Does triage of chest X-rays with artificial intelligence shorten the time to lung cancer diagnosis: a randomised controlled trial
Not Applicable
- Conditions
- ung cancerCancer
- Registration Number
- ISRCTN78987039
- Lead Sponsor
- ottingham University Hospitals NHS Trust
- Brief Summary
Not available
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- Ongoing
- Sex
- All
- Target Recruitment
- 150000
Inclusion Criteria
1. Chest X-ray referred from primary care
2. Age = 18 years
3. Anteroposterior (AP) or Posteroanterior (PA) view
Exclusion Criteria
1. Age <18 years
2. CXR referral not from primary care
3. Lateral X-ray view of the chest
Study & Design
- Study Type
- Interventional
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method 1. Time from chest X-ray to lung cancer diagnosis in days from the cancer waiting time database<br>2. Time from chest X-ray to CT (when performed) in days from the radiology information system
- Secondary Outcome Measures
Name Time Method 1. Time to first respiratory cancer outpatient appointment in days from the cancer waiting time database<br>2. Time to treatment start for lung cancer patients in days from the cancer waiting time database<br>3. Agreement between AI (qXR) and human readers for normal/abnormal interpretation of chest X-ray as an agree/disagree decision with discordance review by a thoracic radiologist where required<br>4. Number of urgent lung cancer referrals from the cancer waiting time database<br>5. The incidence of lung cancer from the cancer waiting time database<br>6. The stage of lung cancer diagnosis from the cancer waiting time database<br>7. Cost-effectiveness of AI support at the time of CXR acquisition and prioritisation for immediate review of CXRs; to be measured by difference in costs per patient diagnosed, per percentage increase in early-stage diagnosis and potentially per QALY subject to the availability of health utilities in the published studies