Ignite Creation Date:
2024-05-06 @ 6:31 PM
Last Modification Date:
2024-10-26 @ 2:49 PM
Study NCT ID:
NCT05689437
Status:
RECRUITING
Last Update Posted:
2023-01-19
First Post:
2023-01-09
Brief Title:
MIRA Clinical Learning Environment MIRACLE Lung
Sponsor:
University Health Network Toronto
Organization:
University Health Network Toronto
Study Overview
Official Title:
MIRA Clinical Learning Environment MIRACLE Lung
Status:
RECRUITING
Status Verified Date:
2023-01
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
MIRACLE
Brief 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 ie 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
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 ie 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 clinicaltranslational researchers within Princess Margaret Hospital PMUniversity 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 eg 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 PMUHN studying diagnostic radiotherapy treatment planning andor 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 projects 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 ILDSGR and monitoringadjusting 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 004 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 ie 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
The current process for clinicaltranslational researchers within Princess Margaret Hospital PMUniversity 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 eg 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 PMUHN studying diagnostic radiotherapy treatment planning andor 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 projects 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 ILDSGR and monitoringadjusting 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 004 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 ie 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
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None