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 

  • 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. Imaging-based diagnosis and monitoring of liver cancer.

  2. Development, validation and clinical translation of imaging-based biomarkers of chronic liver disease which are risk factors to development of hepatocellular carcinoma

  3. Pathologies: hepatocellular carcinoma (HCC), liver metastases, chronic liver disease, steatosis, steatohepatitis, liver fibrosis.


Current projets (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. Léonie Petitclerc, B.Sc., research assistant, leonie.petitclerc3@gmail.com

  2. Assia Belblidia, trained as MD, research assistant, assia.belblidia.chum@ssss.gouv.qc.ca

  3. Walid, El Abyad Walid, trained as MD, research assistant, walid.el.abyad.chum@ssss.gouv.qc.ca

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

  5. Siavash Kazemirad, Ph.D., siavash.kazemirad@mail.mcgill.ca

  6. Pol Grasland-Mongrain, Ph.D., pol.grasland-mongrain@ens-cachan.org


Former students

  1. Akshat Gotra, M.D.

  2. Eric Zhang, M.D.


  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

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

  11. 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.

  12. 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.

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