Deep learning-assisted interactive contouring of lung cancer: Impact on contouring time and consistency-Reference-Cited by-同舟云学术

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Deep learning-assisted interactive contouring of lung cancer: Impact on contouring time and consistency

Published:2024-11 Issue: Volume:200 Page:110500
ISSN:0167-8140
Container-title:Radiotherapy and Oncology
language:en
Short-container-title:Radiotherapy and Oncology

Author:

Trimpl Michael J.^ORCID,Campbell Sorcha,Panakis Niki,Ajzensztejn Daniel,Burke Emma,Ellis Shawn^ORCID,Johnstone Philippa^ORCID,Doyle Emma^ORCID,Towers Rebecca,Higgins Geoffrey,Bernard Claire,Hustinx Roland,Vallis Katherine A.,Stride Eleanor P.J.,Gooding Mark J.^ORCID

Funder

Horizon 2020 Marie Skłodowska-Curie Actions

CRUK

Publisher

Elsevier BV

Reference42 articles.

1. Comprehensive quantitative evaluation of variability in magnetic resonance-guided delineation of oropharyngeal gross tumor volumes and high-risk clinical target volumes: An r-ideal stage 0 prospective study;Cardenas;Int. J. Radiat. Oncol. Biol. Phys.,2022

2. Tumor delineation: The weakest link in the search for accuracy in radiotherapy;Njeh;J. Med. Phys.,2008

3. Intra- and inter-physician variability in target volume delineation in radiation therapy;Das;J. Radiat. Res.,2021

4. A review of interventions to reduce inter-observer variability in volume delineation in radiation oncology;Vinod;J. Med. Imaging Radiat. Oncol.,2016

5. Impact of fdg-pet on lung cancer delineation for radiotherapy;Morarji;J. Med. Imaging Radiat. Oncol.,2012

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