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| Name | Class |
|---|---|
| Queen Mary University of London | OTHER |
| The Leeds Teaching Hospitals NHS Trust | OTHER |
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The aim of the trial is to understand whether a computerised tool designed to quickly spot problems in brain scans (MIDI) can help doctors diagnose cases faster. This will help patients by getting them the treatment they need sooner. How useful doctors find the tool will also be measured, and whether it is cost-saving for the National Health Service (NHS).
NHS radiology services are under unprecedented pressure as rising demand outpaces available workforce capacity. MRI utilisation across the NHS has increased by 9% year-on-year. As a result, many departments have relied on unsustainable measures such as outsourcing and weekend insourcing to manage reporting backlogs.
Delays in scan reporting have serious implications, including missed or delayed diagnoses, prolonged hospital stays, higher healthcare costs, and poorer patient outcomes. A systematic review and meta-analysis found that each month of delay in initiating cancer treatment increases mortality risk by approximately 10%.
There is an urgent need for validated digital tools that can reduce TATs without compromising diagnostic accuracy. The TRIAGE study (Triage Radiology Imaging Assessment for Greater Effectiveness (TRIAGE): a Cluster-Randomised Crossover Trial Evaluating the Reporting Turn-Around-Time of Brain Magnetic Resonance Images using an AI-enabled Abnormality Detector for Prioritisation) evaluates whether MIDI can reduce the average TAT of abnormal scans in routine NHS clinical practice.
The primary purpose of MIDI is workflow triage: it enables prioritisation of abnormal scans within radiology reporting worklists, helping departments manage high volumes efficiently.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Intervention - MIDI ON | Experimental | Scans are triaged by the MIDI AI tool. The system automatically classifies each MRI scan as either "normal" or "abnormal" shortly after image acquisition. |
|
| Control - MIDI OFF | No Intervention | MRI scans are reported according to standard care in chronological order, with no AI prioritisation. |
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| MIDI | Device | MIDI is an AI-based TRIAGE tool and its primary purpose is workflow triage of brain MRI scans. |
|
| Measure | Description | Time Frame |
|---|---|---|
| Turnaround time for abnormal outpatient brain MRI scans | Time from abnormal MRI acquisition to radiologist report authorisation over 8 months. |
| Measure | Description | Time Frame |
|---|---|---|
| Turnaround time for abnormal inpatient brain MRI scans | Time from abnormal MRI acquisition to radiologist report authorisation over 8 months. | |
| Turnaround time for abnormal combined Brain MRI scans | Time from abnormal MRI acquisition to radiologist report authorisation over 8 months. |
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Inclusion Criteria:
For main trial
For O3 subgroup ● Radiologists, radiographers, and referring clinicians involved with the use of the MIDI tool at sites who consent to completing the staff survey
For O7 subgroup
For O8 subgroup
Additionally for those completing EORTC questionnaires:
● Able to give consent and likely to be able to complete EORTC questionnaires.
Exclusion Criteria:
For main trial
For O3 subgroup ● Radiologists, radiographers, and referring clinicians involved with the use of the MIDI tool at sites who do not consent to completing the staff survey
For O7 subgroup
For O8 subgroup
Additionally for those completing EORTC questionnaires:
● Unable to give consent and unlikely to be able to complete EORTC questionnaires.
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| PCTU Trial Management team | Contact | 00 | pctu-triage@qmul.ac.uk | |
| Giusi Manfredi, PhD | Contact | 00 | giusi.manfredi@kcl.ac.uk |
| Name | Affiliation | Role |
|---|---|---|
| Thomas C Booth | King's College London | Study Chair |
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| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 25519890 | Background | Candel MJ, van Breukelen GJ. Sample size calculation for treatment effects in randomized trials with fixed cluster sizes and heterogeneous intraclass correlations and variances. Stat Methods Med Res. 2015 Oct;24(5):557-73. doi: 10.1177/0962280214563100. Epub 2014 Dec 17. | |
| 23017638 | Background | van Breukelen GJ, Candel MJ. Calculating sample sizes for cluster randomized trials: we can keep it simple and efficient! J Clin Epidemiol. 2012 Nov;65(11):1212-8. doi: 10.1016/j.jclinepi.2012.06.002. |
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The TRIAGE study is a definitive, cluster-randomised crossover trial (CRT). Each participating site will be involved for a total duration of 22 months, and the study has four stages:
At randomisation, sites are allocated (in a 1:1 ratio) to two sequences of intervention conditions. There are two possible intervention conditions:
The two possible sequences to which a site may be randomised are: MIDI on in the first phase, crossing over to MIDI OFF in the second phase; or MIDI OFF in the first phase, crossing over to MIDI ON in the second phase.
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| Cost Effectiveness - Incremental cost per reduction in brain MRI scan turnaround time, radiologist reporting time, insourcing/outsourcing of reporting activity | From MRI scan acquisition to final radiologist report authorisation over 8 months. |
| Cost Effectiveness for patients with suspected TIA or Stroke - incremental cost per reduction in brain MRI turnaround time, time to diagnosis and time to treatment change | From MRI scan acquisition to initiation of treatment over 8 months. |
| Cost Effectiveness for patients newly diagnosed with brain metastases secondary to melanoma, lung cancer, or breast cancer | Incremental cost per change in turnaround time, time to diagnosis, time to treatment change, in 28-day mortality, in 90-day mortality, in ECOG/KPS at 90 days. | From MRI scan acquisition to initiation of treatment within 8 months as well as from MRI scan acquisition to 28 and 90 days for mortality and 90 days for ECOG/KPS outcomes. |
| Staff satisfaction - Questionnaire score from radiologists, radiographers, and referring clinicians, collected at month 6 of the intervention | From the start to month 6 of the intervention. |
| Turnaround time for all brain MRI scans (normal and abnormal combined) | Time from MRI acquisition to final radiologist report acquisition over 8 months |
| Turnaround Time for Critically Abnormal Brain MRI scans | Acquisition of critically abnormal brain MRI scans to final radiologist report authorisation over 8 months. |
| Performance accuracy - MIDI assessment of abnormal/normal brain MRI scan, radiologist assessment of abnormal/normal brain MRI scan and reference standard of abnormal/normal brain MRI scan | MRI scan acquisition to final radiologist report authorisation over 8 months. |
| Clinical Effectiveness Outcomes in patients with Suspected Transient Ischemic Attack/Stroke (time from MRI acquisition to definitive diagnosis, censoring or treatment change) | Time from MRI Acquisition to Definitive Diagnosis or Censoring, Time from MRI Acquisition to Treatment Change during Phase I and II (19 months) |
| Proportion of diagnoses within 14 days in a subgroup of patients diagnosed with melanoma or lung cancer with de novo metastases | Within 14 days following index brain scan containing brain metastases |
| Mortality rates in a subgroup of patients diagnosed with melanoma or lung cancer with de novo metastases | Within 28 days following the index MRI brain scan and within 90 days following the index MRI brain scan |
| Days alive and out of hospital following index MRI brain scan in a subgroup of patients diagnosed with melanoma or lung cancer with de novo metastases | Days alive and out of hospitals within 90 days following index MRI brain scan |
| Time to Treatment Change in a subgroup of patients diagnosed with melanoma or lung cancer with de novo metastases | Time from MRI scan acquisition to treatment (or censoring at the end of the data collection period) within 8 months. |
| Occurrence of treatment/SRS (Stereotactic Radiosurgery) treatment in a subgroup of patients diagnosed with melanoma or lung cancer with de novo metastases | Treatment - Within 48 days following index MRI brain scan Treatment with SRS - Within 90 days following index brain MRI scan |
| Functional status using ECOG and KPS where possible in a subgroup of patients diagnosed with melanoma or lung cancer with de novo metastases | ECOG: Standardised 0 to 4 scale - 0 is fully active, 4 is completely disabled KPS - 10 to 100 scale - 10 is critically ill, 100 is no evidence of disease | Within 90 days following index MRI brain scan |
| Brain tumour-related Quality of Life in a consenting sub-group of patients with eligible brain metastases measured using EORTC QLQ-C30 questionnaire | At 90 days (± 14 days) following the index MRI brain scan |
| Functional status in a consenting sub-group of patients with eligible brain metastases using ECOG and KPS scoring | ECOG: Standardised 0 to 4 scale - 0 is fully active, 4 is completely disabled KPS - 10 to 100 scale - 10 is critically ill, 100 is no evidence of disease | At 90 days (± 14 days) following index MRI brain scan |
| Brain tumour-related Quality of Life in a consenting sub-group of patients with eligible brain metastases measured using EORTC QLQ-BN20 questionnaire | At 90 days (± 14 days) following the index MRI brain scan |
| 27350420 | Background | Hooper R, Teerenstra S, de Hoop E, Eldridge S. Sample size calculation for stepped wedge and other longitudinal cluster randomised trials. Stat Med. 2016 Nov 20;35(26):4718-4728. doi: 10.1002/sim.7028. Epub 2016 Jun 28. |
| Background | Powers DMW (2011). "Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness & Correlation". Journal of Machine Learning Technologies. 2 (1): 37-63. doi:10.1186/s12880-015-0068-x. |
| Background | ClinicalTrials.gov. Study NCT04368481. https://clinicaltrials.gov/study/NCT04368481https://www.sciencedirect.com/science/article/pii/S1361841522000433 |
| 35183876 | Background | Wood DA, Kafiabadi S, Busaidi AA, Guilhem E, Montvila A, Lynch J, Townend M, Agarwal S, Mazumder A, Barker GJ, Ourselin S, Cole JH, Booth TC. Deep learning models for triaging hospital head MRI examinations. Med Image Anal. 2022 May;78:102391. doi: 10.1016/j.media.2022.102391. Epub 2022 Feb 12. |
| Background | NHS England (2023). Faster Diagnosis Standard. https://www.england.nhs.uk/cancer/faster-diagnosis |
| Background | NICE (2022). Evidence Standards Framework for Digital Health Technologies. https://www.nice.org.uk/corporate/ecd7 |
| Background | Rothwell PM et al. (2007). Early risk of stroke after a transient ischaemic attack. JAMA, 297(14), 1472-1482. https://jamanetwork.com/journals/jama/fullarticle/193353 |
| Background | Royal College of Radiologists (2023). Clinical Radiology Workforce Census Report. https://www.rcr.ac.uk |
| 33148535 | Background | Hanna TP, King WD, Thibodeau S, Jalink M, Paulin GA, Harvey-Jones E, O'Sullivan DE, Booth CM, Sullivan R, Aggarwal A. Mortality due to cancer treatment delay: systematic review and meta-analysis. BMJ. 2020 Nov 4;371:m4087. doi: 10.1136/bmj.m4087. |