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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Featured project

Development of an automatic prognostic tool combining images and clinical data for highgrade prostate cancer.

Student member : Maxence Larose

Prostate cancer is the second most frequent cancer and the fifth leading cause of cancer death among men. To improve patient outcomes, treatment must be personalized based on accurate prognosis. Nomograms already exist to identify patients at low risk for recurrence based on preoperative clinical information, but these tools do not use patients’ medical images.

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