Deep learning-assisted interactive contouring of lung cancer: Impact on contouring time and consistency
A DL-assisted interactive contouring approach decreased active contouring time and local inter-observer variability when used to delineate lung cancer GTVs compared to a standard manual method. Integration of this tool into the clinical workflow could assist clinicians in contouring tasks and improve contouring efficiency.
Authors
- Shawn Ellis
- Emma Burke
- Daniel Ajzensztejn
- Claire Bernard (ORCID: https://orcid.org/0000-0002-1003-7996)
- N. Panakis (ORCID: https://orcid.org/0000-0001-7884-3380)
- Michael J. Trimpl (ORCID: https://orcid.org/0000-0003-3393-0717)
- Geoff S. Higgins (ORCID: https://orcid.org/0000-0003-3072-909X)
- Roland Hustinx (ORCID: https://orcid.org/0000-0001-7164-8742)
- Katherine A. Vallis (ORCID: https://orcid.org/0000-0003-4672-5683)
- Mark J. Gooding (ORCID: https://orcid.org/0000-0002-1177-5608)
- Eleanor Stride (ORCID: https://orcid.org/0000-0003-3371-5929)
- Philippa Johnstone (ORCID: https://orcid.org/0009-0001-4123-5987)
- Emma Doyle (ORCID: https://orcid.org/0000-0001-8978-2966)
- Sorcha Campbell
- Rebecca Towers
Institutions
- NHS Lothian (GB)
- Western General Hospital (GB)
- Peter MacCallum Cancer Centre (AU)
- Edinburgh Cancer Research (GB)
- University of Manchester (GB)
- University of Oxford (GB)
- Mirada Medical (United Kingdom) (GB)
- Centre Hospitalier Universitaire de Liège (BE)
- Science Oxford (GB)
- Oxford University Hospitals NHS Trust (GB)
Publication Details
- Journal
- Radiotherapy and Oncology
- Published
- 2024-09-03
- DOI
- https://doi.org/10.1016/j.radonc.2024.110500
- Citations
- 3
- Primary Topic
- Lung Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.26
Funders
- Cancer Research UK
- European Commission
- Cancer Research UK Cambridge Institute, University of Cambridge
- H2020 Marie Skłodowska-Curie Actions