MIRA Clinical Learning Environment (MIRACLE): Lung

Overview

The goal of this quality improvement (QI) study is to develop automated clinical pipelines to implement machine learning models in the care pathway of lung cancer patients. The main questions it aims to answer are: * Can model-prompted risk classifications be incorporated into clinician workflows to enable informed clinical decision-making? * What are clinicians' perceptions of the information from model outputs, and do they change their decision about data already available to them as a result of the model-prompted risk classification (i.e., to re-review or further assess patients identified by the models as being higher risk)? Participating radiation oncologists will receive the risk prediction from the model and be asked to complete a survey to give feedback on how they used the prediction in their decision-making.

Novel data science and imaging-based methods to personalize care are being identified retrospectively and explored at many centers. Unfortunately, most of these methods require significant manual intervention to apply to any given patient situation and are difficult to deploy in a timely fashion to affect patient treatment decisions. Clinical implementation of data science research will require automated pipelines that are tied into the entire treatment pathway in ways that facilitate real-time data analysis and enable translational research. The current process for clinical/translational researchers within Princess Margaret Hospital (PM)/University Health Network (UHN) to analyze imaging data involves extensive manual curation consisting of interactions with electronic databases and analysis tools to: identify patients with imaging data; collect that data; delineate targets of interest manually (minutes-to-hours per patient); analyze targets based on manually-selected images; and then correlate the analyzed images with clinical information sources (e.g. outcomes or correlative data). Thus, projects with large patient numbers often encounter insurmountable obstacles that limit research productivity. MIRA (an in-house developed programming toolkit) solves a common problem for all researchers at PM/UHN studying diagnostic, radiotherapy treatment planning, and/or on-treatment imaging by providing a consistent automated analysis environment for these data. MIRA also enhances ethics approved studies with direct linkage to real-time clinical data including diagnostic imaging via collaboration with the Joint Department of Medical Imaging, radiation oncology treatment planning information, and daily radiation oncology on-treatment imaging. The MIRA Clinical Learning Environment (MIRACLE) quality improvement project intends to use the MIRA platform to develop automated clinical pipelines to address three specific study aims: To identify lung cancer patients with undiagnosed underlying inflammatory lung disease (ILD) from pre-treatment diagnostic images To estimate individual patients' tumor growth-rate between diagnostic and treatment planning images (specific growth-rate, SGR) To provide each patient with an estimate of dynamic radiation treatment toxicity risk using radiation treatment planning information, while continuously updating risk estimates using daily cone-beam computed tomography (CBCT) images routinely obtained before each radiation treatment. MIRACLE is linked safely to active clinical data repositories and has the potential to directly impact daily cancer treatment decisions by making existing imaging data findable, rapidly accessible, interoperable, and reusable for both clinical and research analysis by end users including the physicians caring for lung cancer patients, and cancer researchers. This facilitates evaluation of novel imaging research findings in large patient numbers for clinical and research use. The MIRACLE project's goal is to specifically demonstrate the clinical implementation feasibility of automatically linking and analyzing clinical imaging data alongside clinical outcome; ultimately, helping to deliver value-based healthcare via better patient selection (ILD/SGR) and monitoring/adjusting treatment to decrease toxicity (CBCT). Feedback from the participating radiation oncologists will be gathered to assess the feasibility and effectiveness of showing patient-specific insights for inflammatory lung disease (ILD), a specific tumour growth rate greater than 0.04 (SGR) and cone-beam computed tomography system (CBCT) changes to clinicians at the point of care. The analysis will help to understand clinicians' perceptions of information provided to them from the model regarding ILD prediction, SGR and lung density changes over the QI period and whether clinicians changed their decision about data already available to them as a result of the model-prompted risk classification (i.e., to re-review or further assess patients for ILD, SGR and CBCT changes based on those patients highlighted by the model as being higher risk).