MIRA Clinical Learning Environment (MIRACLE): Lung

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.

Conditions

• Lung Cancer

Interventions

OTHER

Application of ILD prediction machine learning model to planning imaging

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.

OTHER

Routine, automatic presentation of ILD risk level for evaluation by the clinician.

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.

OTHER

Application of SGR machine learning model to diagnostic and planning imaging

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.

OTHER

Routine estimation of tumor specific growth rate (SGR) for lesions being considered for radiation therapy presented to clinician.

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.

OTHER

Application of CBCT machine learning model to on-treatment imaging

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.

OTHER

Routine monitoring of lung density changes during the course of treatment presented to clinician.

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.

Sponsors & Collaborators

• University of Toronto

  collaborator OTHER

• University Health Network, Toronto

  lead OTHER

Principal Investigators

• Hope · University Health Network, Toronto

Eligibility

Min Age

18 Years

Sex

ALL

Healthy Volunteers

No

Timeline & Regulatory

Start

2022-01-01

Primary Completion

2023-12-31

Completion

2023-12-31

Countries

• Canada

Study Locations