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

Dr. Derek Gillies and Jessica Rogers
Dr. Derek Gillies and Jessica Rodgers

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.”

Read more about OICR’s Imaging Program, or the latest OICR Imaging news.

August 12, 2019

Dr. Rola Saleeb on her path to becoming a pathologist

Rola Saleeb
Dr. Rola Saleeb, Assistant Professor, Department of Laboratory Medicine and Pathobiology, University of Toronto.

OMPRN grantee and former Transformative Pathology Fellow discusses her recently-awarded faculty appointment with the University of Toronto

Despite research advances in identifying the subtypes of kidney cancer, treatment decisions are often based on the size of a patient’s tumour. Dr. Rola Saleeb, who has been studying kidney cancer for nearly a decade, thinks there’s a better way to make these decisions.

“Each month, more than 500 people are diagnosed with kidney cancer in Canada,” says Saleeb. “These individuals and their oncologists face tough decisions to make about their treatment options and I want to help make that decision easier.”

Saleeb, a former OICR Transformative Pathology Fellow and two-time Ontario Molecular Pathology Network (OMPRN) grantee, has recently become a certified pathologist and faculty member in the Department of Laboratory Medicine and Pathobiology at the University of Toronto.

Throughout her doctoral research, Saleeb developed a classification system that could help pathologists distinguish between aggressive kidney cancers and less aggressive cancers. She says this system could, one day, help spare patients from unnecessary surgery if they don’t have aggressive tumours. Additionally, she says classifying these tumours could enable the development of new therapies for these subtypes.

Now as a certified pathologist, Saleeb is the second Transformative Pathology Fellow to have been recruited to a faculty position. Both former fellows have committed to a career where research and development is central to their practice of pathology.

“Not all pathologists do research,” says Saleeb. “But for me, I feel like I can help more patients if I can help find solutions to unsolved problems.”

Saleeb is currently completing a validation study on her classification system. She looks forward to implementing the system at St. Michael’s Hospital and broadening her research to study the molecular origins of kidney cancers and new kidney cancer prevention strategies.

Read more about OMPRN here or find current pathology funding opportunities here.