Clinical trial · Observational
MIRA Clinical Learning Environment (MIRACLE): Lung
NCT05689437CI-TRIAL-00063330MIRACLEunknownClinicalTrials.gov clinicaltrialsProvenance
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Lung Cancer | Malignant Lung Neoplasm | CURATED_EXACT | 0.92 |
Interventions
Interventions (6)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Application of CBCT machine learning model to on-treatment imaging | Other | — | UNRESOLVED |
| Application of ILD prediction machine learning model to planning imaging | Other | — | UNRESOLVED |
| Application of SGR machine learning model to diagnostic and planning imaging | Other | — | UNRESOLVED |
| Routine, automatic presentation of ILD risk level for evaluation by the clinician. | Other | — | UNRESOLVED |
| Routine estimation of tumor specific growth rate (SGR) for lesions being considered for radiation therapy presented to clinician. | Other | — | UNRESOLVED |
| Routine monitoring of lung density changes during the course of treatment presented to clinician. | Other | — | UNRESOLVED |
Design
Arms and outcomes
Arms (6)
- label
- ILD Silent Mode
- description
- 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.
- interventionNames
- Other: Application of ILD prediction machine learning model to planning imaging
- label
- ILD Prospective Mode
- description
- 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.
- interventionNames
- Other: Application of ILD prediction machine learning model to planning imaging
- Other: Routine, automatic presentation of ILD risk level for evaluation by the clinician.
- label
- SGR Silent Mode
- description
- 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.
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
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
References
Publications (12)
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- BACKGROUNDSteiner 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. PMID 30312179
- BACKGROUNDLi 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. PMID 30253801
- BACKGROUNDDascalu 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. PMID 31101596
- BACKGROUNDPhillips 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. PMID 31617929
- BACKGROUNDAbramoff 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. PMID 31304320