Research Studentship - Omid Shearkhani

Omid Shearkhani is a student at the University of Toronto.

Omid's project has been generously funded by a gift in memory of Ron Hobbs

Update: November 2017

"I am writing to give you an update on our project. Recently we were lucky enough to publish our work - which was made possible by the kind and generous donors at Brain Tumour Foundation of Canada - at the AJNR. AJNR is one of Radiology's most reputable journals, and the top journal in the field of Neuroradiology. Moreover, the project was recently the recipient of the Stephen A. Kieffer Award during the 2017 Eastern Neuroradiological Conference, for being the "Best scientific paper by a trainee". We are very excited about this and sincerely hope our work will have a positive impact on the care of patients who are battling with metastatic brain tumours.
 
Thank you so much for providing me with this amazing opportunity. I sincerely appreciate all your help and support throughout this wonderful journey."
 
 

About Omid's experience

Research Student - Omid ShearkhaniBecause of the amazing experience I have had in the field of brain tumor research as a direct result of Brain Tumour Foundation of Canada’s Research Studentship award, my plan for future is to enter the Clinician Investigator Program at my homeschool, University of Toronto, where I can complete my education as a diagnostic medical imaging resident, while simultaneously pursuing my graduate studies, at the doctoral level, in computer science and image processing. Receiving the prestigious Brain Tumour Foundation of Canada’s Research Studentship award meant that I could pursue a research project in the field that I am truly passionate about, and to utilize my skills and experiences in order to have a positive impact, however small, on the lives of many patients who are battling with metastatic brain tumors. And through that, I was able to grow as a scientist and future physician, and thus prepare myself for a career as a clinician scientist.

Summary of work

Metastatic brain tumors (MBTs) occur in 24–45% of patients diagnosed with primary cancers outside the brain. For treatment of MBTs, stereotactic radiosurgery (SRS) has been shown to be associated with superior rates of survival and fewer complications compared to whole brain radiotherapy (WBRT). Critical to SRS treatment planning and follow-up is accurate volume assessment. However, serial volumetric imaging obtained every 2–3 months results in a demanding workload for radiologists, especially for detection of subtle volume changes. One method used to increase radiologists accuracy and efficiency is utilisation of computer-aided detection tools. However, at present the literature lacks studies that have evaluated the efficacy of computer algorithms in regards to follow-up of MBTs, and detection of growing and shrinking MBTs (vMBTs). The fully automated algorithm that was the outcome of our project was able to detect vMBTs on longitudinal brain MRI with a statistically high accuracy, demonstrating its potential to complement the performance of radiologists and radiation oncologists. This in turn would translate to better treatment planning and follow-up for patients battling with MBTs. Currently we have submitted our manuscript to the prestigious journal of Neuro-Oncology, and are awaiting their decision.
 

About the research

Project title: “Developing and evaluating the performance of an automated computer-aided detection software for the follow-up of metastatic brain tumours”
 
Project Update, May 2016:
 
Initially our technique relied on intensity-based approaches for detecting changing metastatic brain tumours across MR scans. In such approach, after aligning two consecutive scans of the brain of the same patient at different time points, the intensity of respective voxels on each volume is compared. Tumour tissue in an area that previously did not have tumour tissue (i.e. tumour expansion) would result in an increase in intensity on the second scan, and shrinkage of tumour would result in decrease of voxel intensity on the second scan. This approach, however, resulted in a high false positive rate, since other hyperintense structures such as pulsating vessels and movement of dura results in similar changes in intensity (as tumours) across scans. The only approach to minimize such false positives would be to use machine learning techniques which would be resource intensive. To avoid this we looked into the literature for alternative approaches, and were able to find a very suitable approach called deformity-based approach. In this method the deformity filed that results from non-linear co-registration of the two scans is quantified. This quantification gives information about the compression and expansion of the structures that are present in the two scans. After implementing this method, we were able to achieve promising results. Our plan is to focus solely on reducing false positives to improve our current technique so that it has the sensitivity and positive predictive value that is required for use in clinical settings.
 
Project Description:

Brain tumours are developed in about 32 out of 100’000 individuals. Out of this number, 75% originated from a region of the body other than the brain, but was spread to the brain (referred to as metastatic brain tumours; MBTs). Furthermore, 20-40% of the individuals with cancer develop MBTs at some point, and as cancer patients live longer, this number is increasing. MBTs can result in mortality and devastating morbidities. Follow-up of MBTs and their changes over time, especially of MBTs smaller in size, remains to be a difficult task, associated with an innate risk of error due to user subjectivity and the demanding workload.
 
Computer-aided detection (CAD) methods have been successfully used to analyze breast and prostate cancers in magnetic resonance imaging (MRI) scans. To date, no study has focused on developing a CAD system to facilitate MBTs’ follow-up. A software capable of attaining that purpose can serve as a powerful tool in treatment planning and monitoring MBTs, because accurate follow-up of MBTs is necessary for chemotherapy, radiation planning, and surgical approach. The purpose of this study is to develop a CAD software in order to improve accuracy and efficiency for the follow-up of MBTs. 

About Omid, in his own words...

It may not be common to see a medical student who is deeply passionate about mathematics and computer science. Some may even find it paradoxical, because the abstract field of mathematics may seem irrelevant to the practical field of medicine. As a medical student, a technology aficionado, and someone who majored in mathematics as an undergraduate student, I have been amazed by how mathematical models and computer programs are utilized in the fight to conquer brain cancer. It is for this reason that I believe brain tumour research is the field that encompasses all of my interests. It is my ultimate niche, and where my passions and skills intersect.
 
Being awarded a Brain Tumour Research Studentship, made possible by the generous contribution from kind donors, means that I can pursue a research project in the field that I am truly passionate about, and to utilize my skills and experiences in order to have a positive impact, however small, on the lives of many patients who are battling with brain tumours. This opportunity will allow me to grow as a scientist and future physician, and will thus prepare me for a career as a clinician scientist.
 

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