Dr An Tang, M.D., M.Sc.

Associate professor;
Radiology, Faculty of Medicine;
University of Montreal;
CHUM, CRCHUM and Institut du cancer de Montréal.
Key words: Liver cancer, hepatocellular carcinoma (HCC), liver metastases, imaging, magnetic resonance imaging, ultrasound, elastography, quantitative biomarkers, steatosis, fibrosis.

Contact :


Office: 514-890-8000 x.31418

2000 MD, University of Sherbrooke

2005 Diploma, University of Montreal, Radiology

2006 Fellowship, University of Toronto, Abdominal Imaging Fellowship, Supervisor: Dr. Stephanie Wilson

2012 MSc, University of Montreal, Supervisor: Dr. Gilles Soulez

2012 Fellowship, University of California, San Diego, Supervisor: Dr. Claude Sirlin

2013-2017 FRQS Clinical Research Scholarship

2011-2012 Research Scholarship, Fullbright program 

2011-2012 CIHR Fellowship Award 


  • 2020-2022 (Co-PI) Operating Grant, Onco-Tech (Consortium composed of Oncopole, Medteq, Institut TransMedTech, Société de recherche sur le cancer)

    "Added value of shear wave viscoelasticity imaging, homodyned K tissue imaging and acoustic attenuation to assess liver cancer at ultrasound: A multiparametric machine learning approach" 
  • 2020-2022 (PI) Operating Grant

    Institut de valorisation des données (IVADO)
    "Ultrasound classification of chronic liver disease with deep learning" 
  • 2018-2023 (PI) Operating Grant

    Canadian Institutes of Health Research (CIHR)
    "Quantitative ultrasound techniques for diagnosis of nonalcoholic steatohepatitis"
  • 2015-2018 (Co-PI) Operating Grant, Consortium for research and innovation in medical technologies in Quebec (MEDTEQ)

    "Cancer Analysis with DEep Learning Artifical intelligence (CANDELA)"
  • 2013-2015 (PI) Operating Grant, IRSC.

    "Comparison of MR and US Elastography with Liver Biopsy for Noninvasive Staging of Liver Fibrosis."

  • 2011-2012 (PI) Operating Grant. RSNA R&E Foundation, CHAR-GE Development Awards and Diabète Québec.

    "Randomized trial of liraglutide and insulin on hepatic steatosis"


Awards and prixes

2015 Young Investigator Award, Canadian Association of Radiologists.

2012 Bernadette-Nogrady Award, Société Canadienne-Française de Radiologie.

2012 Bronze Award, European Society of Gastrointestinal and Abdominal Radiology.

Description of research interests:

  1. Modalities: magnetic resonance imaging (MRI), computed tomography (CT), ultrasound (US).

  2. DWI-IVIM for assessment of liver inflammation, MRI-PDFF for liver fat quantification, R2* for liver iron quantification, MR elastography for liver fibrosis quantification.

  3. Machine learning for liver tissue and tumor classification.


Current projects (techniques used):

  1. Modalities: magnetic resonance imaging (MRI), computed tomography (CT), ultrasound (US).

  2. DCE-MRI for assessment of liver tumor perfusion, MRI-PDFF for liver fat quantification, elastography for liver fibrosis quantification, T2* and R2* for liver iron quantification


Associated websites:

  1. CRCHUM webpage

  2. NCBI webpage.



  • Liver imaging (liver cancer and diffuse liver disease)

  1. Catherine Huet, research assistant, catherine.huet.chum@ssss.gouv.qc.ca

  2. Emmanuel Montagnon, research associate, emmanuelmontagnon@gmail.com

  3. Jennifer Satterthwaite, research assistant, Jennifer.satterthwaite.chum@ssss.gouv.qc.ca

  4. Thierry Lefebvre, MSc, thierry.lefebvre@live.ca 


  • Tang, A., I. Cruite, and C.B. Sirlin, Toward a standardized system for hepatocellular carcinoma diagnosis using computed tomography and MRI. Expert Rev Gastroenterol Hepatol, 2013. 7(3): p. 269-79.

  • Tang, A., et al., Optimal Pancreatic Phase Delay with 64-Detector CT Scanner and Bolus-tracking Technique. Acad Radiol, 2014. 21(8): p. 977-85.

  • Tang, A., M.A. Valasek, and C.B. Sirlin, Update on the Liver Imaging Reporting and Data System: What the Pathologist Needs to Know. Adv Anat Pathol, 2015. 22(5): p. 314-22.

  • Tang, A., et al., Ultrasound Elastography and MR Elastography for Assessing Liver Fibrosis: Part 2, Diagnostic Performance, Confounders, and Future Directions. AJR Am J Roentgenol, 2015. 205(1): p. 33-40.

  • Tang, A., et al., Ultrasound Elastography and MR Elastography for Assessing Liver Fibrosis: Part 1, Principles and Techniques. AJR Am J Roentgenol, 2015. 205(1): p. 22-32.

  • Costa, E.A., et al., Diagnostic Accuracy of Preoperative Gadoxetic Acid-enhanced 3-T MR Imaging for Malignant Liver Lesions by Using Ex Vivo MR Imaging-matched Pathologic Findings as the Reference Standard. Radiology, 2015. 276(3): p. 775-86.

  • Cruite, I., A. Tang, and C.B. Sirlin, Imaging-based diagnostic systems for hepatocellular carcinoma. AJR Am J Roentgenol, 2013. 201(1): p. 41-55.

  • Grasland-Mongrain, P., et al., Contactless remote induction of shear waves in soft tissues using a transcranial magnetic stimulation device. Phys Med Biol, 2016. 61(6): p. 2582-93.

  • Hanna, R.F., V.Z. Miloushev, and A. Tang, Comparative 13-year meta-analysis of the sensitivity and positive predictive value of ultrasound, CT, and MRI for detecting hepatocellular carcinoma. 2016. 41(1): p. 71-90.

  • Kadoury, S., E. Vorontsov, and A. Tang, Metastatic liver tumour segmentation from discriminant Grassmannian manifolds. Phys Med Biol, 2015. 60(16): p. 6459-78.

  • Marks, R.M., et al., Diagnostic per-patient accuracy of an abbreviated hepatobiliary phase gadoxetic acid-enhanced MRI for hepatocellular carcinoma surveillance. AJR Am J Roentgenol, 2015. 204(3): p. 527-35.

  • Santillan, C.S., et al., Understanding LI-RADS: a primer for practical use. Magn Reson Imaging Clin N Am, 2014. 22(3): p. 337-52.

  • Shah, A., et al., Cirrhotic liver: What's that nodule? The LI-RADS approach. J Magn Reson Imaging, 2016. 43(2): p. 281-94.

  • Tang A, Abukasm K, Cunha GM, Song B, Wang J, Wai A, Wagner M, Dietrich C, Brancatelli G, Ueda K, Choi JY, Aguirre D, Sirlin CB. Imaging of hepatocellular carcinoma: a pilot international survey [published online ahead of print, 2020 Jun 1]. Abdom Radiol (NY). 2020;10.1007/s00261-020-02598-0. doi:10.1007/s00261-020-02598-0 DOI: 10.1007/s00261-020-02598-0. https://rdcu.be/b4zYP

  • Mansour R, Thibodeau Antonacci A, Bilodeau L, Romaguera L, Cerny M, Gilbert G, Tang A, Kadoury S. Impact of temporal resolution and motion correction for dynamic contrast-enhanced MRI of the liver using an accelerated golden-angle radial sequence. Physics in Medicine and Biology. 2020;65(8):085004. Published 2020 Apr 17. doi:10.1088/1361-6560/ab78be
  • Maaref A, Perdigon Romero F, Montagnon E, Cerny M, Nguyen B, Vandenbroucke-Menu F, Geneviève S, Turcotte S, Tang A, Kadoury S. Predicting the Response to FOLFOX-based Chemotherapy Regimen from Untreated Liver Metastases on Baseline CT: A Deep Neural Network Approach. Journal of Digital Imaging. 2020 Mar 19. doi: 10.1007/s10278-020-00332-2. PubMed PMID: 32193665. https://link.springer.com/article/10.1007/s10278-020-00332-2

  • Voizard N, Cerny M, Assad A, Billiard JS, Olivié D, Perreault P, Kielar A, Do RKG, Yokoo T, Sirlin CB, Tang A. Assessment of Hepatocellular Carcinoma Treatment Response with LI-RADS: a Pictorial Review. Insights into Imaging. 2019 Dec 18;10(1):121. doi: 10.1186/s13244-019-0801-z. Review. PMID: 31853668 

  • Featured in: Highlights in Insights into Imaging

  • Thibodeau-Antonacci A, Petitclerc L, Gilbert G, Bilodeau L, Olivié D, Cerny M, Castel H, Turcotte S, Huet C, Perreault P, Soulez G, Chagnon M, Kadoury S, Tang A. Dynamic Contrast-Enhanced MRI to Assess Hepatocellular Carcinoma Response to Transarterial Chemoembolization Using LI-RADS Criteria: a Pilot Study. Magn Reson Imaging. 2019 Jun 25;62:78-86. doi: 10.1016/j.mri.2019.06.017. [Epub ahead of print] https://doi.org/10.1016/j.mri.2019.06.017