Lung CancerMIRACLENCT05689437
MIRA Clinical Learning Environment (MIRACLE): Lung
An observational study of Lung Cancer, run by University Health Network, Toronto at 1 site in Canada.
Trial 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… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Lung Cancer
- What is being tested?
- Application of ILD prediction machine learning model to planning imaging, Routine, automatic presentation of ILD risk level for evaluation by the clinician. and Application of SGR machine learning model to diagnostic and planning imaging
- How many people
- 1,000 participants
- Where
- 1 siteCanada
- Study dates
- 1 Jan 2022to 31 Dec 2023
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: 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.
Who may be able to join
- 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.
Who cannot join
- No exclusion criteria
Trial locations and contacts
1 site in 1 country.
Canada1 site, 1 recruiting
- Recruiting
Princess Margaret Hospital
Toronto, Ontario
Andrew Hope, MD, FRCPC
About this study
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.
Read the 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).
- Conditions
- Lung Cancer
- Being tested
- 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
- Keywords
- Machine Learning, Artificial Intelligence, Quality Improvement, Clinical Implementation
- Medical subject headings
- Lung Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Application of ILD prediction machine learning model to planning imagingOther
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.Other
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 imagingOther
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.Other
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 imagingOther
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.Other
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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Other: Application of ILD prediction machine learning model to planning imaging | |
| 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. Receives: Other: Application of ILD prediction machine learning model to planning imaging; Other: Routine, automatic presentation of ILD risk level for evaluation by the clinician. | |
| 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. Receives: Other: Application of SGR machine learning model to diagnostic and planning imaging | |
| 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. Receives: Other: Application of SGR machine learning model to diagnostic and planning imaging; Other: Routine estimation of tumor specific growth rate (SGR) for lesions being considered for radiation therapy presented to clinician. | |
| 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. Receives: Other: Application of CBCT machine learning model to on-treatment imaging | |
| 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. Receives: Other: Application of CBCT machine learning model to on-treatment imaging; Other: Routine monitoring of lung density changes during the course of treatment presented to clinician. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Rates of true positive diagnosis of ILD increase with high/low patient risk predictions being made available to clinicians.
An expert review of the cases and chart review will be correlated with survey responses to determine whether the rate of true positive cases were impacted by the implementation of the MIRACLE pathways.
Measured: 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
Clinicians will provide feedback on the communication of the predictions, the integration into their clinical workflow and timeliness of receiving the predictions in order to incorporate into their decision-making.
Measured: January 2022 - December 2023
Radiation oncologists use predictions provided from the model to support their clinical decision-making.
Clinicians will indicate in the survey their perceptions of accuracy and usefulness of the predictions and whether they have incorporated the predictions into their decision-making.
Measured: January 2022 - December 2023
Secondary outcomes (1)
Additional expertise is focused on patients identified as being higher risk for ILD, SGR > 0.04, or possible pneumonitis.
Clinicians will indicate in the survey whether they have gone back and reassessed or flagged patients in cases where the model identifies a possible high-risk for ILD, SGR > 0.04, or pneumonitis.
Measured: January 2022 - December 2023
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted19 Jan 2023
- Study start1 Jan 2022
- Primary completion31 Dec 2023 (estimated)
- Study completion31 Dec 2023 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (12)Papers the sponsor has linked to this study.
- Other trials for Lung CancerStudies of the same condition, open and under way.
- More trials run by University Health Network, TorontoThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 1,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- University Health Network, Toronto
- Sponsor type
- University, hospital or other
- Collaborators
- University of Toronto
- Responsible party
- Principal investigator, Andrew Hope, University Health Network, Toronto
- Investigators
- Hope, Principal investigator, University Health Network, Toronto
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05689437
- Sponsor's protocol ID
- QI ID#: 21-0193
- Acronym
- MIRACLE
- Status last verified
- Jan 2023
- First submitted
- 9 Jan 2023
- Last updated
- 19 Jan 2023
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: The individual patient data collected during the trial after de-identification will be made available 1-2 years after trial publication and ending 4 years after trial publication. The study protocol will be available. Researchers who provide a methodologically sound proposal will be granted access to the data made available to achieve the approved aims. To gain access to the data location, data requestors will need to sign a data access agreement.
Publications
Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019 Jan;25(1):44-56. doi: 10.1038/s41591-018-0300-7. Epub 2019 Jan 7.
PubMed 30617339Steiner DF, MacDonald R, Liu Y, Truszkowski P, Hipp JD, Gammage C, Thng F, Peng L, Stumpe MC. Impact of Deep Learning Assistance on the Histopathologic Review of Lymph Nodes for Metastatic Breast Cancer. Am J Surg Pathol. 2018 Dec;42(12):1636-1646. doi: 10.1097/PAS.0000000000001151.
PubMed 30312179Li C, Jing B, Ke L, Li B, Xia W, He C, Qian C, Zhao C, Mai H, Chen M, Cao K, Mo H, Guo L, Chen Q, Tang L, Qiu W, Yu Y, Liang H, Huang X, Liu G, Li W, Wang L, Sun R, Zou X, Guo S, Huang P, Luo D, Qiu F, Wu Y, Hua Y, Liu K, Lv S, Miao J, Xiang Y, Sun Y, Guo X, Lv X. Development and validation of an endoscopic images-based deep learning model for detection with nasopharyngeal malignancies. Cancer Commun (Lond). 2018 Sep 25;38(1):59. doi: 10.1186/s40880-018-0325-9.
PubMed 30253801Dascalu A, David EO. Skin cancer detection by deep learning and sound analysis algorithms: A prospective clinical study of an elementary dermoscope. EBioMedicine. 2019 May;43:107-113. doi: 10.1016/j.ebiom.2019.04.055. Epub 2019 May 14.
PubMed 31101596Phillips M, Marsden H, Jaffe W, Matin RN, Wali GN, Greenhalgh J, McGrath E, James R, Ladoyanni E, Bewley A, Argenziano G, Palamaras I. Assessment of Accuracy of an Artificial Intelligence Algorithm to Detect Melanoma in Images of Skin Lesions. JAMA Netw Open. 2019 Oct 2;2(10):e1913436. doi: 10.1001/jamanetworkopen.2019.13436.
PubMed 31617929Abramoff MD, Lavin PT, Birch M, Shah N, Folk JC. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ Digit Med. 2018 Aug 28;1:39. doi: 10.1038/s41746-018-0040-6. eCollection 2018.
PubMed 31304320Gulshan V, Rajan RP, Widner K, Wu D, Wubbels P, Rhodes T, Whitehouse K, Coram M, Corrado G, Ramasamy K, Raman R, Peng L, Webster DR. Performance of a Deep-Learning Algorithm vs Manual Grading for Detecting Diabetic Retinopathy in India. JAMA Ophthalmol. 2019 Sep 1;137(9):987-993. doi: 10.1001/jamaophthalmol.2019.2004.
PubMed 31194246Kanagasingam Y, Xiao D, Vignarajan J, Preetham A, Tay-Kearney ML, Mehrotra A. Evaluation of Artificial Intelligence-Based Grading of Diabetic Retinopathy in Primary Care. JAMA Netw Open. 2018 Sep 7;1(5):e182665. doi: 10.1001/jamanetworkopen.2018.2665.
PubMed 30646178Lee H, Yune S, Mansouri M, Kim M, Tajmir SH, Guerrier CE, Ebert SA, Pomerantz SR, Romero JM, Kamalian S, Gonzalez RG, Lev MH, Do S. An explainable deep-learning algorithm for the detection of acute intracranial haemorrhage from small datasets. Nat Biomed Eng. 2019 Mar;3(3):173-182. doi: 10.1038/s41551-018-0324-9. Epub 2018 Dec 17.
PubMed 30948806Mori Y, Kudo SE, Misawa M, Saito Y, Ikematsu H, Hotta K, Ohtsuka K, Urushibara F, Kataoka S, Ogawa Y, Maeda Y, Takeda K, Nakamura H, Ichimasa K, Kudo T, Hayashi T, Wakamura K, Ishida F, Inoue H, Itoh H, Oda M, Mori K. Real-Time Use of Artificial Intelligence in Identification of Diminutive Polyps During Colonoscopy: A Prospective Study. Ann Intern Med. 2018 Sep 18;169(6):357-366. doi: 10.7326/M18-0249. Epub 2018 Aug 14.
PubMed 30105375Glick D, Lyen S, Kandel S, Shapera S, Le LW, Lindsay P, Wong O, Bezjak A, Brade A, Cho BCJ, Hope A, Sun A, Giuliani M. Impact of Pretreatment Interstitial Lung Disease on Radiation Pneumonitis and Survival in Patients Treated With Lung Stereotactic Body Radiation Therapy (SBRT). Clin Lung Cancer. 2018 Mar;19(2):e219-e226. doi: 10.1016/j.cllc.2017.06.021. Epub 2017 Jul 10.
PubMed 29066051Atallah S, Cho BC, Allibhai Z, Taremi M, Giuliani M, Le LW, Brade A, Sun A, Bezjak A, Hope AJ. Impact of pretreatment tumor growth rate on outcome of early-stage lung cancer treated with stereotactic body radiation therapy. Int J Radiat Oncol Biol Phys. 2014 Jul 1;89(3):532-8. doi: 10.1016/j.ijrobp.2014.03.003.
PubMed 24929163
Study at a glance
From the registry record, last updated by the sponsor on 19 Jan 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- MIRA Clinical Learning Environment (MIRACLE): Lung
- ClinicalTrials.gov ID
- NCT05689437
- Acronym / sponsor ID
- MIRACLE / QI ID#: 21-0193
- Conditions
- Lung Cancer
- Keywords
- Machine Learning, Artificial Intelligence, Quality Improvement, Clinical Implementation
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 1,000 participants (estimated)
- Lead sponsor
- University Health Network, TorontoUniversity, hospital or other
- Collaborators
- University of Toronto
- Study start
- 1 Jan 2022 (actual)
- Primary completion
- 31 Dec 2023 (estimated)
- Study completion
- 31 Dec 2023 (estimated)
- First posted
- 19 Jan 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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).
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 6
| Arm | Type | Receives |
|---|---|---|
| ILD Silent Mode | – | Other: Application of ILD prediction machine learning model to planning imagingThe 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 | – | Other: Application of ILD prediction machine learning model to planning imaging, Other: Routine, automatic presentation of ILD risk level for evaluation by the clinician.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 | – | Other: Application of SGR machine learning model to diagnostic and planning imagingThe 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 | – | Other: Application of SGR machine learning model to diagnostic and planning imaging, Other: Routine estimation of tumor specific growth rate (SGR) for lesions being considered for radiation therapy presented to clinician.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 | – | Other: Application of CBCT machine learning model to on-treatment imagingThe 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 | – | Other: Application of CBCT machine learning model to on-treatment imaging, Other: Routine monitoring of lung density changes during the course of treatment presented to clinician.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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- 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.
Key inclusion criteria (4 listed)
- 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.
Key exclusion criteria (1 listed)
- No exclusion criteria
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Application of ILD prediction machine learning model to planning imagingThe 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 | ILD Prospective Mode, ILD Silent Mode |
| 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 | ILD Prospective Mode |
| Application of SGR machine learning model to diagnostic and planning imagingThe 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 | SGR Prospective Mode, SGR Silent Mode |
| 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 | SGR Prospective Mode |
| Application of CBCT machine learning model to on-treatment imagingThe 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 | CBCT Prospective Mode, CBCT Silent Mode |
| 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. | Other | CBCT Prospective Mode |
Primary outcome
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
An expert review of the cases and chart review will be correlated with survey responses to determine whether the rate of true positive cases were impacted by the implementation of the MIRACLE pathways.
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
Clinicians will provide feedback on the communication of the predictions, the integration into their clinical workflow and timeliness of receiving the predictions in order to incorporate into their decision-making.
Radiation oncologists use predictions provided from the model to support their clinical decision-making.
Time frame: January 2022 - December 2023
Clinicians will indicate in the survey their perceptions of accuracy and usefulness of the predictions and whether they have incorporated the predictions into their decision-making.
1 secondary outcome
Additional expertise is focused on patients identified as being higher risk for ILD, SGR > 0.04, or possible pneumonitis.
Time frame: January 2022 - December 2023
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 19 Jan 2023.
- Background: PubMed 30617339Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019 Jan;25(1):44-56. doi: 10.1038/s41591-018-0300-7. Epub 2019 Jan 7.
- Background: PubMed 30312179Steiner DF, MacDonald R, Liu Y, Truszkowski P, Hipp JD, Gammage C, Thng F, Peng L, Stumpe MC. Impact of Deep Learning Assistance on the Histopathologic Review of Lymph Nodes for Metastatic Breast Cancer. Am J Surg Pathol. 2018 Dec;42(12):1636-1646. doi: 10.1097/PAS.0000000000001151.
- Background: PubMed 30253801Li C, Jing B, Ke L, Li B, Xia W, He C, Qian C, Zhao C, Mai H, Chen M, Cao K, Mo H, Guo L, Chen Q, Tang L, Qiu W, Yu Y, Liang H, Huang X, Liu G, Li W, Wang L, Sun R, Zou X, Guo S, Huang P, Luo D, Qiu F, Wu Y, Hua Y, Liu K, Lv S, Miao J, Xiang Y, Sun Y, Guo X, Lv X. Development and validation of an endoscopic images-based deep learning model for detection with nasopharyngeal malignancies. Cancer Commun (Lond). 2018 Sep 25;38(1):59. doi: 10.1186/s40880-018-0325-9.
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- Individual participant dataSharing planned: Yes. The individual patient data collected during the trial after de-identification will be made available 1-2 years after trial publication and ending 4 years after trial publication. The study protocol will be available. Researchers who provide a methodologically sound proposal will be granted access to the data made available to achieve the approved aims. To gain access to the data location, data requestors will need to sign a data access agreement.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Hope principal investigator
University Health Network, Toronto
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Lung Cancer, run by University Health Network, Toronto at 1 site in Canada.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 19 Jan 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Canada. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Andrew Hope, MD, FRCPC (416-946-2124, Andrew.Hope@rmp.uhn.ca), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the 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). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05689437, which also lists 12 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 19 Jan 2023.