Transforming Cancer Care through Digital and Data-Driven Innovation

CDI Impact Report

Fiscal Year 2022–2023

A Message from our Leaders

We are proud of the achievements of the Cancer Digital Intelligence (CDI) team to advance research and innovation across the Princess Margaret Cancer Centre. In our first year of operation, we dedicated our work to reimagining the way we deliver cancer care by leveraging data science and analytics to improve patient outcomes and experiences. Some of our most impactful work this past year include:

੶ We designed and launched digital remote patient monitoring programs to enable access to care anywhere.

੶ We collaborated with clinical directors, managers, frontline staff, and patients to co-create a new digital triage tool to make it easier for patients to connect with their care teams.

੶ We advanced the field of radiation therapy by training artificial intelligence models to identify patients with underlying conditions and incorporating these findings into treatment plans.

੶ We launched digital tools to collect patient outcomes and improve our care delivery model.

੶ We built foundational data infrastructures that will fuel scientific discovery and improve patient outcomes.

੶ We empowered future leaders of cancer research by providing resources, expertise, and mentorship through the Spark Award.

੶ We inspired the data science community through collaborations with scientists, clinicians, and students to develop artificial intelligence models that will expedite cancer treatment planning to allow patients to receive radiation treatment sooner.

੶ We collaborated on a ground-breaking computational platform that matches patients to the best clinical trials using an unprecedented level of automation.

੶ We co-created the first automated early warning system at the Princess Margaret to address missed or delayed detection of undesirable cancer events to improve patient safety and quality of life.

੶ We built digital dashboards operations and flow across the Cancer Centre.

As we look forward, we will continue to find innovative ways to provide the best care to every patient at the Princess Margaret Cancer Centre.

Ale

& Ben

Alejandro Berlin Medical Director, CDI

Benjamin Haibe-Kains Scientific Director, CDI

Cancer Digital Intelligence

Who we are

Cancer Digital Intelligence (CDI) is a research and innovation program within the Princess Margaret Cancer Center that utilizes data and analytics to continuously improve care for people affected by cancer. Through close collaborations with patients, care teams, researchers, and external partners, CDI is able to advance the scale and application of digital discoveries to transform cancer care, education, and research and create the academic cancer centre of the future.

What we do

CDI’s program structure is built on a strong foundation in Data Science and Analytics that enable progress in three strategic domains: Discovery Integration, Care Innovation, and Business Intelligence.

Discovery Integration bridges the clinic and lab to enable the exchange of data, tools, and ideas that accelerate discovery.

Care Innovation uses digital innovation and service design to improve the continuum of care by streamlining the patient and provider experience and enabling access to care anytime, anywhere.

Business Intelligence harmonizes data and analytics to inform strategic decision-making and optimize care delivery.

Data Science and Analytics are the foundation of each strategic domain and provide the robust data infrastructure and deep expertise needed to advance novel applications of data science and artificial intelligence methods.

The Cancer Centre of the Future

CDI’s work over the next year will continue to advance and build expertise and capacity for large-scale data sharing that unlocks real-time and predictive analytics to guide organizational decision-making. This will include the opening of Canada’s first Cancer Command at the Princess Margaret Cancer Centre. Cancer Command will be a centralized system that curates and visualizes data collected across the cancer program to support informed decision-making, improve efficiency, and deliver the best cancer care. This large-scale initiative led by CDI will transform and optimize clinical workflows by combining deep clinical expertise with robust business intelligence using state-of-the-art data architecture, tools, and technology expertise.

Our Strategic Domains

Innovating Cancer Care through Remote Patient Monitoring

Enabling Access to Care Anywhere Using the My Bowels On Track (MBOT) App

The My Bowels On Track (MBOT) app was created to digitally optimize the Malignant Bowel Obstruction (MBO) Proactive Call Program in the Gynecological Cancer Clinic by monitoring and prioritizing calls to patients with serious symptoms. Patients utilized the app to report symptoms online and their responses automatically triaged based on severity.

The MBOT app increased the number of patients monitored and reduced nursing workload on proactive calls with 100% of nurses reporting that they would use an electronic monitoring service again. The app also included resources to teach, support and empower patients in the self-management of MBO symptoms. By collaborating with patients and providers (Dr. Stephanie Lheureux, Dr. Ainhoa Madariaga, and Nazlin Jivraj), CDI ensured that the app met the needs of the clinic and patients and was easy-to-use.

“I love that the app has many resources at your fingertips,” says a patient of the MBOT program. “It is a user-friendly app. I can contact my nurse and team very quickly and communicate with them through text. I love the quick access to my team.”

60

patients supported by the remote patient monitoring app, MBOT

Collaborative Co-Design Leads to Effective Remote Monitoring Program for Melanoma Patients

Actively monitoring patients on immunotherapy in the Melanoma/Skin Oncology (MSO) clinic is essential to avoid adverse events in patients and urgent hospital visits. To support the MSO clinic’s nurse-led proactive monitoring program, CDI collaborated with providers (Dr. Marcus Butler, Dr. Mauricio Ribeiro, and Nancy Gregorio) and patients to co-design a digital remote monitoring program (eIMBRASE). This app-based program makes it easier and more efficient for nurses to follow-up with patients, track symptoms, and prioritize in-person assessments for patients at risk of an adverse event.

In the eIMBRASE program, patients wear discrete biometric devices and complete scheduled electronic patient-reported outcomes (ePROs) questionnaires that are delivered directly to their mobile device of choice. The real-time biometrics and ePROs data are prioritized by an algorithm that accounts for the number of symptoms, changes in symptoms, and the symptom’s strength of association with the treatment type.

The algorithm generates alerts for care teams displayed on a clinical dashboard and designates a priority-oriented timeframe for contacting patients at risk of adverse events. To co-develop this prioritization algorithm, CDI conducted in-clinic shadowing, used sample case worksheets, and completed algorithm simulations to translate clinical practices and processes into the factors considered for prioritization.

Working closely with care providers, patients, and technology partners to co-design the user interfaces and the workflows, eIMBRASE illustrates the potential for digital health technologies to create a program that benefits both patients and providers. Following the design of the digital remote monitoring program, CDI is launching a pilot to assess its feasibility and measure its impact on patient and provider outcomes and satisfaction.

“eIMBRASE involves app-based ePROs that provide real-time measurements of patients’ health status, enabling clinicians to prioritize care, proactively manage adverse events related to immunotherapy, and ensure patient safety.”

Nancy Gregorio, Nurse

Designing Digital Solutions to Improve Cancer Care for Patients and Providers

In September 2022, CDI tackled the critical yet complicated telephone triage service that served as the main channel of communication for patients to connect with their care team while outside the Cancer Centre. CDI collaborated with clinical director Lesley Moody, managers Sim Kooner and Ana Bravo, frontline staff, and patients to cocreate a digital triage tool within the Lung/ Sarcoma clinics that acted as a single point of contact to fully capture patient issues and route patient concerns or questions to the appropriate responder quickly and efficiently.

This digital triage service greatly improves upon the previous telephone triage system that would receive approximately 100,000 triage voicemails per year across the Princes Margaret Cancer Centre and required tremendous amounts of manual work and follow-up to address each single voicemail. Digital triage systematically builds prioritization into the service and prompts patients to provide important details to get the best support.

82% of patients are satisfied with their experience using a digital triage tool as the main way to connect with their care teams

By collecting and recording patient questions and concerns upfront, CDI created an integrated database of patient requests that can be used to improve care by tracking patient outcomes and identifying trends in patient needs for future change initiatives. In addition, this database will allow clinics to make data-driven decisions about how to allocate resources to best orchestrate services and respond to the high volumes of requests.

With high patient and provider satisfaction, CDI plans to integrate the learnings from digital triage into Epic, the newly launched electronic Health Information System at UHN, and expand the service across all outpatient clinics at PM.

620 requests sent from patients using the digital triage tool

Dr. Andrew Hope Radiation Oncologist, PM

Improving Patient Care, Research and Discovery by Tracking Patient Outcomes

9,800 clinical notes with real-time patient outcomes collected

Through close collaborations with clinical site leads and championed by Dr. John Waldron (Head and Neck site group leader), CDI designed synoptic outcomes templates within Epic, UHN’s new electronic Health Information System launched in June 2022, to track real-time outcomes for patients with cancer.

CDI customized templates for each of the 14 disease sites across three specialties at the Princess Margaret Cancer Centre. These templates are user-friendly and allow providers to record patient outcomes in a consistent and retrievable format in Epic. By collecting this data across the Cancer Centre, the aim is to gain insights into patient outcomes and use data-driven insights to provide better care to patients. It creates a unique opportunity for future research initiatives and the development of care management guidelines.

Since launching in June 2022, over 9,800 clinical notes have synoptic outcomes collected, even before any targeted promotion effort around adoption, demonstrating a high level of provider interest in collecting patient outcomes.

14 templates customized over

3 disease sites

Applying Artificial Intelligence for Real-Time Early Detection of Undesirable Cancer Events

Cancer and its treatment can cause undesirable cancer events (UCEs) that occur frequently and significantly burden patients, their families, and hospitals. UCEs can include worsening symptoms or treatment toxicities (patient-reported), dangerous drops in blood cell counts, acute kidney injury, emergency department visits or hospitalizations, and increased risk of death. Detecting these UCEs early could reduce the frequency and severity of these events and improve patient safety and quality of life by alerting healthcare teams to intervene.

Under the leadership of Dr. Robert Grant, medical oncologist and researcher at the Princess Margaret Cancer Centre, CDI worked to overcome missed or delayed detection of UCEs by developing the first automated early warning system (AIM2REDUCE) at the Cancer Centre. AIM2REDUCE applied machine learning to detect UCEs using wide-ranging data from the electronic medical record (EMR), including treatment data, patient-reported outcomes, and laboratory test results.

AIM2REDUCE generated prediction models for 15 UCEs using historical, longitudinal population-level data from patients with gastrointestinal (GI), lung, and head and neck cancers. The results of these prediction models are novel and very promising. The next stage of this work includes evaluating model performance in the real world, particularly in the GI site group, as GI patients experience the highest rates of UCEs. CDI will safely explore the integration and benefits of early warning systems for UCEs at Princess Margaret, with the goal of deploying these systems in clinical practice, across cancer sites.

Example of how AI-model contouring (middle row) compare to human-generated contouring (top row) and the difference between both (bottom row). The results illustrate how well the AI-model matched the contouring process in cancer treatment planning (Benjamin Haibe-Kain’s Lab, 2022)

Applications of Artificial Intelligence in Oncology

Image segmentation to develop radiation treatment in head and neck cancers is a time-consuming, manual task taking several hours per patient. To tackle this issue, CDI partnered with the Vector Institute to launch an open competition to engage scientists, clinicians, and students to improve region of interest segmentation for radiation therapy.

The competition set out to explore the computational limitations of object detection and contouring with the goal of developing accurate auto-segmentation models in medical imaging. 11 teams of over 30 participants across UHN and the Vector Institute accepted this machine learning challenge and developed AI-assisted models by partnering clinical expertise with AI specialists.

“This was a successful challenge that targeted a wide variety of researcher, scientist, and student backgrounds within the UHN and Vector community,” said Ian Gormely, Communications Specialist at the Vector Institute. “We look forward to future CDI collaborations and AI-related projects to continue to help transform healthcare innovation.”

The first-place winner of the 2022 Machine Learning Challenge was the Fight Tumor team composed of Bo Wang (Lead), Jun Ma, Rex Ma, and Ronald Xie. Their winning model will ultimately improve workflow processes and create efficiencies that will allow patients to receive radiation treatment sooner. Their findings were presented at the 2022 Toronto Machine Learning Summit and will be published in an upcoming research paper. The data and computational models will also be made available to the biomedical community (Open Science).

11 teams formed

30 participants

2,700 images used to train autosegmenation models

“I thoroughly enjoyed the challenge and working with the team. The developed AI models hold immense potential to expedite the time-consuming and labor-intensive contouring process in cancer treatment planning.”

Jun Ma, Fight Tumor Team Member

Accelerating How Eligible Patients are Matched to Clinical Trials

Nearly 25% of patients diagnosed with cancer at the Princess Margaret Cancer Centre are eligible for clinical trials.

However, matching patients to trials is a highly manual and time-consuming process that can take anywhere between 2 hours and 4 weeks to complete – time that is essential for patient care.

CDI’s Grand Challenge winner, a team led by Dr. Trevor Pugh, aims to greatly reduce the time, effort, and resources required to match patients to clinical trials by using an unprecedented level of automation and scalability. The new computational platform, Clinical Trial Integrated Matching System (CTIMS), uses a “digital fingerprint” comprised of genomic, pathology and clinical data to match patients to the best clinical trials. By using digital innovation, CTIMS will:

੶ Easily create and validate clinical trial criteria in a secure and userfriendly form

੶ Add new clinical trial criteria to target increasingly specific details

੶ Automatically convert criteria into a clinical trial markup language (CTML)1 used to securely match patient data to clinical trial criteria

੶ View clinical trial matches alongside cancer genomic patient data

੶ Use existing clinical data standards such as those established by the Terry Fox Marathon of Hope Cancer Centre Network (MOHCCN) to align with other UHN and national initiatives

The CTIMS project is a collaboration between CDI and Dr. Trevor Pugh, Prasanna Kumar Jagannathan, Suzanne Trudel, Philippe Bedard, Benjamin Haibe-Kains, Marian Tang, Sophie Cooke, Anton Sukhovatkin, Mickey Ng, Adam Badzynski, and Sharon Narine.

1 Created by Dana Farber Cancer Institute & Harvard Medical School

What is the CDI Grand Challenge?

CDI’s annual Grand Challenge is an open call for applications to support bold, innovative, and high impact projects across the spectrum of cancer care. CTIMS is the winner of the 2022-2023 CDI Grand Challenge. The Grand Challenge competition offers support in front-end and back-end development, data science, design, and project management. Winning projects are selected based on their alignment with CDI priorities, contribution to the PM community, feasibility, and impact.

Empowering the Next Generation of Cancer Research Leaders

CDI recognizes the importance of supporting the next generation of cancer research leaders uncover new trends in their data to make bold cancer discoveries.

To enable future cancer innovations, CDI launched the Spark Award aimed at supporting basic science research trainees across the UHN community leverage the power of data science and machine learning expertise to advance their research.

For the 2022-2023 academic year, CDI awarded four exceptional research trainees with resources, expertise, and invaluable mentorship opportunities in data science, machine learning, and project management throughout the year. The next round of applications for the 2023-2024 CDI Spark Award open April 2023.

Tina Keshavarzian, PhD student

Research: Pinpointing regions of the DNA actively supporting advanced prostate cancer

“It is a source of pride for me that the institution I am affiliated with is actively in the process of recognizing the importance of integrating digital intelligence into healthcare systems. And that as an extension of this, is actively recognizing trainees that are pushing the limits of the applying innovative technologies in this space.”

Sasha Main, PhD student

Research: Using tumour DNA fragments in blood to uncover breast cancer biology.

“I had the privilege of participating in a CDI one-on-one session focused on project management, and since then, my approach to organizing and prioritizing projects has changed fundamentally. The educational webinars, in particular, sparked my imagination and inspired me to explore innovative ways of incorporating machine learning into my work. Additionally, I have been pleasantly surprised by the positive impact of my working relationships with other awardees. Their support and collaboration have been instrumental in advancing my project in ways I never anticipated.”

Derek Wong, Post-doctoral Research Fellow

Research: Utilizing machine learning to classify unique DNA fragments in the blood for early cancer detection.

“The one-on-one sessions have allowed me to further explore machine learning methods, preprocessing steps, and integration methods to better perform and understand the research that I am doing. The learnings thus far will be incorporated at all stages of my project.”

Emma Bell, Post-doctoral Research Fellow

Research: DNA methylation of cell-free DNA: A non-invasive method to detect ovarian cancer.

Early detection of ovarian cancer using cell-free methylated DNA immunoprecipitation sequencing as a potential diagnostic tool.

“I have found the CDI Spark Award valuable in not only helping fill in the gaps in my knowledge, but also giving me the courage to contact experts outside of CDI to pursue my career goals.”

Meet the CDI Team. Summer 2022.

Supporting the Advancement of Essential Oncology Software for Research within Princess Margaret

CDI recognizes that software is critical to modern scientific research, advancing medical discoveries while providing a framework for reproducibility and transparency.

However, many of these software solutions do not have dedicated funding for maintenance, let alone for growth and maximizing their potential for transformational impact. In a targeted effort to advance the use of essential oncology software for research, CDI launched an open call to fund and provide expertise to software-centered projects that are essential to biomedical research.

The open call resulted in the selection of three Princess Margaretdeveloped software projects for the 2022-2023 funding round: cBioPortal developed by the Dr. Trevor Pugh Lab; the Computational & Bench Scientist Ecosystem (CoBE) developed by the Dr. Mathieu Lupien Lab; and XevaDB developed by the Dr. Benjamin Haibe-Kains Lab.

Key Highlights

cBioPortal, developed by the Pugh Lab, is an open-source software platform that links clinical and genomic data from patients with cancer across the country. This software enables clinical and research teams to easily access genomic data, integrate clinical and genomic data, and analyze the data at both the patient and cohort levels.

Computational & Bench Scientist Ecosystem (CoBE), developed by the Lupien Lab, is a web portal recommendation engine that allows scientists to quickly find or contribute software tools and bioinformatics pipelines that address analytical needs from genomics data. CoBE packages software pipelines as fully reproducible code capsules that provide consistent, preloaded environments, allowing users to quickly run any software pipeline in the CoBE database with their own data. CoBE empowers users to share the capsules used in their own publications, ensuring reproducibility of their work.

XevaDB, developed by the BHK Lab, is a database with an intuitive web interface that allows clinicians and researchers to easily access patientderived xenografts of drug response and genomic profiles. XevaDB allows for concurrent visualizations of drug response and associated molecular data such as mutation, copy number alterations, and gene expressions.

CDI’s Impact in Numbers in its First Year of Operation

CDI TECHNOLOGIES

CDI TWITTER 10,000+ patients supported 90,000+ impressions 40,000+ profile visits

CDI WEBSITE

4,600 unique visitors 10,000+ page views

RESEARCH DATA STORAGE (RDS)

RDS is a standardized and secure way to store, share and manage data files generated by research equipment across UHN

CDI WEBSITE’S REACH GRANTS

15,000+ files shared 80 users 60 projects created

$840,000 awarded by the Canadian Institutes for Health Research to Dr. Benjamin Haibe-Kains, Dr. Alexandra Rink, Dr. Kathy Han, Dr. Mike Milosevic, Dr. Tony Tadic, and Dr. Robert Weersink over 5 years for their work on AI-assisted organ and target segmentation for cervix brachytherapy treatment planning.

$100,000

awarded by the Data Science Institute Catalyst Grant to Dr. Andrew Hope, Dr. Tony Tadic, and Dr. Chris McIntosh for their work on the MIRACLE project.

Publications & Conferences

Gouthamchand, V., Choudhury, A., Hoebers, F., Wesseling, F., Welch, M., Kim, S., Kazmierska, J., Dekker, A., Haibe-Kains, B., Soest, J., & Wee, L. (2023). FAIR-ification of structured Head and Neck Cancer clinical data for multi-institutional collaboration and federated learning. Research Square. Advance online publication. https://doi.org/10.21203/rs.3.rs-2705743/v1

Hope, A. (2023, February 2-3). AI in Radiation Oncology (Canadian Perspective). Canadian Lung Cancer Conference, Vancouver, BC, Canada. https://clcco.ca/agenda

Kozak, M., Hope, A., Tadic, T., Patel, T., Welch, M., Truong, T., McIntosh, C., & Kandel, S. (2023, May 28-30). Incorporating AI results in clinical workflows: A human factors perspective. [Conference presentation abstract]. eHealth 2023 Conference, Toronto, ON, Canada. https://www.e-healthconference.com/program/

Marsilla, J., Kim, J. W., Kim, S., Tkachuck, D., Rey-McIntyre, K., Patel, T., Tadic, T., Liu, F.-F., Bratman, S., Hope, A., & Haibe-Kains, B. (2022). Evaluating clinical acceptability of organ-at-risk segmentation in head & neck cancer using a compendium of open-source 3D convolutional neural networks. medRxiv. 
https://doi. org/10.1101/2022.01.15.22269276

Moskowitz, C.S., Welch, M.L., Jacobs, M.A., Kurland, B.F., & Simpson, A.L. (2022). Radiomic Analysis: Study Design, Statistical Analysis, and Other Bias Mitigation Strategies. Radiology, 304 (2), pp. 265-273. DOI: 10.1148/radiol.211597

Ribeiro, M., Gregorio, N., Somji, F., Melwani, S., Lovas, M., Macedo, A., Gray, D., Singh, R., Giovannetti, E., Lee, S., Chong, S., Berlin, A., Saibil, S., Spreafico, A., Hogg, D., & Butler, M. (2022). Development of a remote monitoring program for melanoma/skin oncology patients at Princess Margaret Cancer Centre. Journal of Clinical Oncology, 40(16). DOI: 10.1200/JCO.2022.40.16_suppl.e18630

Safavi, A.H., Lovas, M., Liu, Z.A., Melwani, S., Truong, T., Devonish, S., Abdelmutti, N., Sayani, A., Rodin, D., & Berlin, A. (2022). Virtual Care and Electronic Patient Communication During COVID-19: Cross-sectional Study of Inequities Across a Canadian Tertiary Cancer Center. J Med Internet Res., 24 (11).

DOI: 10.2196/39728. PMID: 36331536; PMCID: PMC9640204.

Somji, F., Gregorio, N., Ribeiro, M., Melwani, S., Dozois, G., Lee, S., Taiwo, A., Lovas, M., Macedo, A., Giovannetti, E., Berlin, A., Saibil, S., Spreafico, A., & Butler, M. (2023). Level up remote care with biometric devices, ePROs and real-time dashboards. [Conference poster presentation]. 17th Canadian Melanoma Conference, Banff, AB, Canada.

Suleman A, Vijenthira A, Liu ZA, Truong T, Berlin A, Prica A, Rodin D. (2023). Virtual Care During the COVID-19 Pandemic for Patients with Hematologic Malignancies: A Single-Institution Experience. JCO Oncol Pract.

DOI: 10.1200/OP.22.00690. Epub ahead of print. PMID: 36821811.

[In press]. Multi-institutional prognostic modelling in head and neck cancer: Evaluating impact and generalizability of deep learning and radiomics. Cancer Research Communications.

CDI Governance

Leadership Team

Alejandro Berlin, Medical Director

Benjamin Haibe-Kains, Scientific Director

Luke Brzozowski, Partnership Lead

Tran Truong, Director, Data & Technology

Kelly Lane, Director of Operations & Projects

Mike Lovas, Director of Design & Innovation

Tony Tadic, Imaging Platform Lead

Executive Oversight Commitee

Keith Stewart, VP, Cancer, and Director of Princess Margaret Cancer Program

Aaron Schimmer, Senior Scientist

CDI Leadership Team

CDI Advisory Committee

Clinical Practice and Operations

Lesley Moody, Clinical Director

Anet Julius, Professional Practice

Monika Krzyzanowska, Chair of Clinical Practice & Quality

Clinician Researchers

John De Almeida, Clinician Investigator (Surgery)

Camilla Zimmermann, Senior Scientist (Supportive Care)

Robert Grant, Clinician scientist (DMOH)

Basic Researchers

Mathieu Lupien, Senior Scientist

Hansen He, Senior Scientist

Trevor Pugh, Senior Scientist

Education

Meredith Giuliani, Director of Cancer Education

Patient & Caregiver Partners

Elizabeth Scott, Caregiver Partner

Justin Aling, Patient Partner

UHN Digital and Other Partners

Michael Brudno, Chief Data Scientist

Asif Saleh, UHN Digital

Mary Gospodarowicz, Advisor