Performance and Evaluation in Computed Tomographic Colonography Screening (PERFECTS): Protocol for a Cluster Randomised Trial
Plumb, A. A.; Obaro, A. E.; Bassett, P.; Baldwin-Cleland, R.; Halligan, S.; Burling, D.
Show abstract
BackgroundColorectal cancer (CRC) is a common, important healthcare priority and improving patient outcome relies on early diagnosis. Colonoscopy and computed tomographic colonography (CTC) are commonly-used diagnostic tests. Although colonoscopists are highly regulated and must be accredited, no analogous process exists for CTC. There are currently no universally accepted radiologist performance indicators for CTC, and lack of regulatory oversight may lead to variability in quality and lower neoplasia detection rates. This study aims to determine whether a structured educational training and feedback programme can improve radiologist interpretation accuracy. MethodsNHS England CTC reporting radiologists will be cluster randomised to either an intervention (one-day individualised training and assessment with feedback) or control (assessment with no training or feedback) arm. Each cluster represents radiologists reporting CTC in a single NHS site. Both the intervention and control arm will undertake four CTC assessments at baseline, 1-month (after training; intervention arm or enrolment; control arm), 6- and 12 months to assess their detection of colorectal cancer (CRC) and 6mm+ polyps. The primary outcome will be difference in sensitivity at the 1-month test between arms. Secondary outcomes will include sensitivity at 6 and 12 months and radiologist characteristics associated with improved performance. Multilevel logistic regression will be used to analyse per-polyp and per-case sensitivity. Local ethical and Health Research Authority approval have been obtained. DiscussionLack of infrastructure to ensure that CTC radiologists can report adequately and lack of consensus regarding appropriate quality metrics may lead to variability in performance. Our provision of a structured education programme with feedback will evaluate the impact of individualised training and identify the factors related to improved radiologist performance in CTC reporting. An improvement in performance could lead to increased neoplasia detection and better patient outcome. RegistrationClinical Trials (ClinicalTrials.gov Identifier: NCT02892721); available from: https://clinicaltrials.gov/ct2/show/NCT02892721. NIHR Clinical Research Network (CPMS ID 32293).
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- What is the suitability of clinical vignettes in benchmarking the performance of online symptom checkers? An audit study 91%
- Development and validation of multivariable machine learning algorithms to predict risk of cancer in symptomatic patients referred urgently from primary care 90%
- Psychological Impact of the Galleri Test (sIG(n)al): Protocol for a longitudinal evaluation of the psychological impact of receiving a cancer signal in the NHS-Galleri Trial 90%
Similar papers in this journal
- The Role of Prehabilitation in Improving Brain Health and Cognition After Chemotherapy in Patients with Colorectal Cancer: Study Protocol of the Chemo Brain Prehab Project 90%
- Colorectal cancer in patients with single versus double positive faecal immunochemical test results: A retrospective cohort study 89%
- Study Protocol: LIAM Mc Trial (Linking In with Advice and supports for Men impacted by Metastatic cancer) 89%
Similar papers in this journal
- Development and validation of AI-based pre-screening of large bowel biopsies 91%
- Remote Covid Assessment in Primary Care (RECAP) risk prediction tool: derivation and real-world validation studies 88%
- An external validation of the QCovid risk prediction algorithm for risk of mortality from COVID-19 in adults: national validation cohort study in England 88%
Similar papers in this journal
- Assessing awareness of blood cancer symptoms and barriers to symptomatic presentation: Measure development and results from a population survey in the UK 88%
- Machine learning enables detection of early-stage colorectal cancer by whole-genome sequencing of plasma cell-free DNA 88%
- Risk-Reducing Salpingectomy: Considerations from an OBGYN Perspective 87%
Similar papers in this journal
- Combining faecal immunochemical testing with blood test results to identify patients with symptoms at risk of colorectal cancer: a consecutive cohort of 16,604 patients tested in primary care 92%
- Evaluation and Improvement of the National Early Warning Score (NEWS2) for COVID-19: a multi-hospital study 88%
- Describing the population experiencing COVID-19 vaccine breakthrough following second vaccination in England: A cohort study from OpenSAFELY 86%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.