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Scalable radiotherapy data curation infrastructure for deep-learning based autosegmentation of organs-at-risk: A case study in head and neck cancer

In this era of patient-centered, outcomes-driven and adaptive radiotherapy, deep learning is now being successfully applied to tackle imaging-related workflow bottlenecks such as autosegmentation and dose planning. These applications typically require supervised learning approaches enabled by relati...

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Autores principales: Tryggestad, E., Anand, A., Beltran, C., Brooks, J., Cimmiyotti, J., Grimaldi, N., Hodge, T., Hunzeker, A., Lucido, J. J., Laack, N. N., Momoh, R., Moseley, D. J., Patel, S. H., Ridgway, A., Seetamsetty, S., Shiraishi, S., Undahl, L., Foote, R. L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9464982/
https://www.ncbi.nlm.nih.gov/pubmed/36106100
http://dx.doi.org/10.3389/fonc.2022.936134
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author Tryggestad, E.
Anand, A.
Beltran, C.
Brooks, J.
Cimmiyotti, J.
Grimaldi, N.
Hodge, T.
Hunzeker, A.
Lucido, J. J.
Laack, N. N.
Momoh, R.
Moseley, D. J.
Patel, S. H.
Ridgway, A.
Seetamsetty, S.
Shiraishi, S.
Undahl, L.
Foote, R. L.
author_facet Tryggestad, E.
Anand, A.
Beltran, C.
Brooks, J.
Cimmiyotti, J.
Grimaldi, N.
Hodge, T.
Hunzeker, A.
Lucido, J. J.
Laack, N. N.
Momoh, R.
Moseley, D. J.
Patel, S. H.
Ridgway, A.
Seetamsetty, S.
Shiraishi, S.
Undahl, L.
Foote, R. L.
author_sort Tryggestad, E.
collection PubMed
description In this era of patient-centered, outcomes-driven and adaptive radiotherapy, deep learning is now being successfully applied to tackle imaging-related workflow bottlenecks such as autosegmentation and dose planning. These applications typically require supervised learning approaches enabled by relatively large, curated radiotherapy datasets which are highly reflective of the contemporary standard of care. However, little has been previously published describing technical infrastructure, recommendations, methods or standards for radiotherapy dataset curation in a holistic fashion. Our radiation oncology department has recently embarked on a large-scale project in partnership with an external partner to develop deep-learning-based tools to assist with our radiotherapy workflow, beginning with autosegmentation of organs-at-risk. This project will require thousands of carefully curated radiotherapy datasets comprising all body sites we routinely treat with radiotherapy. Given such a large project scope, we have approached the need for dataset curation rigorously, with an aim towards building infrastructure that is compatible with efficiency, automation and scalability. Focusing on our first use-case pertaining to head and neck cancer, we describe our developed infrastructure and novel methods applied to radiotherapy dataset curation, inclusive of personnel and workflow organization, dataset selection, expert organ-at-risk segmentation, quality assurance, patient de-identification, data archival and transfer. Over the course of approximately 13 months, our expert multidisciplinary team generated 490 curated head and neck radiotherapy datasets. This task required approximately 6000 human-expert hours in total (not including planning and infrastructure development time). This infrastructure continues to evolve and will support ongoing and future project efforts.
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spelling pubmed-94649822022-09-13 Scalable radiotherapy data curation infrastructure for deep-learning based autosegmentation of organs-at-risk: A case study in head and neck cancer Tryggestad, E. Anand, A. Beltran, C. Brooks, J. Cimmiyotti, J. Grimaldi, N. Hodge, T. Hunzeker, A. Lucido, J. J. Laack, N. N. Momoh, R. Moseley, D. J. Patel, S. H. Ridgway, A. Seetamsetty, S. Shiraishi, S. Undahl, L. Foote, R. L. Front Oncol Oncology In this era of patient-centered, outcomes-driven and adaptive radiotherapy, deep learning is now being successfully applied to tackle imaging-related workflow bottlenecks such as autosegmentation and dose planning. These applications typically require supervised learning approaches enabled by relatively large, curated radiotherapy datasets which are highly reflective of the contemporary standard of care. However, little has been previously published describing technical infrastructure, recommendations, methods or standards for radiotherapy dataset curation in a holistic fashion. Our radiation oncology department has recently embarked on a large-scale project in partnership with an external partner to develop deep-learning-based tools to assist with our radiotherapy workflow, beginning with autosegmentation of organs-at-risk. This project will require thousands of carefully curated radiotherapy datasets comprising all body sites we routinely treat with radiotherapy. Given such a large project scope, we have approached the need for dataset curation rigorously, with an aim towards building infrastructure that is compatible with efficiency, automation and scalability. Focusing on our first use-case pertaining to head and neck cancer, we describe our developed infrastructure and novel methods applied to radiotherapy dataset curation, inclusive of personnel and workflow organization, dataset selection, expert organ-at-risk segmentation, quality assurance, patient de-identification, data archival and transfer. Over the course of approximately 13 months, our expert multidisciplinary team generated 490 curated head and neck radiotherapy datasets. This task required approximately 6000 human-expert hours in total (not including planning and infrastructure development time). This infrastructure continues to evolve and will support ongoing and future project efforts. Frontiers Media S.A. 2022-08-29 /pmc/articles/PMC9464982/ /pubmed/36106100 http://dx.doi.org/10.3389/fonc.2022.936134 Text en Copyright © 2022 Tryggestad, Anand, Beltran, Brooks, Cimmiyotti, Grimaldi, Hodge, Hunzeker, Lucido, Laack, Momoh, Moseley, Patel, Ridgway, Seetamsetty, Shiraishi, Undahl and Foote https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Tryggestad, E.
Anand, A.
Beltran, C.
Brooks, J.
Cimmiyotti, J.
Grimaldi, N.
Hodge, T.
Hunzeker, A.
Lucido, J. J.
Laack, N. N.
Momoh, R.
Moseley, D. J.
Patel, S. H.
Ridgway, A.
Seetamsetty, S.
Shiraishi, S.
Undahl, L.
Foote, R. L.
Scalable radiotherapy data curation infrastructure for deep-learning based autosegmentation of organs-at-risk: A case study in head and neck cancer
title Scalable radiotherapy data curation infrastructure for deep-learning based autosegmentation of organs-at-risk: A case study in head and neck cancer
title_full Scalable radiotherapy data curation infrastructure for deep-learning based autosegmentation of organs-at-risk: A case study in head and neck cancer
title_fullStr Scalable radiotherapy data curation infrastructure for deep-learning based autosegmentation of organs-at-risk: A case study in head and neck cancer
title_full_unstemmed Scalable radiotherapy data curation infrastructure for deep-learning based autosegmentation of organs-at-risk: A case study in head and neck cancer
title_short Scalable radiotherapy data curation infrastructure for deep-learning based autosegmentation of organs-at-risk: A case study in head and neck cancer
title_sort scalable radiotherapy data curation infrastructure for deep-learning based autosegmentation of organs-at-risk: a case study in head and neck cancer
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9464982/
https://www.ncbi.nlm.nih.gov/pubmed/36106100
http://dx.doi.org/10.3389/fonc.2022.936134
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