Metrics to evaluate the performance of auto-segmentation for radiation treatment planning: A critical review.


Journal

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
ISSN: 1879-0887
Titre abrégé: Radiother Oncol
Pays: Ireland
ID NLM: 8407192

Informations de publication

Date de publication:
07 2021
Historique:
received: 18 11 2020
revised: 01 05 2021
accepted: 03 05 2021
pubmed: 14 5 2021
medline: 28 7 2021
entrez: 13 5 2021
Statut: ppublish

Résumé

Advances in artificial intelligence-based methods have led to the development and publication of numerous systems for auto-segmentation in radiotherapy. These systems have the potential to decrease contour variability, which has been associated with poor clinical outcomes and increased efficiency in the treatment planning workflow. However, there are no uniform standards for evaluating auto-segmentation platforms to assess their efficacy at meeting these goals. Here, we review the most frequently used evaluation techniques which include geometric overlap, dosimetric parameters, time spent contouring, and clinical rating scales. These data suggest that many of the most commonly used geometric indices, such as the Dice Similarity Coefficient, are not well correlated with clinically meaningful endpoints. As such, a multi-domain evaluation, including composite geometric and/or dosimetric metrics with physician-reported assessment, is necessary to gauge the clinical readiness of auto-segmentation for radiation treatment planning.

Identifiants

pubmed: 33984348
pii: S0167-8140(21)06228-9
doi: 10.1016/j.radonc.2021.05.003
pmc: PMC9444281
mid: NIHMS1832301
pii:
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, U.S. Gov't, P.H.S. Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

185-191

Subventions

Organisme : NCI NIH HHS
ID : P30 CA008748
Pays : United States
Organisme : AHRQ HHS
ID : R18 HS026881
Pays : United States

Informations de copyright

Copyright © 2021 Elsevier B.V. All rights reserved.

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Auteurs

Michael V Sherer (MV)

Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, United States.

Diana Lin (D)

Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, United States.

Sharif Elguindi (S)

Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, United States.

Simon Duke (S)

Department of Oncology, Cambridge University Hospitals, United Kingdom.

Li-Tee Tan (LT)

Department of Oncology, Cambridge University Hospitals, United Kingdom.

Jon Cacicedo (J)

Department of Radiation Oncology, Cruces University Hospital/BioCruces Health Research Institute, Osakidetza, Barakaldo, Spain.

Max Dahele (M)

Department of Radiation Oncology, Amsterdam University Medical Center, Amsterdam, The Netherlands.

Erin F Gillespie (EF)

Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, United States. Electronic address: efgillespie@ucsd.edu.

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