Retrospective Validation and Clinical Implementation of Automated Contouring of Organs at Risk in the Head and Neck: A Step Toward Automated Radiation Treatment Planning for Low- and Middle-Income Countries Article Swipe
Rachel McCarroll
,
Beth M. Beadle
,
Peter Balter
,
Hester Burger
,
Carlos Cárdenas
,
Sameera Dalvie
,
D Followill
,
Kelly Kisling
,
Michael Benedict A. Mejia
,
Komeela Naidoo
,
Chris L. Nelson
,
Christine B. Peterson
,
Karin Vorster
,
Julie Wetter
,
Lifei Zhang
,
Laurence E. Court
,
Jinzhong Yang
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1200/jgo.18.00055
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1200/jgo.18.00055
Purpose We assessed automated contouring of normal structures for patients with head-and-neck cancer (HNC) using a multiatlas deformable-image-registration algorithm to better provide a fully automated radiation treatment planning solution for low- and middle-income countries, provide quantitative analysis, and determine acceptability worldwide. Methods Autocontours of eight normal structures (brain, brainstem, cochleae, eyes, lungs, mandible, parotid glands, and spinal cord) from 128 patients with HNC were retrospectively scored by a dedicated HNC radiation oncologist. Contours from a 10-patient subset were evaluated by five additional radiation oncologists from international partner institutions, and interphysician variability was assessed. Quantitative agreement of autocontours with independently physician-drawn structures was assessed using the Dice similarity coefficient and mean surface and Hausdorff distances. Automated contouring was then implemented clinically and has been used for 166 patients, and contours were quantitatively compared with the physician-edited autocontours using the same metrics. Results Retrospectively, 87% of normal structure contours were rated as acceptable for use in dose-volume-histogram–based planning without edit. Upon clinical implementation, 50% of contours were not edited for use in treatment planning. The mean (± standard deviation) Dice similarity coefficient of autocontours compared with physician-edited autocontours for parotid glands (0.92 ± 0.10), brainstem (0.95 ± 0.09), and spinal cord (0.92 ± 0.12) indicate that only minor edits were performed. The average mean surface and Hausdorff distances for all structures were less than 0.15 mm and 1.8 mm, respectively. Conclusion Automated contouring of normal structures generates reliable contours that require only minimal editing, as judged by retrospective ratings from multiple international centers and clinical integration. Autocontours are acceptable for treatment planning with no or, at most, minor edits, suggesting that automated contouring is feasible for clinical use and in the ongoing development of automated radiation treatment planning algorithms.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1200/jgo.18.00055
- https://ascopubs.org/doi/pdfdirect/10.1200/JGO.18.00055
- OA Status
- gold
- Cited By
- 56
- References
- 34
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2885048735
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2885048735Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1200/jgo.18.00055Digital Object Identifier
- Title
-
Retrospective Validation and Clinical Implementation of Automated Contouring of Organs at Risk in the Head and Neck: A Step Toward Automated Radiation Treatment Planning for Low- and Middle-Income CountriesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2018Year of publication
- Publication date
-
2018-08-15Full publication date if available
- Authors
-
Rachel McCarroll, Beth M. Beadle, Peter Balter, Hester Burger, Carlos Cárdenas, Sameera Dalvie, D Followill, Kelly Kisling, Michael Benedict A. Mejia, Komeela Naidoo, Chris L. Nelson, Christine B. Peterson, Karin Vorster, Julie Wetter, Lifei Zhang, Laurence E. Court, Jinzhong YangList of authors in order
- Landing page
-
https://doi.org/10.1200/jgo.18.00055Publisher landing page
- PDF URL
-
https://ascopubs.org/doi/pdfdirect/10.1200/JGO.18.00055Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://ascopubs.org/doi/pdfdirect/10.1200/JGO.18.00055Direct OA link when available
- Concepts
-
Contouring, Medicine, Radiation oncologist, Radiation treatment planning, Head and neck cancer, Radiation therapy, Hausdorff distance, Nuclear medicine, Radiology, Low and middle income countries, Artificial intelligence, Computer science, Developing country, Economics, Economic growth, Computer graphics (images)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
56Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 9, 2023: 11, 2022: 10, 2021: 10, 2020: 10Per-year citation counts (last 5 years)
- References (count)
-
34Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.agreement | 94 |
| abstract_inverted_index.algorithm | 18 |
| abstract_inverted_index.analysis, | 36 |
| abstract_inverted_index.assessed. | 92 |
| abstract_inverted_index.automated | 3, 24, 269, 282 |
| abstract_inverted_index.brainstem | 192 |
| abstract_inverted_index.cochleae, | 49 |
| abstract_inverted_index.dedicated | 68 |
| abstract_inverted_index.determine | 38 |
| abstract_inverted_index.distances | 215 |
| abstract_inverted_index.evaluated | 78 |
| abstract_inverted_index.generates | 234 |
| abstract_inverted_index.mandible, | 52 |
| abstract_inverted_index.patients, | 126 |
| abstract_inverted_index.planning. | 171 |
| abstract_inverted_index.radiation | 25, 70, 82, 283 |
| abstract_inverted_index.structure | 145 |
| abstract_inverted_index.treatment | 26, 170, 258, 284 |
| abstract_inverted_index.10-patient | 75 |
| abstract_inverted_index.Conclusion | 228 |
| abstract_inverted_index.acceptable | 150, 256 |
| abstract_inverted_index.additional | 81 |
| abstract_inverted_index.brainstem, | 48 |
| abstract_inverted_index.clinically | 119 |
| abstract_inverted_index.contouring | 4, 115, 230, 270 |
| abstract_inverted_index.countries, | 33 |
| abstract_inverted_index.deviation) | 176 |
| abstract_inverted_index.distances. | 113 |
| abstract_inverted_index.multiatlas | 16 |
| abstract_inverted_index.performed. | 208 |
| abstract_inverted_index.similarity | 106, 178 |
| abstract_inverted_index.structures | 7, 46, 100, 218, 233 |
| abstract_inverted_index.suggesting | 267 |
| abstract_inverted_index.worldwide. | 40 |
| abstract_inverted_index.algorithms. | 286 |
| abstract_inverted_index.coefficient | 107, 179 |
| abstract_inverted_index.development | 280 |
| abstract_inverted_index.implemented | 118 |
| abstract_inverted_index.oncologist. | 71 |
| abstract_inverted_index.oncologists | 83 |
| abstract_inverted_index.variability | 90 |
| abstract_inverted_index.Autocontours | 42, 254 |
| abstract_inverted_index.Quantitative | 93 |
| abstract_inverted_index.autocontours | 96, 135, 181, 185 |
| abstract_inverted_index.integration. | 253 |
| abstract_inverted_index.quantitative | 35 |
| abstract_inverted_index.acceptability | 39 |
| abstract_inverted_index.head-and-neck | 11 |
| abstract_inverted_index.independently | 98 |
| abstract_inverted_index.institutions, | 87 |
| abstract_inverted_index.international | 85, 249 |
| abstract_inverted_index.middle-income | 32 |
| abstract_inverted_index.respectively. | 227 |
| abstract_inverted_index.retrospective | 245 |
| abstract_inverted_index.interphysician | 89 |
| abstract_inverted_index.quantitatively | 130 |
| abstract_inverted_index.implementation, | 160 |
| abstract_inverted_index.physician-drawn | 99 |
| abstract_inverted_index.retrospectively | 64 |
| abstract_inverted_index.Retrospectively, | 141 |
| abstract_inverted_index.physician-edited | 134, 184 |
| abstract_inverted_index.deformable-image-registration | 17 |
| abstract_inverted_index.dose-volume-histogram–based | 154 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 94 |
| corresponding_author_ids | https://openalex.org/A5066224894 |
| countries_distinct_count | 5 |
| institutions_distinct_count | 17 |
| corresponding_institution_ids | https://openalex.org/I1282355914, https://openalex.org/I1343551460, https://openalex.org/I157614274, https://openalex.org/I26092322, https://openalex.org/I26999989, https://openalex.org/I2802694459, https://openalex.org/I4210109159, https://openalex.org/I4210112800, https://openalex.org/I4210135154, https://openalex.org/I4210157068, https://openalex.org/I46603895, https://openalex.org/I97018004 |
| citation_normalized_percentile.value | 0.98041174 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |