Deep learning; Interactive contouring; Lung tumour; NSCLC; Hematology; Oncology; Radiology, Nuclear Medicine and Imaging
Abstract :
[en] [en] BACKGROUND AND PURPOSE: To evaluate the impact of a deep learning (DL)-assisted interactive contouring tool on inter-observer variability and the time taken to complete tumour contouring.
MATERIALS AND METHODS: Nine clinicians contoured the gross tumour volume (GTV) using the PET-CT scans of 10 non-small cell lung cancer (NSCLC) patients, either using DL-assisted or manual contouring tools. After contouring a case using one contouring method, the same case was contoured one week later using the other method. The contours and time taken were compared.
RESULTS: Use of the DL-assisted tool led to a statistically significant decrease in active contouring time of 23 % relative to the standard manual segmentation method (p < 0.01). The mean observation time for all clinicians and cases made up nearly 60 % of interaction time for both contouring approaches. On average the time spent contouring per case was reduced from 22 min to 19 min when using the DL-assisted tool. Additionally, the DL-assisted tool reduced contour variability in the parts of tumour where clinicians tended to disagree the most, while the consensus contour was similar whichever of the two contouring approaches was used.
CONCLUSIONS: 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.
Disciplines :
Radiology, nuclear medicine & imaging
Author, co-author :
Trimpl, Michael J ; Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK, Department of Oncology, University of Oxford, Oxford, UK, Mirada Medical Ltd, Oxford, UK. Electronic address: michael.trimpl@wadham.ox.ac.uk
Campbell, Sorcha; Edinburgh Cancer Centre, Western General Hospital, Edinburgh, UK. Electronic address: sorcha.campbell@nhslothian.scot.nhs.uk
Panakis, Niki; Oxford University Hospitals NHS Foundation Trust, UK. Electronic address: niki.panakis@ouh.nhs.uk
Ajzensztejn, Daniel; Oxford University Hospitals NHS Foundation Trust, UK. Electronic address: daniel.ajzensztejn@ouh.nhs.uk
Burke, Emma; Oxford University Hospitals NHS Foundation Trust, UK. Electronic address: emma.burke1@ouh.nhs.uk
Ellis, Shawn ; Oxford University Hospitals NHS Foundation Trust, UK. Electronic address: shawn.ellis@ouh.nhs.uk
Johnstone, Philippa ; Peter MacCallum Cancer Centre, Melbourne, Australia. Electronic address: philippa.johnstone@petermac.org
Doyle, Emma ; Edinburgh Cancer Centre, Western General Hospital, Edinburgh, UK. Electronic address: emma.doyle@nhs.scot
Towers, Rebecca; Mirada Medical Ltd, Oxford, UK. Electronic address: rebeccajtowers@yahoo.co.uk
Higgins, Geoffrey; Department of Oncology, University of Oxford, Oxford, UK. Electronic address: geoffrey.higgins@oncology.ox.ac.uk
Bernard, Claire ; Université de Liège - ULiège > Département de physique
Hustinx, Roland ; Université de Liège - ULiège > Département des sciences cliniques > Médecine nucléaire
Vallis, Katherine A; Department of Oncology, University of Oxford, Oxford, UK. Electronic address: katherine.vallis@oncology.ox.ac.uk
Stride, Eleanor P J; Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK. Electronic address: eleanor.stride@eng.ox.ac.uk
Gooding, Mark J ; Mirada Medical Ltd, Oxford, UK, Division of Cancer Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK, Inpictura Ltd, Abingdon, UK. Electronic address: mark.gooding@inpicturamedica.com
H2020 - 766276 - PREDICT - A new era in personalised medicine: Radiomics as decision support tool for diagnostics and theragnostics in oncology
Funders :
EU - European Union CRUK - Cancer Research UK
Funding text :
This project has received funding from the European Union \u2019s Horizon 2020 research and innovation programme under the Marie Sk\u0142odowska-Curie grant agreement No. 766276 , as well as from the Google Cloud Education Pro- gram for researchers. GH and KAV acknowledge funding support from the CRUK Oxford Radnet Centre ( A28736 ).This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sk\u0142odowska-Curie grant agreement No. 766276, as well as from the Google Cloud Education Program for researchers. GH and KAV acknowledge funding support from the CRUK Oxford Radnet Centre (A28736).
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