August 28, 2020
The tools behind the treatment: Building image-guided devices for more accurate and effective cancer procedures
OICR-supported researchers develop multi-purpose AI algorithm to help track needle placement and improve the accuracy of several image-guided treatment techniques
Cancer patients often encounter many needles, some of which are used to collect tissue samples or deliver therapy directly to a tumour. Specialists who carry out these procedures are trained to place needles precisely in the correct location, but what if we could give these specialists a real-time GPS for needles? Would biopsies be more accurate? Could needle-related therapies be more effective?
Dr. Aaron Fenster’s lab is working to develop tools for these specialists to guide their needles and ultimately improve the accuracy of biopsies and therapies for patients. In their recent paper, published in Medical Physics, they describe their new deep learning method to track needles in ultrasound images in real time.
“It may be surprising to many individuals, but a lot of these procedures are still done based on skill alone and without image processing,” says Dr. Derek Gillies, medical physicist in training and co-first author of the paper. “We’re working to provide clinicians with tools so they can better see their needles in real time rather than going in blind for some procedures.”
The deep learning methods presented in this paper are applicable to many types of needle procedures, from biopsies – where a clinician draws a tumour sample from the body – to brachytherapy – where a clinician delivers radiotherapy directly to the tumour. The methods could also be applied to several cancer types including kidney cancer, liver cancer and gynecologic cancers.
“Developing artificial intelligence algorithms requires a lot of data,” says Jessica Rodgers, co-first author of the paper and PhD Candidate at Western University’s Robarts Research Institute. “We didn’t have a lot of imaging data from gynecologic procedures, so we decided to team up to develop a method that could work across several applications and areas of the body.”
“That’s the most exciting aspect of this effort,” says Gillies. “To our knowledge, we were the first to develop a generalizable needle segmentation deep learning method.”
Now, members of the Fenster lab are working to integrate these algorithms into the video software equipment used in the clinic.
“Our work is giving clinicians new tools, which can help them make these procedures more precise and more accessible,” says Rodgers. “These tools could ultimately help lead to fewer missed cancer diagnoses and fewer patients with cancer recurrence.”
September 3, 2019
OICR is proud to welcome Dr. Parisa Shooshtari as an OICR Investigator.
Shooshtari specializes in developing computational, statistical and machine learning methods to understand the biological mechanisms underlying complex diseases, like cancer and autoimmune conditions. She is interested in uncovering how genes are dysregulated in complex diseases by integrating multiple data types and applying machine learning methods to analyze single-sell sequencing data.
Of her many achievements, Shooshtari developed a computational pipeline to uniformly process more than 800 epigenomic data samples from different international consortia. She then built and led a team that developed a web-interface and an interactive genome-browser to make the database publicly available to download and explore.
Shooshtari joins the OICR community with research experience from Yale University and the Broad Institute of MIT and Harvard. She also served as a Research Associate with the Centre for Computational Medicine at the Hospital for Sick Children (SickKids).
Shooshtari recently became an Assistant Professor in the Schulich School of Medicine and Dentistry at Western University, where she officially began her career as an independent researcher. Here, Shooshtari discusses her commitment to collaboration and her transition to professorship.
Your work spans multiple disease areas from autoimmune diseases to cancer, what do these diseases have in common? Is there a specific disease that you’re more interested in?
My work focuses on complex diseases, where instead of one gene causing the disease, there are sometimes tens or hundreds of genes working together to give rise to an ailment.
When it comes to complex diseases, we also know that there are multiple factors that we need to consider, including genetics, epigenetics and environmental factors. We live in an era where we have rich datasets with many different types of data. Each of these data types sheds light upon a different aspect of the disease mechanism, but we need to integrate these data types to gain a comprehensive understanding of how a complex disease works.
I develop computational methods for integrative analysis, so complex diseases are definitely the most interesting to me. I feel lucky to be a researcher at this time when I can help bring these data types together to understand mechanisms of diseases, which in turn will help inform treatment selection or help find new therapeutic strategies.
I am interested in applying our data integration methods to several complex diseases but I am currently working with a few Canadian groups to help better understand Diffuse Intrinsic Pontine Glioma (DIPG) – a type of fatal childhood brain cancer.
Your current collaborators include researchers from Yale, Harvard, MIT, SickKids and other leading organizations. How did you initiate and sustain these collaborations?
At the beginning of my research career, I would reach out to scientists who were working on interesting, challenging and cutting-edge problems. I enjoy working in collaborative environments because I believe the key to success in biomedical research is through collaborations between researchers from diverse backgrounds.
With the support of my collaborators, I’ve been able to learn and shift my focus from theoretical computational sciences to applications of data science in genetics of complex diseases. Now, sometimes collaborators approach me with their rich data, which I’m eager to help analyze.
With your new appointment, what are you looking forward to over the next few years?
I am eager to continue expanding my research program and working with new scientists on exciting cutting-edge problems in genetics and epigenetics of complex diseases. New technologies have revolutionized how we study diseases, and we are transitioning to a point where these new technologies are revolutionizing how we treat diseases. I am confident that we will have better ways of treating these diseases in the future using personalized medicine, and I want to help make that a reality.
March 8, 2017
In London, OICR leaders discussed cancer research advancements being made in the city. How can OICR help further translate these breakthroughs to patients?
Ontario’s wealth of cancer research expertise is not limited to one city or region. Innovations from researchers and clinician-scientists across the province are changing the approach to cancer worldwide. London is one of Ontario’s major cancer research nodes and boasts a particular strength in developing medical imaging technology. The city is home to the Lawson Health Research Institute, Robarts Research Institute and the Centre for Imaging Technology Commercialization. Life science and biotechnology research is the source of $1.5 billion in economic activity for the city annually.