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NCT05689437: MIRACLE
MIRA Clinical Learning Environment (MIRACLE): Lung
trial testing Application of ILD prediction machine learning model to planning imaging in Lung Cancer in 1,000 participants. Status unknown.
31 December 2023
Quick facts
| Lead sponsor | University Health Network, Toronto |
|---|---|
| Status | Status unknown |
| Study type | OBSERVATIONAL |
| Enrollment | 1,000 |
| Start date | 1 January 2022 |
| Primary completion | 31 December 2023 |
| Estimated completion | 31 December 2023 |
| Sites | 1 location across Canada |
Drugs / interventions 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
- Routine monitoring of lung density changes during the course of treatment presented to clinician.
Conditions studied
- Lung Cancer — all drugs for Lung Cancer →
Sponsor
University Health Network, Toronto
Who can join
18 and older, any sex, with Lung Cancer. Patients with the condition only — healthy volunteers not accepted.
Sponsor's own description
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.
Publications & conference data
No peer-reviewed publications indexed yet for this trial.
Verify or expand the search:
- PubMed search for NCT05689437
- Europe PMC full search
- ASCO Meeting Library
- ESMO Meeting Library
- bioRxiv preprints
- medRxiv preprints
- Google Scholar
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Other University Health Network, Toronto trials
Trials by the same sponsor.
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Verify against primary sources
- ClinicalTrials.gov — authoritative US registry record
- WHO ICTRP — international registry index
- EU Clinical Trials Register
- Sponsor press releases (Google)
- Trial protocol + status: ClinicalTrials.gov NCT05689437 (US National Library of Medicine, public domain)
- Drug + disease cross-links: matched in real time against Drug Landscape's normalised drug + company + condition tables
- Sponsor: as reported to ClinicalTrials.gov by University Health Network, Toronto
- Last refreshed: 19 January 2023
Drug Landscape aggregates and links these public records for informational use only. Always verify against the primary source before clinical or regulatory decisions. Canonical URL: https://druglandscape.com/trial/NCT05689437.
Primary sources · FDA · ClinicalTrials.gov · EMA · SEC EDGAR · ChEMBL · Wikidata · full sourcing