MIRA Clinical Learning Environment
(MIRA-CLE) for Lung
Co-PIs: Andrew Hope, Tony Tadic, Geoff Liu
Machine learning models can provide clinicians with additional insights about their patients, supporting clinical decision making at the point of care. Clinicians have access to all the same information they would normally possess but with the addition of specific information summarized from modeling hundreds or thousands of other similar cases. This easy access to comparison data and summarized patient information can provide added insight to clinicians about individual patients.
As part of MIRACLE, machine learning (ML) models have been created and tested along three aims, to highlight lung cancer patients who may be at higher risk of (1) inflammatory lung disease (ILD), (2) local failures, distant metastases and reduced overall survival based on tumour specific growth rate (SGR), and (3) treatment toxicities based on comparisons of cone beam CT (CBCT) images over time.
Being able to proactively identify patients who are prone to these risks through subtle clues available in a patient’s image, or by comparing and measuring features of a patient’s images over time, and then highlighting this information for radiation oncologists is expected to improve the care provided to our patients, as well as patient outcomes and patient safety.
- Model development, testing and validation completed or nearly completed for all three aims
- Pipeline integration and deployment into clinical tools completed for all three aims
- Web application created for researcher/clinician access to pipelines
- Quality Improvement (QI) application for clinical implementation approved
- Silent mode and prospective mode QI clinical implementation completed for ILD aim
- Stakeholder engagement with clinical site group on clinical acceptability for ILD aim
- Silent mode and prospective mode QI clinical implementation for SGR and CBCT aims
Hope, A. (2023, Feb 3) AI in Rad Onc (Canadian perspective) [Conference Presentation]. Canadian Lung Cancer Conference, Vancouver, Canada.
Hope, A. (2023, May 14) Interdisciplinary Best Paper: Prospective assessment of AI screening for interstitial lung disease (ILD) in radiotherapy [Conference Presentation]. Estro 2023 Conference, Vienna, Austria.
Kozak, M. (2023, May 29) Incorporating AI results in clinical workflows: A human factors perspective [Conference Presentation]. eHealth 2023 Conference, Toronto, Canada.