TY - JOUR
T1 - Scalable radiotherapy data curation infrastructure for deep-learning based autosegmentation of organs-at-risk
T2 - A case study in head and neck cancer
AU - Tryggestad, E.
AU - Anand, A.
AU - Beltran, C.
AU - Brooks, J.
AU - Cimmiyotti, J.
AU - Grimaldi, N.
AU - Hodge, T.
AU - Hunzeker, A.
AU - Lucido, J. J.
AU - Laack, N. N.
AU - Momoh, R.
AU - Moseley, D. J.
AU - Patel, S. H.
AU - Ridgway, A.
AU - Seetamsetty, S.
AU - Shiraishi, S.
AU - Undahl, L.
AU - Foote, R. L.
N1 - Publisher Copyright:
Copyright © 2022 Tryggestad, Anand, Beltran, Brooks, Cimmiyotti, Grimaldi, Hodge, Hunzeker, Lucido, Laack, Momoh, Moseley, Patel, Ridgway, Seetamsetty, Shiraishi, Undahl and Foote.
PY - 2022/8/29
Y1 - 2022/8/29
N2 - 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.
AB - 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.
KW - artificial intelligence
KW - autosegmentation
KW - convolutional neural network
KW - curation
KW - deep learning
KW - head and neck cancer
KW - organs-at-risk
KW - radiotherapy
UR - http://www.scopus.com/inward/record.url?scp=85138020178&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85138020178&partnerID=8YFLogxK
U2 - 10.3389/fonc.2022.936134
DO - 10.3389/fonc.2022.936134
M3 - Article
AN - SCOPUS:85138020178
SN - 2234-943X
VL - 12
JO - Frontiers in Oncology
JF - Frontiers in Oncology
M1 - 936134
ER -