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Études médicales de premier cycle - EMPC

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Nombre de bourses offertes à la Faculté de médecine

372 135

Somme qui a été remise en bourses à l’automne 2023 aux étudiants en médecine de la première à la quatrième année

198

Nombre de récipiendaires à l’automne 2023

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Bourses offertes à la Faculté de médecine

Nous remercions tous les donateurs de leurs contributions. Leur soutien aidera nos étudiants, la prochaine génération de médecins, à bâtir un monde plus sain. Grâce à eux, il nous est possible de proposer plusieurs bourses à la Faculté de médecine.

Objectif du fonds

Offrir une bourse à des étudiantes et étudiants dans le programme de MD qui travaillent sur un projet au Département de radiologie pendant l’été.

Admissibilité

La personne candidate doit:

être citoyenne canadienne, résidente permanente, personne à statut protégé/réfugié ou étudiante internationale;
être inscrite à temps plein au premier cycle à la Faculté de médecine de l’Université d’Ottawa; et
avoir l’intention de participer à un projet de recherche d’été (exigeant un minimum de 8 semaines de travail à 37,5 heures par semaine) au Département de radiologie.

Note : L’horaire de travail sera décidé par la personne récipiendaire, le chercheur ou la chercheuse et le Département.

Valeur de la bourse : 4 000 $ (allouée en trois paiements : 1 000 $ au début du placement, 1 500 $ à la moitié du placement, et 1 500 $ à la fin du placement)

Nombre de bourses : 3

Fréquence d’attribution : Annuelle

Échéance des demandes : 31 mars

Demande:

La demande doit être soumise par l’entremise de Bourses en ligne, accessible une fois connecté au portail uoZone, et doit comprendre:

le Curriculum vitae sur Bourses en ligne;
une copie non-officielle du relevé de notes de la personne candidate;
un courriel adressé à [email protected] dans lequel la personne candidate doit détailler sa motivation pour participer à cette opportunité, ses intérêts de recherche et son classement des propositions de recherche en fonction de son propre intérêt.

Note : En acceptant cette bourse, vous acceptez de renoncer à tout autre stage optionnel ou projet de recherche pendant la période couverte par la bourse. Cette condition est essentielle afin d’assurer votre engagement total et votre disponibilité pour le programme financé par la bourse.

Propositions de recherche du Département d’imagerie médicale pour 2025 :

Note : les descriptifs sont disponibles en anglais seulement.

Description:

Risk assessment is a key tool for personalized screening and early detection of breast cancer. In recent work¹, the MIT (Massachusetts Institute of Technology) team developed deep learning models that assess cancer risk from full-resolution screening mammograms and found them to be significantly more accurate than the Tyrer-Cuzick model². At The Ottawa Hospital, a retrospective study of 10,000 screening 2D mammograms found the MIT tool (MIRAI) to be highly effective at predicting breast cancer in women. Digital breast tomosynthesis (DBT) mammography is now being used more frequently for screening but MIRAI has not yet been validated for this modality. The primary aim of this study is the measure the discriminative performance of the developed risk models on the population of women who are screened with DBT.

Hypothesis:

Similar to 2D mammography, women screened with DBT with the top risk decile by the Hybrid DL model will have a higher cancer detection rate than women in the lower risk decile, thus better predicting breast cancer development and risk assessment than currently exists.

Specific Objective(s):

To evaluate all women screened with DBT from 2015-2024 and diagnosed with breast cancer at the Ottawa Hospital to determine how well the Hybrid DL model performed in prediction of breast cancer.

Brief Methods:

To measure the accuracy of each risk model, we will measure the concordance index of the predictions with the provided outcomes on the retrospective data as well at the ROC AUCs at one to five years from the time of the mammogram. To measure the simulated performance of risk-based screening, we will measure relative screening volume under simulated screening guidelines as well as shifts in diagnosis time. We will assess confidence intervals using a clustered-bootstrap procedure. To facilitate this work, we will collect the risk assessment and outcomes for each anonymized patient ID, and this data will be sent to MIT server onsite at The Ottawa Hospital.

Student skills required:

The student will be required to review patients in EPIC, have Excel and Word skills, be able to review using PACS, and collaborate with the research staff to complete tasks. Prior research collaboration and skills would be an asset.

Potential skills developed:

The student would learn about the imaging for cancer detection, the performance indicators for high quality work in breast imaging, the importance of Artificial intelligence in Radiology, as well as joining a collaborative team of researchers dedicated to early detection of breast cancer.

Timeline:

The project would begin May 1, 2025, and completed by August 31, 2025.

References:

Yala A, Lehman C, Schuster T, Portnoi T, Barzilay R. A Deep Learning Mammography-based Model for Improved Breast Cancer Risk Prediction. Radiology. 2019;292:60-66. doi: 10.1148/radiol.2019182716
Yala A, Mikhael PG, Strand F, Lin G, Satuluru S, Kim T, Banerjee I, Gichoya J, Trivedi H, Lehman CD, et al. Multi-Institutional Validation of a Mammography-Based Breast Cancer Risk Model. J Clin Oncol. 2022;40:1732-1740. doi: 10.1200/jco.21.01337

Description:

Women with breast cancer detected between the ages of 40-49 have been found to have more advanced stages of breast cancer than older women in Canada¹. Screening policies that include women in the 40s has been found associated with lower stage of breast cancer diagnosis in women ages 40-49 and 50-59¹. Canadian breast cancer screening guidelines recommend screening only starting at age 50² but recent screening policy changes allow self-referral for women ages 40-49 in the Ontario Breast Screening program. The impact of the changes has not been evaluated.

Hypothesis:

More women with screen-detected breast cancer will be found in women ages 40- 59 after the screening program changed to include women in the 40s, and more patients will be detected at lower stage (stage 0 or stage I).

Specific Objective(s):

To determine the difference in the method of detection and stage of breast cancer before and after the program policy changes were implemented.

Brief Methods:

Using EPIC, all consecutive women diagnosed with breast cancer between October 1/2023-October 1/2024 will be compared to October 8/2024-May 31/2025, after the policy changed. The method of detection, stage at diagnosis, molecular subtype, and treatment will be determined.

Student skills required:

Potential skills developed:

The student would learn about the imaging for cancer detection, method of detection, the performance indicators for high quality work in breast imaging, as well as joining a collaborative team of researchers dedicated to early detection of breast cancer.

Timeline:

The project would begin May 1, 2025, and completed by August 31, 2025.

References:

Wilkinson AN, Billette JM, Ellison LF, Killip MA, Islam N, Seely JM. The Impact of Organised Screening Programs on Breast Cancer Stage at Diagnosis for Canadian Women Aged 40-49 and 50-59. Curr Oncol. 2022;29:5627-5643. doi: 10.3390/curroncol29080444
Klarenbach S, Sims-Jones N, Lewin G, Singh H, Theriault G, Tonelli M, Doull M, Courage S, Garcia AJ, Thombs BD, et al. Recommendations on screening for breast cancer in women aged 40-74 years who are not at increased risk for breast cancer. CMAJ. 2018;190:E1441-E1451. doi: 10.1503/cmaj.180463

Description:

Risk assessment is a key tool for personalized screening and early detection of breast cancer. In recent work¹, the Massachusetts Institute of Technology (MIT) team developed deep learning models (MIRAI) that assess cancer risk from full-resolution screening mammograms and found them to be significantly more accurate than the Tyrer-Cuzick (TC) model². The MIRAI tool has not been well assessed high-risk patients screened with MRI and mammography. The purpose of this study is to compare the performance of MIRAI to that of TC in high-risk patients.

Hypothesis:

Women in the high-risk program with the top risk decile by the Hybrid DL model will have a higher cancer detection rate than women in the lower risk decile, thus better predicting breast cancer development and risk assessment than currently exists.

Specific Objective(s):

To assess screening MRI performance metrics including cancer detection rate, sensitivity, and specificity in those identified as increased risk by MIRAI vs TC.

Brief Methods:

A database of high-risk patients who underwent annual screening MRI with the Ontario Breast Screening Program (OBSP) between 06/01/2019 to 12/31/2022, with one year follow-up data will be accessed. Demographic information, TC scores, information from the MRI and mammography reports, and biopsy and surgical pathology results will be accessed using EPIC.

Artificial Intelligence Scores: Screening mammograms will be processed through MIRAI through an onsite server at The Ottawa Hospital. MIRAI scores from bilateral screening mammograms from patients who underwent high-risk screening MRI after normal mammography will be obtained.

Student skills required:

Potential skills developed:

Timeline:

The project would begin May 1, 2025, and completed by August 31, 2025.

References:

Yala A, Lehman C, Schuster T, Portnoi T, Barzilay R. A Deep Learning Mammography-based Model for Improved Breast Cancer Risk Prediction. Radiology. 2019;292:60-66. doi: 10.1148/radiol.2019182716
Yala A, Mikhael PG, Strand F, Lin G, Satuluru S, Kim T, Banerjee I, Gichoya J, Trivedi H, Lehman CD, et al. Multi-Institutional Validation of a Mammography-Based Breast Cancer Risk Model. J Clin Oncol. 2022;40:1732-1740. doi: 10.1200/jco.21.01337

Description:

This study aims to retrospectively evaluate the performance of RapidAI in stroke detection at The Ottawa Hospital between January 2022 and January 2025. RapidAI is an AI-enabled tool designed to assist in identifying large-vessel occlusions (LVO) and intracranial hemorrhages (ICH) on CT scans. Despite its adoption, gaps remain in understanding its real-world impact on diagnostic accuracy, workflow efficiency, time-to-intervention metrics, and patient outcomes. This project will provide critical data to assess RapidAI's effectiveness and guide its optimization for clinical practice.

Hypothesis:

We hypothesize that RapidAI implementation has improved stroke detection accuracy, reduced time-to-intervention, and enhanced workflow efficiency, leading to better patient outcomes. However, potential disparities in performance, safety (false positives/negatives), and operational challenges require further investigation.

Specific Objective(s):

Evaluate the diagnostic accuracy of RapidAI in detecting LVO and ICH compared to clinician interpretation. Assess time-to-intervention metrics (e.g., door-to-needle, door-to-groin puncture times) pre- and post-RapidAI implementation. Analyze clinical outcomes, including functional status (mRS), hospital length of stay, and mortality rates.

Brief Methods:

Sensitivity, specificity, and predictive values of RapidAI vs. radiologist interpretation. Statistical comparison of time-to-intervention metrics before and after RapidAI deployment. Multivariate regression models to assess the impact of RapidAI on patient outcomes.

Student skills required:

Basic knowledge of medical imaging. Interest in AI applications in healthcare. Basic statistical analysis skills. Strong analytical and problem-solving skills.

Potential skills developed:

Hands-on experience in retrospective clinical research. Understanding of AI's role in medical imaging and stroke care. Exposure to data analysis, critical appraisal of AI-based tools, and scientific writing. Mentorship in neuroimaging research and ethical considerations in AI deployment.

Timeline:

6 months. Active data collection during summer.

References:

Saver JL. Time is brain—quantified. Stroke. 2006;37(1):263-266. doi:10.1161/01.STR.0000196957.55928.ab
Albers GW, Marks MP, Kemp S, Christensen S, Tsai JP, Ortega-Gutierrez S, et al. Thrombectomy for stroke at 6 to 16 hours with selection by perfusion imaging. N Engl J Med. 2018;378(8):708-718. doi:10.1056/NEJMoa1713973
Goyal M, Ospel JM, Jayaraman M, Fiehler J, Oppenheim C, Menon BK. Evaluating the role of imaging software in clot-based stroke trials and daily practice: Can it substitute for expertise and training? AJNR Am J Neuroradiol. 2022;43(4):555-561. doi:10.3174/ajnr.A7466
Fasen BACM, Scheenen TWJ, Regt M, Jansen I, Van Zwam WH, Coutinho JM, et al. The added value of artificial intelligence software for radiological review of acute stroke imaging: A systematic review of current evidence. J Neurol. 2023;270(5):2124-2134. doi:10.1007/s00415-023-11606-2
Canada’s Drug Agency (CDA-AMC). RapidAI for stroke detection: Main report. Health Technology Review. November 2024. Available at: https://www.cda-amc.ca/rapidai-stroke-detection

Description:

Have you ever undergone an MRI scan? It’s extremely noisy! MRI machines generate loud acoustic noise, often reaching levels as high as 120 dBA, which is comparable to the sound of a rock concert or a jet engine at takeoff. This noise is primarily caused by the rapid switching of magnetic field gradients, which induce vibrations in the MRI scanner's components. Various strategies are employed to mitigate this issue, including the use of sound-dampening materials, MRI-compatible headphones, and advanced noise-reduction sequences. However, finding an effective balance between noise reduction and maintaining high-quality imaging remains an ongoing challenge in the field of radiology. Our project will use many of these suggested strategies and specific audio masking techniques using music to improve patient tolerance and experience. Playing music during MRI scans significantly enhances patient experience by reducing anxiety, improving comfort, and making the procedure seem shorter. To achieve effective noise masking, audio playback will be synchronized with MRI sequence timing via an active optical trigger signal from the MRI machine. Patients can select from music, nature sounds, or guided meditations tailored to their preferences, with dynamic audio adjustments based on MRI noise levels. Patients who are less anxious and more comfortable are more likely to comply with scan protocols, reducing the need for repeat scans.

Hypothesis:

The targeted audio masking technique for MRI noise reduction will enhance patient experience, decrease repeat scans, and minimize movement artifacts.

Brief Methods:

Implement real-time noise monitoring and evaluate quiet MRI technology through sequence modifications; record noise from common MRI scans to align with music or natural sounds (such as oceans waves) for active masking of scanner noise. Patient feedback using PROMs & VAS.

Student skills required:

Understanding musical theory, musical skill and audio masking principles will be advantageous.

Potential skills developed:

Work directly with advanced MRI technology and noise reduction techniques under supervision of MRI physicist. Gain experience in data collection, analysis, patient-centered research and scientific writing.

Timeline:

Summer months

References:

Fülkell, P. (2020). Sequenzbasierte Geräuschreduktion in kranialer MRT-Bildgebung und deren Einfluss auf die Bildqualität [Doctoral dissertation, Universitätsmedizin der Universität Greifswald]. Retrieved from Universität Greifswald.
Knörgen, M., Spielmann, R. P., Haberland, E.-J., & Melkus, G. (2004). Schalldruckpegelmessungen an einer MRT-Anlage. Z. Med. Phys., 14(4), 251-259. Retrieved from Elsevier.
Crossley, E., Biggs, T., Brown, P., & Singh, T. (2021). The Accuracy of iPhone Applications to Monitor Environmental Noise Levels. The Laryngoscope, 131(1), E59–E62. Retrieved from The Laryngoscope.
Smith, J., Brown, L., & Johnson, P. (2018). The effect of music on anxiety and discomfort during MRI scanning. Journal of Magnetic Resonance Imaging. Retrieved from Journal of Magnetic Resonance Imaging.
Müller, R., Schneider, M., & Hofmann, B. (2019). Music reduces perceived duration and discomfort during MRI scans. European Journal of Radiology. Retrieved from European Journal of Radiology.
Radiology Today. (2020, May). The Therapeutic Role of Music in Radiology. Retrieved from Radiology Today.
Lee, S., Thompson, K., & Williams, D. (2021). Music in the MRI suite: Patient experience and outcomes. American Journal of Roentgenology. Retrieved from American Journal of Roentgenology.
Healthline. (2022, July). How Music Therapy Can Improve Medical Imaging Experiences. Retrieved from Healthline.

Description:

Breast infection (mastitis) is one of the most common reasons for referral from the Emergency Department (ED) for breast imaging assessment^1-4. Although mastitis is common, breast abscess is not, occurring in 3%^1-4 of all patients with mastitis, however these usually require percutaneous drainage to resolve the infection. The breast health center receives a variable number of these requests each week (0-10 requests/week) and abscess drainage is performed in only a small percentage of these consults, which detracts from an already limited resource pool.

Hypothesis:

Duration of symptom onset and the presence of a palpable mass at presentation are the strongest predictors of the need for percutaneous abscess drainage in women presenting with symptoms of mastitis.  

Specific Objective(s):

Determine the number of patients who require abscess drainage at the time of referral from the ED (rate of intervention).
Determine which clinical and laboratory indicators are predictive of the need for intervention.
Develop a clinical decision-making tool for implementation in the ED that will aid in triage of patients, stratifying those that can be managed conservatively with antibiotics and versus those who require urgent referral for consideration of breast aspiration.

Brief Methods:

A retrospective chart review of all patients referred from the ED as part of the breast abscess pathway between September 1, 2019, and August 31, 2024, will be conducted. For each patient, clinical notes, laboratory investigations, imaging and microbiology/pathology results will be reviewed, and various metrics will be collected and compiled in excel for analysis.

Student skills required:

Basic excel skills, an understanding of the scientific method, ability to conduct a literature review and an interest in publication.

Potential skills developed:

Opportunity to work with staff radiologists, gain a better understanding of ED workflow and the scientific method including manuscript creation and publication of scientific journal articles.   

Timeline:

Data collection completed by Aug 2025, Data analysis/manuscript preparation Fall 2025.

References:

Mahoney MC, Ingram AD. Breast Emergencies: Types, Imaging Features, and Management. American Journal of Roentgenology. 2014;202(4):W390-W399. doi:https://doi.org/10.2214/ajr.13.11758
Hines, N., Leibman, A.J. & David, M. Breast problems presenting in the emergency room. Emerg Radiol 14, 23–28 (2007). https://doi.org/10.1007/s10140-007-0575-3
Ammann AM, Pratt CG, Lewis JD, Ahmad SA, Shaughnessy E, Heelan AA. Breast infections: A review of current literature. The American Journal of Surgery. Published online October 1, 2023. doi:https://doi.org/10.1016/j.amjsurg.2023.10.040
Gollapalli V, Liao J, Dudakovic A, Sugg SL, Scott-Conner CEH, Weigel RJ. Risk Factors for Development and Recurrence of Primary Breast Abscesses. Journal of the American College of Surgeons. 2010;211(1):41-48. doi:https://doi.org/10.1016/j.jamcollsurg.2010.04.007

Description:

This research focuses on the long-term angiographic outcomes and complications following the use of flow diverters for the treatment of intracranial aneurysms. Flow diverters have emerged as a promising alternative to traditional endovascular coiling, offering a more durable solution for aneurysm occlusion by promoting endothelialization and vessel remodeling^1,2. However, their long-term efficacy, risk of delayed complications, and patient-specific factors influencing outcomes remain areas of active investigation. This study aims to identify risk factors for angiographic recurrence and procedure-related complications in patients treated with flow diverters.

Hypothesis:

We hypothesize that angiographic recurrences and procedure-related complications after flow diverter treatment occur more frequently in specific patient populations, including elderly patient, large or wide-neck aneurysms, and aneurysms with suboptimal initial characteristics.

Specific Objective(s):

Determine the incidence of angiographic recurrence and delayed complications following flow diverter treatments. Identify patient and aneurysm characteristics associated with poor long-term outcomes. Evaluate safety profile to other endovascular techniques.

Brief Methods:

Data will be collected from patient charts in EPIC, including all patients that meet inclusion criteria from January 1, 2012, to October 21, 2024. Data collection will include patient demographics, aneurysm characteristics, and other relevant details.

Student skills required:

Excellent understanding of neurovascular anatomy. Strong research and critical thinking skills. Collaborative skills for working with a multidisciplinary team.

Potential skills developed:

Exposure to advanced neurovascular research. Hands-on experience with clinical data collection and statistical analysis. Training in angiographic interpretation and image analysis. Involvement in publications and conference presentations

Timeline:

April to August 2025: retrospective data collection, statistical analysis, and manuscript preparation and submission for publication

References:

Ravindran K, Casabella AM, Cebral J, Brinjikji W, Kallmes DF, Kadirvel R. Mechanism of Action and Biology of Flow Diverters in the Treatment of Intracranial Aneurysms. Neurosurgery. 2020 Jan 1;86(Suppl 1):S13-S19. doi: 10.1093/neuros/nyz324. PMID: 31838528; PMCID: PMC6911734.
Gaub M, Murtha G, Lafuente M, Webb M, Luo A, Birnbaum LA, Mascitelli JR, Al Saiegh F. Flow Diversion for Endovascular Treatment of Intracranial Aneurysms: Past, Present, and Future Directions. J Clin Med. 2024 Jul 16;13(14):4167. doi: 10.3390/jcm13144167. PMID: 39064207; PMCID: PMC11278297.

Description:

This research focuses on the long-term angiographic outcomes and complications following the use of Woven EndoBridge (WEB) and other intrasaccular devices for the treatment of intracranial aneurysms. These devices have been developed as an alternative to coiling and flow diversion, particularly for wide-neck bifurcation aneurysms, aiming to improve occlusion rates while reducing complications^1,2. However, questions remain about their long-term efficacy, rates of recurrence, and safety profile compared to other endovascular techniques. This study aims to evaluate angiographic recurrence rates, device-related complications, and patient-specific risk factors influencing long-term outcomes.

Hypothesis:

We hypothesize that angiographic recurrences and procedure-related complications after WEB and other intrasaccular devices more frequently in specific patient populations, including elderly patient, large or wide-neck aneurysms, and aneurysms with suboptimal initial characteristics

Specific Objective(s):

Determine the incidence of angiographic recurrence and delayed complications following WEB treatment. Identify patient and aneurysm characteristics associated with poor long-term outcomes. Evaluate the safety profile to other endovascular techniques.

Brief Methods:

Student skills required:

Excellent understanding of neurovascular anatomy. Strong research and critical thinking skills. Collaborative skills for working with a multidisciplinary team

Potential skills developed:

Timeline:

April to August 2025: retrospective data collection, statistical analysis, and manuscript preparation and submission for publication

References:

Arthur AS, Molyneux A, Coon AL for the WEB-IT Study investigators, et alThe safety and effectiveness of the Woven EndoBridge (WEB) system for the treatment of wide-necked bifurcation aneurysms: final 12-month results of the pivotal WEB Intrasaccular Therapy (WEB-IT) StudyJournal of NeuroInterventional Surgery 2019;11:924-930.
Hassankhani A, Ghozy S, Amoukhteh M, Bilgin C, Kadirvel R, Kallmes DF. Long-term outcomes of the Woven EndoBridge device for treatment of intracranial aneurysms: A systematic review and meta-analysis. Interv Neuroradiol. 2023 Jun 26:15910199231184524. doi: 10.1177/15910199231184524. Epub ahead of print. PMID: 37357734.

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Bourses offertes à la Faculté de médecine

Propositions de recherche du Département d’imagerie médicale pour 2025 :

Using Digital Breast Tomosynthesis Mammographic Images in Conjunction with Other Risk Factors to determine the Risk of Breast Cancer arrow_drop_down

Description:

Hypothesis:

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Potential skills developed:

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References:

Method of Detection of Breast Cancer among Women 40-59 years – Impact of Screening Policy Changes to Include Women age 40-49 arrow_drop_down

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To Compare Risk Assessment usinMammographic Images in Conjunction with Other Risk Factors among High-Risk Women arrow_drop_down

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Real-World Performance of RapidAI in Stroke Detection: A Retrospective Institutional Review arrow_drop_down

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Music in MRI arrow_drop_down

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Predictors of the need for percutaneous intervention in emergency patients with breast infection; a 5-year retrospective review arrow_drop_down

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Evaluating Long-Term Angiographic Outcomes and Complications of Flow Diverters for Intracranial Aneurysms arrow_drop_down

Description:

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Long-Term Angiographic Outcomes and Complications of WEB and Intrasaccular Devices in Intracranial Aneurysm Treatment arrow_drop_down

Description:

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Fonds de conférences médicales étudiantes de la Société d’Esculape arrow_drop_down

Bourse Amanda Kelsall arrow_drop_down