Department of Oncology, Division of Radiation Oncology
Jewish General Hospital
Room G-002
3755 Côte Ste. Catherine Road
Montréal, Québec, H3T 1E2
T: 514-340-8288
F: 514-340-7548
Dr. Tamim Niazi, MDCM
Investigator, Lady Davis Institute
Department of Oncology, Division of Radiation Oncology
Associate Professor of the Department of Oncology, McGill University
Dr. Tamim Niazi received his Doctor of Medicine and Master of Surgery (MDCN) designation from McGill University in 2001. He did his internship and residency at the McGill University Health Centre. In 2006-2007, he was the Astra Zeneca Clinical Trials Fellow with the National Cancer Institute of Canada Clinical Trials Group (NCIC-CTG).
He is currently an Assistant Professor of the Department of Oncology at McGill. His clinical expertise makes him a sought after lecturer on both the academic and medical fronts. His specialties include GU malignancies (prostate, bladder, testicle etc.), GI malignancies (stomach, pancreas, rectum, anal canal), and gynecological malignancies.
Research Interests
Dr. Niazi’s reserch interests include:
Stereotactic ablative radiotherapy (SABR),
Prostate cancer oligometastases and metastases directed therapy (MDT),
personalized Prostate cancer treatment approach,
Prostate cancer treatment intensification,
Prostate Cancer biomarkers,
Radiomics and artificial intelligence (AI).
Recent Publications
Chaddad A, Kucharczyk MJ, Desrosiers C, Okuwobi IP, Katib Y, Zhang M, Rathore S, Sargos P, Niazi T. Deep radiomic analysis to predict Gleason score in Prostate Cancer. IEEE Access. doi: 10.1109/ACCESS.2017

Kucharczyk M, So J, Gravis G, Sweeney C, Saad F, Niazi T. A combined biological and clinical rationale for evaluating metastasis directed therapy in the management of oligometastatic prostate cancer. Radiotherapy and Oncology.

Kucharczyk MJ, Tsui JMG, Khosrow-Khavar F, Bahoric B, Souhami L, Anidjar M, Probst S, Chaddad A, Sargos P, Niazi T. Combined Long-Term Androgen Deprivation and Pelvic Radiotherpay in the Post-operative Management of Pathologically Defined High-Risk Prostate Cancer Patients: Results of the Prospecitve Phase II McGill 0913 Study. Frontiers in Oncology., 12 March 2020 /

Chaddad A, Zhang M, Desrosiers C, Niazi T. Deep radiomic features from MRI scans predict survival outcome of recurrent glioblastoma. MICCAI RNO-AI 2019, LNCS 11991, pp. 36-43, 2020.

Rathore S, Chaddad A, Bukhari NH, Niazi T. Imaging signature of 1p/19q co-deletion status derived via machine learning in low-grade glioma. MICCAI RNO-AI 2019, LNCS 11991, pp. 61-69, 2020.

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