For Researchers

CanCOLD Data

With longitudinal evaluations and detailed characterization of study subjects using a range of assessment tools, CanCOLD is an invaluable resource to catalyze COPD research.

Training Opportunity

CanCOLD Training Opportunities serves to train future generations of clinicians, researchers, students and qualified research staff. Below, we showcase our future; our outstanding research trainees who made use of CanCOLD data to advance knowledge on COPD.

To inquire about training opportunities, please contact one of the CanCOLD site principal investigators.

“The CanCOLD study has supplemented my thesis with robust real-world data as well as developed my problem-solving skills and my ability to articulate complex concepts.”

Tahlil Ahmed Parisa

Masters student

McGill University

Sukhraj Virdee

PhD candidate

Toronto Metropolitain Univerisity (TMU)

“The extensive clinical data provided by CanCOLD has bolstered my skills in handling large datasets to address clinically significant inquiries concerning lung disease.”

Witnessing the existing challenges in the Bangladesh healthcare system compelled Tahlil to pursue her studies in Epidemiology under Dr Jean Bourbeau‘s supervision. Through clinical research on COPD patients, she has been able to associate an increase in Blood Eosinophil Counts (BEC), a type of white blood cell, with an increased risk of COPD exacerbation or flare-ups. This result thereby highlights the possible usefulness of BEC as a predictor of COPD exacerbation and its possible use in administering earlier preventive therapy. With an ambition to become a data scientist, Tahlil aims to harness big data to advance clinical research, particularly on underserved populations.

“I’m interested in creating quantitative CT biomarkers […] to predict disease progression in individuals who have COPD”, says Sukhraj, a Medical Physics student in Dr. Miranda Kirby’s lab. His research introduces the novel Normalized Join-Count (NJC) method, which shows potential as a more accurate CT biomarker compared to current approaches. By evaluating the compactness & shape of emphysema clusters, the NJC effectively distinguishes the severity of COPD in patients. Sukhraj aspires to a career in medical physics to make a positive impact on those afflicted with various medical conditions.

“The multi-center nature of CanCOLD has introduced me to a wide array of collaborators from across Canada, and permitted me to form meaningful relationships wto expand my research network.”

Daniel Genkin

PhD candidate

Toronto Metropolitain Univerisity (TMU)

Sophie Collins

PhD candidate

Univerisity of Alberta

“Using the CanCOLD dataset has not only improved my understanding of COPD but has also allowed me to use epidemiological data to address physiological question, a truly unique opportunity!”

Daniel is a Biomedical Engineering trainee in Dr Miranda Kirby’s lab who is
passionate about extrapulmonary conditions that manifest as a result of living
with COPD. He currently focuses on the involuntary loss of muscle mass (sarcopenia) in the chest area and successfully 
developed a fully automated
process using a low-dose CT Scan to more easily collect quantitative CT
measurements from the pectoralis muscles area (PMA)
. PMA results accurately provide insights on lung function and can distinguish healthy and COPD patients. In the future, Daniel will use his skills to either continue on an academic (post-doctoral) or industry path.

Sophie is studying Rehabilitation Sciences under Dr Michael Stickland. Her latest work suggests that the lower exercise capacity experienced by people living with COPD is associated with pulmonary vascular dysfunction/destruction. Her findings suggest that increased exercise capacity may have protective effects against the progression of pulmonary vascular dysfunction/destruction in COPD. Following her PhD, Sophie plans to expand her knowledge and skills on clinical physiology through post-doctoral studies.

“CanCOLD has provided me with invaluable skills in statistical analysis, physiological interpretation of data and an overall undestanding of how to take a scientific question from its begginings to its final stages.”

Alexandra McCartney

Medical student

Univerisity of Ottawa

Kalysta Makimoto

PhD candidate

Toronto Metropolitain Univerisity (TMU)

“The supportive nature of the group and continued interest in respiratory physiology has made this work continually rewarding!” says Alexandra as she describes her experience in Dr Nicolle Domnik’s lab. Alexandra is currently studying the relationship between chronic sleep impairment and exercise capacity in older adults with and without COPD and aims to contribute to further detailing the mechanism that exists between the two. In the future, she will continue her involvement in applied physiology and epidemiological research as a physician.


Currently studying Biomedical Physics in Dr Miranda Kirby’s lab, Kalysta’s research interests center on quantitative CT image analysis and machine learning, a combination of methods that can identify structural abnormalities in the lungs and help classify COPD cases. Her current work, focused on at risk undiagnosed COPD patients, brilliantly shows the ability of CT imaging analysis to improve prediction of disease progression and lung function decline when combined with machine learning and patient demographic data. Passionate about research, Kalysta intends to continue her path in science by undergoing post-doctoral studies.


“My work using [the CanCOLD dataset] has led me to start projects with other cohorts in the USA and UK which help advance understanding of the relationship between COPD and cardiovascular disease.”

Suurya Krishnan

PhD candidate

McGill Univerisity

Meghan Koo

PhD candidate

Toronto Metropolitain Univerisity (TMU)

Suurya, studies in the Division of Experimental Medicine under Dr Jean Bourbeau‘s supervision where he explores the relationship between COPD and cardiovascular disease (CVD). His work is uncovering (1) that individuals with mild to moderate impaired lung function, both with and without COPD, face an increased risk of existing or future CVD, and (2) that adding impaired lung function to established CVD risk scores does not enhance the CVD prediction. This underlines the need to develop a CVD assessment tool tailored to individuals with impaired lung function that considers COPD-related factors beyond lung function, helping physicians target high-risk patients for early intervention. Suurya wishes to practice family medicine in the future.



As a CAMPEP Medical Physics student supervised by Dr Miranda Kirby, Meghan is deeply involved in quantitative CT lung imaging. Her work showed that integrating multiple quantitative CT measurements with CT visual emphysema scores was more effective/comprehensive than solely relying on the visual scores when predicting baseline and lung function decline in COPD patients. These results can help identify reliable biomarkers for early diagnosis and management of COPD in clinical settings. Meghan plans to pursue a CAMPEP-accredited residency in therapeutic medical physics.




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