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Trial Title:
MIRA Clinical Learning Environment (MIRACLE): Lung
NCT ID:
NCT05689437
Condition:
Lung Cancer
Conditions: Keywords:
Machine Learning
Artificial Intelligence
Quality Improvement
Clinical Implementation
Study type:
Observational
Overall status:
Recruiting
Study design:
Time perspective:
Prospective
Intervention:
Intervention type:
Other
Intervention name:
Application of ILD prediction machine learning model to planning imaging
Description:
The ILD prediction machine learning model will be applied to the treatment planning
imaging of lung cancer patients receiving radiation therapy (RT). The model will score
the risk as high risk or low risk for having underlying ILD.
Arm group label:
ILD Prospective Mode
Arm group label:
ILD Silent Mode
Intervention type:
Other
Intervention name:
Routine, automatic presentation of ILD risk level for evaluation by the clinician.
Description:
Participating clinicians will be provided with an ILD risk estimate for all lung cancer
patients receiving RT who are deemed potentially high-risk based on the model. In these
cases, the clinician will receive an email identifying the patient medical record number
(MRN) and 'potential high-risk for ILD' flag. Clinicians will then be able to decide
whether, based on the information, they want to reassess the patient for ILD prior to
starting treatment. Clinicians will also be presented with a short survey each time they
are sent an email for a potential high-risk for ILD case so the study team can better
understand how that information was used, if at all.
Arm group label:
ILD Prospective Mode
Intervention type:
Other
Intervention name:
Application of SGR machine learning model to diagnostic and planning imaging
Description:
The SGR machine learning model will be applied to the imaging of lung cancer patients
with node negative lung cancer receiving stereotactic RT. The automatic calculation will
compare target lesions on the patient's diagnostic images with those same lesions on
treatment planning images.
Arm group label:
SGR Prospective Mode
Arm group label:
SGR Silent Mode
Intervention type:
Other
Intervention name:
Routine estimation of tumor specific growth rate (SGR) for lesions being considered for radiation therapy presented to clinician.
Description:
Participating clinicians will be provided with an SGR calculation for each lung cancer
patient with node negative lung cancer receiving stereotactic RT. This SGR calculation
will be presented to clinicians, who will then be able to decide, based on the
information, how they want to address and track a patient's overall survival and failure
free survival. Clinicians will also be presented with a short survey each time they are
provided with a patient's SGR calculation so the study team can better understand how
that information was used, if at all.
Arm group label:
SGR Prospective Mode
Intervention type:
Other
Intervention name:
Application of CBCT machine learning model to on-treatment imaging
Description:
The CBCT machine learning model will be applied to on-treatment imaging as part of
routine care for patients with node positive lung cancer receiving standard RT. An
indicator of lung density changes will be calculated automatically by comparing cone beam
CTs (CBCTs) completed prior to each treatment.
Arm group label:
CBCT Prospective Mode
Arm group label:
CBCT Silent Mode
Intervention type:
Other
Intervention name:
Routine monitoring of lung density changes during the course of treatment presented to clinician.
Description:
Participating clinicians will be provided with a daily indicator of lung density changes
for each patient with node positive lung cancer receiving standard RT. This measurement
will be presented to the clinical team, who will then be able to decide, based on the
information, how they want to address and track relevant outcomes such as pneumonitis.
Additionally, this information may provide the clinical team with feedback about the lung
reaction occurring as a result of treatment. Density changes will be documented and
monitored for future validation studies, which are outside of the scope of this
application.
Arm group label:
CBCT Prospective Mode
Summary:
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.
Detailed description:
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).
Criteria for eligibility:
Study pop:
All lung cancer patients 18 years or older receiving radiation therapy (RT) will be
included in the study between 2000 until the end of the study. The type of RT received by
the patient will further determine which of the three aims their data is suitable for,
and therefore which clinical trial methodology will be used.
Sampling method:
Non-Probability Sample
Criteria:
Inclusion Criteria:
- Diagnosed with lung cancer stage I-IV and planned for treatment with radiotherapy at
Princess Margaret hospital. The three aims of this project have specific inclusion
criteria as follows.
- Aim 1 ILD: All lung cancer patients receiving RT.
- Aim 2 SGR: Node negative lung cancer patients receiving stereotactic body RT.
- Aim 3 CBCT: Node positive lung cancer patients receiving standard RT.
Exclusion Criteria:
- No exclusion criteria
Gender:
All
Minimum age:
18 Years
Maximum age:
N/A
Healthy volunteers:
No
Locations:
Facility:
Name:
Princess Margaret Hospital
Address:
City:
Toronto
Country:
Canada
Status:
Recruiting
Contact:
Last name:
Andrew Hope, MD, FRCPC
Start date:
January 1, 2022
Completion date:
December 31, 2023
Lead sponsor:
Agency:
University Health Network, Toronto
Agency class:
Other
Collaborator:
Agency:
University of Toronto
Agency class:
Other
Source:
University Health Network, Toronto
Record processing date:
ClinicalTrials.gov processed this data on November 12, 2024
Source: ClinicalTrials.gov page:
https://clinicaltrials.gov/ct2/show/NCT05689437
https://ctep.cancer.gov/protocoldevelopment/electronic_applications/docs/ctcae_v5_quick_reference_5x7.pdf