title: An automated workflow for patient-specific quality control of contour propagation.
abstract: Contour propagation is an essential component of adaptive radiotherapy, but current contour propagation algorithms are not yet sufficiently accurate to be used without manual supervision. Manual review of propagated contours is time-consuming, making routine implementation of real-time adaptive radiotherapy unrealistic. Automated methods of monitoring the performance of contour propagation algorithms are therefore required. We have developed an automated workflow for patient-specific quality control of contour propagation and validated it on a cohort of head and neck patients, on which parotids were outlined by two observers. Two types of error were simulated-mislabelling of contours and introducing noise in the scans before propagation. The ability of the workflow to correctly predict the occurrence of errors was tested, taking both sets of observer contours as ground truth, using receiver operator characteristic analysis. The area under the curve was 0.90 and 0.85 for the observers, indicating good ability to predict the occurrence of errors. This tool could potentially be used to identify propagated contours that are likely to be incorrect, acting as a flag for manual review of these contours. This would make contour propagation more efficient, facilitating the routine implementation of adaptive radiotherapy.
authors.affiliation: Division of Molecular and Clinical Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK. Christie Medical Physics and Engineering (CMPE), The Christie NHS Foundation Trust, Manchester, UK.
authors.forename: William J
authors.initials: WJ
authors.lastname: Beasley
authors.forename: Alan
authors.initials: A
authors.lastname: McWilliam
authors.forename: Nicholas J
authors.initials: NJ
authors.lastname: Slevin
authors.forename: Ranald I
authors.initials: RI
authors.lastname: Mackay
authors.forename: Marcel
authors.initials: M
authors.lastname: van Herk
mesh_terms: D000465:Algorithms; D015331:Cohort Studies; D006258:Head and Neck Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D011785:Quality Assurance, Health Care / Q000592:standards*; D011786:Quality Control; D012372:ROC Curve; D014057:Tomography, X-Ray Computed / Q000379:methods*; D057188:Workflow