Reza Forghani, MD, PhD - Reza Forghani, MD, PhD - Research Institute MUHC
Artificial Intelligence • Advanced Imaging • Machine Learning • Natural Language Processing • Diagnostic Radiology • Medical Imaging • Dual Energy Computed Tomography • Spectral Computed Tomography • Computed Tomography • Magentic Resonance Imaging • Head and Neck Imaging • Neuroradiology • Neuroimaging • Head and Neck Cancer • Head and Neck Squmaous Cell Carcinoma
My research focuses on applications of artificial intelligence and advanced medical imaging, including an advanced type of CAT scan referred to as dual energy CT, for improving patient diagnostics. Our laboratory focuses especially on cancer imaging. Using the most advanced technologies, we investigate different imaging techniques and artificial intelligence for improving cancer diagnosis, with the ultimate goal of providing the best patient care by identifying factors unique to the patient for a more personalized medicine. We investigate using imaging and other clinical information with artificial intelligence to better stage and characterize cancers at the onset. The aims are to reduce unnecessary treatment or surgery, select the best therapy, predict tumour characteristics such as molecular composition, and facilitate prognosis. A significant part of this work is focused on head and neck cancer, but our team also investigates other cancers, as well as applications of artificial intelligence for non-oncologic diseases or healthcare processes.
Al Ajmi E, Forghani B, Reinhold C, Bayat M, Forghani R (2018). Spectral multi-energy CT texture analysis with machine learning for tissue classification: an investigation using classification of benign parotid tumours as a testing paradigm. European Radiology, 2018 Jun;28(6):2604-2611. doi: 10.1007/s00330-017-5214-0. [Epub ahead of print]. PMID: 29294157.
Forghani R, Kelly HR, Curtin HD (2017). Applications of dual-energy computed tomography for the evaluation of head and neck squamous cell carcinoma. Neuroimaging Clin N Am. 2017 Aug;27(3):445-459. doi: 10.1016/j.nic.2017.04.001. PMID: 28711204.
Forghani R, Srinivasan A, Forghani B (2017). Advanced tissue characterization and texture analysis using dual-energy computed tomography: Horizons and emerging applications. Neuroimaging Clin N Am. 2017 Aug;27(3):533-546. doi: 10.1016/j.nic.2017.04.007. PMID: 28711211.
Ueno Y, Forghani B, Forghani R, Dohan A, Zeng Z, Chamming’s F, Arseneau J, Fu L, Gilbert L, Gallix B, Reinhold C (2017). Endometrial Carcinoma: MR Imaging-based texture model for preoperative risk stratification-A preliminary analysis. Radiology. 2017 Sep;284(3);748-757. doi: 10.1148/radiol.2017161950. PMID: 28493790.
Forghani R, Levental M, Gupta R, Lam S, Dadfar N. Curtin HD (2015). Different Spectral Hounsfield Unit Curve and High-Energy Virtual Monochromatic Image Characteristics of Squamous Cell Carcinoma Compared with Nonossified Thyroid Cartilage. AJNR Am J Neuroradiol. 2015 Jun; 36(6): 1194-200. doi: 10.3174/ajnr.A4253. PMID: 25742986.