions that are discriminating between HCC and ICC, and use them as an input for classification using support vector machine model. By testing on a dataset of labeled multi-phase CT scans provided by Maharaj Nakorn Chiang Mai Hospital, Thailand, we have obtained 88% in classification accuracy. Our proposed method has a great potential in helping radiologists diagnosing liver cancer.

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References

  1. Adcock CS, Florez E, Zand KA, Patel A, Howard CM, Fatemi A (2018) Assessment of treatment response following yttrium-90 transarterial radioembolization of liver malignancies. Cureus https://doi.org/10.7759/cureus.2895

  2. Alsultan A A, Smits M L J, Barentsz M W, Braat A J A T, Lam M G E H (2019) The value of yttrium-90 PET/CT after hepatic radioembolization: a pictorial essay. Clinical and Translational Imaging 7(4):303–312. https://doi.org/10.1007/s40336-019-00335-2

    Article  Google Scholar 

  3. Altenbernd J, Wetter A, Forsting M, Umutlu L (2016) Treatment response after radioembolisation in patients with hepatocellular carcinoma—an evaluation with dual energy computed-tomography. European Journal of Radiology Open 3:230–235. https://doi.org/10.1016/j.ejro.2016.08.002

    Article  PubMed  PubMed Central  Google Scholar 

  4. Azer SA (2019) Deep learning with convolutional neural networks for identification of liver masses and hepatocellular carcinoma: a systematic review. World Journal of Gastrointestinal Oncology 11 (12):1218–1230. https://doi.org/10.4251/wjgo.v11.i12.1218. https://pubmed.ncbi.nlm.nih.gov/31908726

    Article  PubMed  PubMed Central  Google Scholar 

  5. Berre CL, Sandborn WJ, Aridhi S, Devignes MD, Fournier L, Smaïl-Tabbone M, Danese S, Peyrin-Biroulet L (2020) Application of artificial intelligence to gastroenterology and hepatology. Gastroenterology 158(1):76–94.e2. https://doi.org/10.1053/j.gastro.2019.08.058. http://www.sciencedirect.com/science/article/pii/S0016508519414121

    Article  Google Scholar 

  6. Bilic P, Christ PF, Vorontsov E, Chlebus G, Chen H, Dou Q, Fu CW, Han X, Heng PA, Hesser J (2019) The liver tumor segmentation benchmark (LiTS). arXiv:1901.04056

  7. Bray F, Ferlay J, Soerjomataram I, Siegel R L, Torre L A, Jemal A (2018) Global cancer statistics 2018: globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA. A Cancer Journal for Clinicians 68(6):394–424. https://doi.org/10.3322/caac.21492

    Article  Google Scholar 

  8. Bruix J, Sherman M (2011) Management of hepatocellular carcinoma: an update. Hepatology 53(3):1020–1022. https://doi.org/10.1002/hep.24199

    Article  PubMed  PubMed Central  Google Scholar 

  9. Colli A, Fraquelli M, Casazza G, Massironi S, Colucci A, Conte D, Duca P (2006) Accuracy of ultrasonography, spiral ct, magnetic resonance, and alpha-fetoprotein in diagnosing hepatocellular carcinoma: a systematic review: Cme. American Journal of Gastroenterology 101(3):513–523. https://doi.org/10.1111/j.1572-0241.2006.00467.x

    Article  CAS  PubMed  Google Scholar 

  10. Cortes C, Vapnik V (1995) Support-vector networks. Mach. Learn. 20(3):273–297. https://doi.org/10.1007/BF00994018

    Article  Google Scholar 

  11. DeOliveira M L, Cunningham S C, Cameron J L, Kamangar F, Winter J M, Lillemoe K D, Choti M A, Yeo C J, Schulick R D (2007) Cholangiocarcinoma: thirty-one-year experience with 564 patients at a single institution. Ann. Surg. 245(5)

  12. European Association For The Study Of The Liver EOFRTOC (2012) Easleortc clinical practice guidelines: management of hepatocellular carcinoma. J. Hepatol. 56(4):908–943. https://doi.org/10.1016/j.jhep.2011.12.001

    Article  Google Scholar 

  13. Forner A, Vilana R, Ayuso C, Bianchi L, Solé M, Ayuso J R, Boix L, Sala M, Varela M, Llovet J M, Brú C, Bruix J (2008) Diagnosis of hepatic nodules 20 mm or smaller in cirrhosis: prospective validation of the noninvasive diagnostic criteria for hepatocellular carcinoma. Hepatology 47(1):97–104. https://doi.org/10.1002/hep.21966

    Article  PubMed  Google Scholar 

  14. ircad France (2009) 3dircadb. https://www.ircad.fr/research/3dircadb, Accessed on 06.21.2019

  15. Gensure R H, Foran D J, Lee V M, Gendel V M, Jabbour S K, Carpizo D R, Nosher J L, Yang L (2012) Evaluation of hepatic tumor response to yttrium-90 radioembolization therapy using texture signatures generated from contrast-enhanced CT images. Acad. Radiol. 19(10):1201–1207. https://doi.org/10.1016/j.acra.2012.04.015

    Article  PubMed  PubMed Central  Google Scholar 

  16. Heaton J (2008) Introduction to neural networks for Java, 2nd edn. Heaton Research Inc

  17. Heimann T, van Ginneken B, Styner MA, Arzhaeva Y, Aurich V, Bauer C, Beck A, Becker C, Beichel R, Bekes G, Bello F, Binnig G, Bischof H, Bornik A, Cashman PMM, Chi Y, Cordova A, Dawant BM, Fidrich M, Furst JD, Furukawa D, Grenacher L, Hornegger J, KainmÜller D, Kitney RI, Kobatake H, Lamecker H, Lange T, Lee J, Lennon B, Li R, Li S, Meinzer H, Nemeth G, Raicu DS, Rau A, van Rikxoort EM, Rousson M, Rusko L, Saddi KA, Schmidt G, Seghers D, Shimizu A, Slagmolen P, Sorantin E, Soza G, Susomboon R, Waite JM, Wimmer A, Wolf I (2009) Comparison and evaluation of methods for liver segmentation from ct datasets. IEEE Trans Med Imaging 28(8):1251–1265

    Article  Google Scholar 

  18. Imsamran W, Chaiwerawattana A, Wiangnon S, Suwanrungruang K, Sangrajrang S, Buasom R (2018) Cancer in Thailand, vol IX. National Cancer Institute, Thailand

  19. Jemal A, Center MM, DeSantis C, Ward EM (2010) Global patterns of cancer incidence and mortality rates and trends. Cancer Epidemiology and Prevention Biomarkers 19 (8):1893–1907. https://doi.org/10.1158/1055-9965.EPI-10-0437. https://cebp.aacrjournals.org/content/19/8/1893, https://cebp.aacrjournals.org/content/19/8/1893.full.pdf

    Article  Google Scholar 

  20. Kaewpitoon N, Kaewpitoon S J, Pengsaa P (2008) Opisthorchiasis in Thailand: review and current status. World Journal of Gastroenterology 14(15):2297–2302. https://doi.org/10.3748/wjg.14.2297

    Article  PubMed  PubMed Central  Google Scholar 

  21. Kobayashi M, Ikeda K, Saitoh S, Suzuki F, Tsubota A, Suzuki Y, Arase Y, Murashima N, Chayama K, Kumada H (2000) Incidence of primary cholangiocellular carcinoma of the liver in Japanese patients with hepatitis c virus–related cirrhosis. Cancer 88(11):2471–2477. https://doi.org/10.1002/1097-0142(20000601)88:11h2471::AID-CNCR7i3.0.CO;2-T

    Article  CAS  PubMed  Google Scholar 

  22. Krähenbühl P, Koltun V (2011) Efficient inference in fully connected crfs with gaussian edge potentials. In: Shawe-Taylor J, Zemel RS, Bartlett PL, Pereira F, Weinberger KQ (eds) Advances in neural information processing systems, vol 24. Curran Associates Inc., pp 109–117

  23. Lalkhen A G, McCluskey A (2008) Clinical tests: sensitivity and specificity. BJA Education 8(6):221–223. https://doi.org/10.1093/bjaceaccp/mkn041

    Article  Google Scholar 

  24. Lawrence D A, Oliva I B, Israel G M (2012) Detection of hepatic steatosis on contrast-enhanced CT images: diagnostic accuracy of identification of areas of presumed focal fatty sparing. Am. J. Roentgenol. 199(1):44–47. https://doi.org/10.2214/ajr.11.7838

    Article  Google Scholar 

  25. Marrero J A, Kulik L M, Sirlin C B, Zhu A X, Finn R S, Abecassis M M, Roberts L R, Heimbach J K (2018) Diagnosis, staging, and management of hepatocellular carcinoma: 2018 practice guidance by the american association for the study of liver diseases. Hepatology 68(2):723–750. https://doi.org/10.1002/hep.29913

    Article  PubMed  Google Scholar 

  26. Midya A, Chakraborty J, MD L M P, MD J Z, MD W R J, MD R K G D, Simpson A L (2018) Deep convolutional neural network for the classification of hepatocellular carcinoma and intrahepatic cholangiocarcinoma. In: Petrick N, Mori K (eds) Medical Imaging 2018: Computer-Aided Diagnosis, International Society for Optics and Photonics, SPIE, vol 10575, pp 501–506, https://doi.org/10.1117/12.2293683

  27. Moccia S, Mattos L S, Patrini I, Ruperti M, Poté N, Dondero F, Cauchy F, Sepulveda A, Soubrane O, Momi E D, Diaspro A, Cesaretti M (2018) Computer-assisted liver graft steatosis assessment via learning-based texture analysis. Int. J. Comput. Assist. Radiol. Surg. 13(9):1357–1367. https://doi.org/10.1007/s11548-018-1787-6

    Article  PubMed  Google Scholar 

  28. Nayak A, Baidya Kayal E, Arya M, Culli J, Krishan S, Agarwal S, Mehndiratta A (2019) Computer-aided diagnosis of cirrhosis and hepatocellular carcinoma using multi-phase abdomen ct. Int. J. Comput. Assist. Radiol. Surg. 14(8):1341–1352. https://doi.org/10.1007/s11548-019-01991-5

    Article  PubMed  Google Scholar 

  29. Ohishi W, Fujiwara S, Cologne J B, Suzuki G, Akahoshi M, Nishi N, Tsuge M, Chayama K (2011) Impact of radiation and hepatitis virus infection on risk of hepatocellular carcinoma. Hepatology 53 (4):1237–1245. https://doi.org/10.1002/hep.24207

    Article  PubMed  Google Scholar 

  30. Rimola J, Forner A, Reig M, Vilana R, de Lope C R, Ayuso C, Bruix J (2009) Cholangiocarcinoma in cirrhosis: absence of contrast washout in delayed phases by magnetic resonance imaging avoids misdiagnosis of hepatocellular carcinoma. Hepatology 50(3):791–798. https://doi.org/10.1002/hep.23071

    Article  PubMed  Google Scholar 

  31. Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells W M, Frangi A F (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI, vol 2015. Springer International Publishing, Cham, pp 234–241

  32. Sangiovanni A, Manini M A, Iavarone M, Romeo R, Forzenigo L V, Fraquelli M, Massironi S, Della Corte C, Ronchi G, Rumi M G, Biondetti P, Colombo M (2010) The diagnostic and economic impact of contrast imaging techniques in the diagnosis of small hepatocellular carcinoma in cirrhosis. Gut 59 (5):638–644. https://doi.org/10.1136/gut.2009.187286

    Article  PubMed  Google Scholar 

  33. Sato M, Morimoto K, Kajihara S, Tateishi R, Shiina S, Koike K, Yatomi Y (2019) Machine-learning approach for the development of a novel predictive model for the diagnosis of hepatocellular carcinoma. Sci. Rep. 9(1):7704. https://doi.org/10.1038/s41598-019-44022-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Spina J C, Hume I, Pelaez A, Peralta O, Quadrelli M, Monaco R G (2019) Expected and unexpected imaging findings after 90y transarterial radioembolization for liver tumors. RadioGraphics 39(2):578–595. https://doi.org/10.1148/rg.2019180095

    Article  PubMed  Google Scholar 

  35. Tomimatsu M, Ishiguro N, Taniai M, Okuda H, Saito A, Obata H, Yamamoto M, Takasaki K, Nakano M (1993) Hepatitis c virus antibody in patients with primary liver cancer (hepatocellular carcinoma, cholangiocarcinoma, and combined hepatocellular-cholangiocarcinoma) in Japan. Cancer 72(3):683–688. https://doi.org/10.1002/1097-0142(19930801)72:3h683::AID-CNCR2820720310i3.0.CO;2-C

    Article  CAS  PubMed  Google Scholar 

  36. Welzel T M, Graubard B I, El–Serag H B, Shaib Y H, Hsing A W, Davila J A, McGlynn K A (2007) Risk factors for intrahepatic and extrahepatic cholangiocarcinoma in the united states: a population-based case-control study. Clin. Gastroenterol. Hepatol. 5(10):1221–1228. https://doi.org/10.1016/j.cgh.2007.05.020

    Article  PubMed  PubMed Central  Google Scholar 

  37. Yasaka K, Akai H, Abe O, Kiryu S (2018) Deep learning with convolutional neural network for differentiation of liver masses at dynamic contrast-enhanced ct: a preliminary study. Radiology 286 (3):887–896. https://doi.org/10.1148/radiol.2017170706

    Article  PubMed  Google Scholar 

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Acknowledgments

We would like to thank Chiang Mai University, Thailand, for financial support and computing resources.

Funding

This study was funded by Faculty of Medicine, Chiang Mai University, Thailand.

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Authors and Affiliations

  1. Data Science Research Center, Department of Statistics, Faculty of Science, Chiang Mai University, Chiang Mai, 50200, Thailand

    Donlapark Ponnoprat & Patrinee Traisathit

  2. Advanced Research Center for Computational Simulation, Chiang Mai University, Chiang Mai, 50200, Thailand

    Papangkorn Inkeaw

  3. Data Science Research Center, Department of Computer Science, Faculty of Science, Chiang Mai University, Chiang Mai, 50200, Thailand

    Jeerayut Chaijaruwanich

  4. Research Institute for Health Sciences, Chiang Mai University, Chiang Mai, 50200, Thailand

    Patumrat Sripan

  5. Department of Radiology, Faculty of Medicine, Chiang Mai University, Chiang Mai, 50200, Thailand

    Nakarin Inmutto, Wittanee Na Chiangmai & Imjai Chitapanarux

  6. Cancer Registry Unit, Lampang Cancer Hospital, Lampang, 52000, Thailand

    Donsuk Pongnikorn

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  1. Donlapark Ponnoprat
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Correspondence to Papangkorn Inkeaw.

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The authors declare that they have no competing interests.

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This study was approved and monitored by the Ethics Committee of Faculty of Medicine, Chiang Mai University, Thailand.

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Author contributions

Conceptualization: Donlapark Pornnopparath, Papangkorn Inkeaw. Methodology: Donlapark Pornnopparath, Papangkorn Inkeaw. Software: Donlapark Pornnopparath, Papangkorn Inkeaw. Validation: Donlapark Pornnopparath, Papangkorn Inkeaw. Formal analysis: Donlapark Pornnopparath, Papangkorn Inkeaw. Investigation: Donlapark Pornnopparath, Papangkorn Inkeaw. Data curation: Donlapark Pornnopparath, Patumrat Sripan, Nakarin Inmutto. Writing—original draft: Donlapark Pornnopparath, Papangkorn Inkeaw, Patumrat Sripan. Writing—review and editing: Jeerayut Chaijaruwanich, Patrinee Traisathit, Nakarin Inmutto, Wittanee Na Chiangmai, Donsuk Pongnikorn, Imjai Chitapanarux. Visualization: Donlapark Pornnopparath, Papangkorn Inkeaw. Supervision: Jeerayut Chaijaruwanich, Patrinee Traisathit, Imjai Chitapanarux. Project administration: Nakarin Inmutto. Funding acquisition: Nakarin Inmutto.

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Ponnoprat, D., Inkeaw, P., Chaijaruwanich, J. et al. Classification of hepatocellular carcinoma and intrahepatic cholangiocarcinoma based on multi-phase CT scans. Med Biol Eng Comput 58, 2497–2515 (2020). https://doi.org/10.1007/s11517-020-02229-2

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