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Development and validation of a fully automatic tissue delineation model for brain metastasis using a deep neural network

BACKGROUND: Stereotactic radiosurgery (SRS) treatment planning requires accurate delineation of brain metastases, a task that can be tedious and time-consuming. Although studies have explored the use of convolutional neural networks (CNNs) in magnetic resonance imaging (MRI) for automatic brain meta...

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Autores principales: Zhao, Jie-Yi, Cao, Qi, Chen, Jing, Chen, Wei, Du, Si-Yu, Yu, Jie, Zeng, Yi-Miao, Wang, Shu-Min, Peng, Jing-Yu, You, Chao, Xu, Jian-Guo, Wang, Xiao-Yu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10585546/
https://www.ncbi.nlm.nih.gov/pubmed/37869331
http://dx.doi.org/10.21037/qims-22-1216
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author Zhao, Jie-Yi
Cao, Qi
Chen, Jing
Chen, Wei
Du, Si-Yu
Yu, Jie
Zeng, Yi-Miao
Wang, Shu-Min
Peng, Jing-Yu
You, Chao
Xu, Jian-Guo
Wang, Xiao-Yu
author_facet Zhao, Jie-Yi
Cao, Qi
Chen, Jing
Chen, Wei
Du, Si-Yu
Yu, Jie
Zeng, Yi-Miao
Wang, Shu-Min
Peng, Jing-Yu
You, Chao
Xu, Jian-Guo
Wang, Xiao-Yu
author_sort Zhao, Jie-Yi
collection PubMed
description BACKGROUND: Stereotactic radiosurgery (SRS) treatment planning requires accurate delineation of brain metastases, a task that can be tedious and time-consuming. Although studies have explored the use of convolutional neural networks (CNNs) in magnetic resonance imaging (MRI) for automatic brain metastases delineation, none of these studies have performed clinical evaluation, raising concerns about clinical applicability. This study aimed to develop an artificial intelligence (AI) tool for the automatic delineation of single brain metastasis that could be integrated into clinical practice. METHODS: Data from 426 patients with postcontrast T1-weighted MRIs who underwent SRS between March 2007 and August 2019 were retrospectively collected and divided into training, validation, and testing cohorts of 299, 42, and 85 patients, respectively. Two Gamma Knife (GK) surgeons contoured the brain metastases as the ground truth. A novel 2.5D CNN network was developed for single brain metastasis delineation. The mean Dice similarity coefficient (DSC) and average surface distance (ASD) were used to assess the performance of this method. RESULTS: The mean DSC and ASD values were 88.34%±5.00% and 0.35±0.21 mm, respectively, for the contours generated with the AI tool based on the testing set. The DSC measure of the AI tool’s performance was dependent on metastatic shape, reinforcement shape, and the existence of peritumoral edema (all P values <0.05). The clinical experts’ subjective assessments showed that 415 out of 572 slices (72.6%) in the testing cohort were acceptable for clinical usage without revision. The average time spent editing an AI-generated contour compared with time spent with manual contouring was 74 vs. 196 seconds, respectively (P<0.01). CONCLUSIONS: The contours delineated with the AI tool for single brain metastasis were in close agreement with the ground truth. The developed AI tool can effectively reduce contouring time and aid in GK treatment planning of single brain metastasis in clinical practice.
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spelling pubmed-105855462023-10-20 Development and validation of a fully automatic tissue delineation model for brain metastasis using a deep neural network Zhao, Jie-Yi Cao, Qi Chen, Jing Chen, Wei Du, Si-Yu Yu, Jie Zeng, Yi-Miao Wang, Shu-Min Peng, Jing-Yu You, Chao Xu, Jian-Guo Wang, Xiao-Yu Quant Imaging Med Surg Original Article BACKGROUND: Stereotactic radiosurgery (SRS) treatment planning requires accurate delineation of brain metastases, a task that can be tedious and time-consuming. Although studies have explored the use of convolutional neural networks (CNNs) in magnetic resonance imaging (MRI) for automatic brain metastases delineation, none of these studies have performed clinical evaluation, raising concerns about clinical applicability. This study aimed to develop an artificial intelligence (AI) tool for the automatic delineation of single brain metastasis that could be integrated into clinical practice. METHODS: Data from 426 patients with postcontrast T1-weighted MRIs who underwent SRS between March 2007 and August 2019 were retrospectively collected and divided into training, validation, and testing cohorts of 299, 42, and 85 patients, respectively. Two Gamma Knife (GK) surgeons contoured the brain metastases as the ground truth. A novel 2.5D CNN network was developed for single brain metastasis delineation. The mean Dice similarity coefficient (DSC) and average surface distance (ASD) were used to assess the performance of this method. RESULTS: The mean DSC and ASD values were 88.34%±5.00% and 0.35±0.21 mm, respectively, for the contours generated with the AI tool based on the testing set. The DSC measure of the AI tool’s performance was dependent on metastatic shape, reinforcement shape, and the existence of peritumoral edema (all P values <0.05). The clinical experts’ subjective assessments showed that 415 out of 572 slices (72.6%) in the testing cohort were acceptable for clinical usage without revision. The average time spent editing an AI-generated contour compared with time spent with manual contouring was 74 vs. 196 seconds, respectively (P<0.01). CONCLUSIONS: The contours delineated with the AI tool for single brain metastasis were in close agreement with the ground truth. The developed AI tool can effectively reduce contouring time and aid in GK treatment planning of single brain metastasis in clinical practice. AME Publishing Company 2023-08-31 2023-10-01 /pmc/articles/PMC10585546/ /pubmed/37869331 http://dx.doi.org/10.21037/qims-22-1216 Text en 2023 Quantitative Imaging in Medicine and Surgery. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Original Article
Zhao, Jie-Yi
Cao, Qi
Chen, Jing
Chen, Wei
Du, Si-Yu
Yu, Jie
Zeng, Yi-Miao
Wang, Shu-Min
Peng, Jing-Yu
You, Chao
Xu, Jian-Guo
Wang, Xiao-Yu
Development and validation of a fully automatic tissue delineation model for brain metastasis using a deep neural network
title Development and validation of a fully automatic tissue delineation model for brain metastasis using a deep neural network
title_full Development and validation of a fully automatic tissue delineation model for brain metastasis using a deep neural network
title_fullStr Development and validation of a fully automatic tissue delineation model for brain metastasis using a deep neural network
title_full_unstemmed Development and validation of a fully automatic tissue delineation model for brain metastasis using a deep neural network
title_short Development and validation of a fully automatic tissue delineation model for brain metastasis using a deep neural network
title_sort development and validation of a fully automatic tissue delineation model for brain metastasis using a deep neural network
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10585546/
https://www.ncbi.nlm.nih.gov/pubmed/37869331
http://dx.doi.org/10.21037/qims-22-1216
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