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Isocitrate Dehydrogenase 1 Mutation Status of Low-Grade Gliomas Prediction from Radiomics Biomarkers of Conventional MRI scans

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Mojtaba Safari

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Isocitrate Dehydrogenase 1 Mutation Status of Low-Grade Gliomas Prediction from Radiomics Biomarkers of Conventional MRI scans

In 2016, the world health organization recommended using genotypic information such as isocitrate dehydrogenase (IDH) mutation status for grading and treatment planning of the central nervous system cancers such as low-grade gliomas. This presentation proposes a machine learning technique to predict Isocitrate dehydrogenase 1 (IDH 1) gene mutation status from T1, T1-Gd, T2, and T2-fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) sequences. 

 

Biography


Mojtaba Safari is a Ph.D. student at Medical Physics program at the Département de physique, de génie physique et d'optique in the Université Laval. Mojtaba is doing Ph.D. under the supervision of Louis Archambault. 

Mojtaba is interested in MRI physics and quantitative MRI sequences. He is also passionate about machine learning (ML) applications in medical imaging. He has done some projects on quantitative MRI sequences radiomics biomarkers for predicting brain tumor type and grade.
 

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Intraoperative analgesic treatment decisions based on the NOL index: contribution of a data-based approach to improve accuracy and relevance

Student member : Marzieh Ghiyasinasab

This research project is based on the analysis of massive data on the NOL index and other intraoperative clinical parameters used by anesthesiologists during surgery. These parameters help them make analgesic treatment decisions in a non-communicating patient under general anesthesia and in whom it is impossible to assess pain and analgesic needs by standard questionnaires performed on awake patients. 
First, the objective is to interpret the values of this index in relation to the decisions made by the clinician. 

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