Recruiting

Observational Study

Sponsor:

University Health Network, Toronto

Code:

NCT05689437

Conditions

Lung Cancer

Eligibility Criteria

Sex: All

Age: 18+

Healthy Volunteers: Not accepted

Interventions

Application of ILD prediction machine learning model to planning imaging

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

Application of SGR machine learning model to diagnostic and planning imaging

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

Application of CBCT machine learning model to on-treatment imaging

Study Details

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 (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

Study ID

NCT05689437

Start date

Jan 1, 2022

Status verified date

Jan, 2023

Completion date

Dec 31, 2023

Anticipated

Primary completion date

Dec 31, 2023

Anticipated

Eligibility Criteria

Eligibility Criteria

Sex: All

Age: 18+

Healthy Volunteers: Not accepted

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

Study Design

Enrollment

1000 participants

Anticipated

Interventions and Outcome Measures

Arms

ILD Silent Mode

The ILD model will be run on patients undergoing routine treatment planning imaging where the notification is sent to the study team for a period of one month to ensure the pipeline is operating as intended.

ILD Prospective Mode

Following successful silent mode, the ILD model will be run on patients undergoing routine treatment planning imaging and the notifications will be sent to the treating physician to incorporate into their workflow.

SGR Silent Mode

The SGR model will be run on patients undergoing routine treatment planning imaging where the notification is sent to the study team for a period of one month to ensure the pipeline is operating as intended.

SGR Prospective Mode

Following successful silent mode, the SGR model will be run on patients undergoing routine treatment planning imaging and the notifications will be sent to the treating physician to incorporate into their workflow.

CBCT Silent Mode

The CBCT model will be run on patients receiving routine on-treatment imaging where the notification is sent to the study team for a period of one month to ensure the pipeline is operating as intended.

CBCT Prospective Mode

Following successful silent mode, The CBCT model will be run on patients receiving routine on-treatment imaging and the notifications will be sent to the treating physician to incorporate into their workflow.

Interventions

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.

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.

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.

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.

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.

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.

Primary outcome measure

  • Rates of true positive diagnosis of ILD increase with high/low patient risk predictions being made available to clinicians. [ Time Frame: January 2022 - December 2023 ]
  • Previously difficult-to-assess information are made available during the clinical workflow as an easily accessible information source available to clinicians [ Time Frame: January 2022 - December 2023 ]
  • Radiation oncologists use predictions provided from the model to support their clinical decision-making. [ Time Frame: January 2022 - December 2023 ]

Central Contacts and Locations

Central contacts

Andrew Hope, MD, FRCPC

416-946-2124Andrew.Hope@rmp.uhn.ca

Locations

Princess Margaret Hospital

Recruiting

Toronto, Ontario, Canada

More Information

Sponsor

University Health Network, Toronto

Last update posted

Jan 19, 2023

Last verified

Jan, 2023

Keywords

  • Machine Learning
  • Artificial Intelligence
  • Quality Improvement
  • Clinical Implementation

Trial information was received from ClinicalTrials.gov and was last updated on 2026-09-10. This information was provided to ClinicalTrials.gov by University Health Network, Toronto on 2023-01-19.