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Automatic Segmentation of Clinical Target Volumes for Post-Modified Radical Mastectomy Radiotherapy Using Convolutional Neural Networks
BACKGROUND: This study aims to construct and validate a model based on convolutional neural networks (CNNs), which can fulfil the automatic segmentation of clinical target volumes (CTVs) of breast cancer for radiotherapy. METHODS: In this work, computed tomography (CT) scans of 110 patients who unde...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7921705/ https://www.ncbi.nlm.nih.gov/pubmed/33665160 http://dx.doi.org/10.3389/fonc.2020.581347 |
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author | Liu, Zhikai Liu, Fangjie Chen, Wanqi Liu, Xia Hou, Xiaorong Shen, Jing Guan, Hui Zhen, Hongnan Wang, Shaobin Chen, Qi Chen, Yu Zhang, Fuquan |
author_facet | Liu, Zhikai Liu, Fangjie Chen, Wanqi Liu, Xia Hou, Xiaorong Shen, Jing Guan, Hui Zhen, Hongnan Wang, Shaobin Chen, Qi Chen, Yu Zhang, Fuquan |
author_sort | Liu, Zhikai |
collection | PubMed |
description | BACKGROUND: This study aims to construct and validate a model based on convolutional neural networks (CNNs), which can fulfil the automatic segmentation of clinical target volumes (CTVs) of breast cancer for radiotherapy. METHODS: In this work, computed tomography (CT) scans of 110 patients who underwent modified radical mastectomies were collected. The CTV contours were confirmed by two experienced oncologists. A novel CNN was constructed to automatically delineate the CTV. Quantitative evaluation metrics were calculated, and a clinical evaluation was conducted to evaluate the performance of our model. RESULTS: The mean Dice similarity coefficient (DSC) of the proposed model was 0.90, and the 95th percentile Hausdorff distance (95HD) was 5.65 mm. The evaluation results of the two clinicians showed that 99.3% of the chest wall CTV slices could be accepted by clinician A, and this number was 98.9% for clinician B. In addition, 9/10 of patients had all slices accepted by clinician A, while 7/10 could be accepted by clinician B. The score differences between the AI (artificial intelligence) group and the GT (ground truth) group showed no statistically significant difference for either clinician. However, the score differences in the AI group were significantly different between the two clinicians. The Kappa consistency index was 0.259. It took 3.45 s to delineate the chest wall CTV using the model. CONCLUSION: Our model could automatically generate the CTVs for breast cancer. AI-generated structures of the proposed model showed a trend that was comparable, or was even better, than those of human-generated structures. Additional multicentre evaluations should be performed for adequate validation before the model can be completely applied in clinical practice. |
format | Online Article Text |
id | pubmed-7921705 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-79217052021-03-03 Automatic Segmentation of Clinical Target Volumes for Post-Modified Radical Mastectomy Radiotherapy Using Convolutional Neural Networks Liu, Zhikai Liu, Fangjie Chen, Wanqi Liu, Xia Hou, Xiaorong Shen, Jing Guan, Hui Zhen, Hongnan Wang, Shaobin Chen, Qi Chen, Yu Zhang, Fuquan Front Oncol Oncology BACKGROUND: This study aims to construct and validate a model based on convolutional neural networks (CNNs), which can fulfil the automatic segmentation of clinical target volumes (CTVs) of breast cancer for radiotherapy. METHODS: In this work, computed tomography (CT) scans of 110 patients who underwent modified radical mastectomies were collected. The CTV contours were confirmed by two experienced oncologists. A novel CNN was constructed to automatically delineate the CTV. Quantitative evaluation metrics were calculated, and a clinical evaluation was conducted to evaluate the performance of our model. RESULTS: The mean Dice similarity coefficient (DSC) of the proposed model was 0.90, and the 95th percentile Hausdorff distance (95HD) was 5.65 mm. The evaluation results of the two clinicians showed that 99.3% of the chest wall CTV slices could be accepted by clinician A, and this number was 98.9% for clinician B. In addition, 9/10 of patients had all slices accepted by clinician A, while 7/10 could be accepted by clinician B. The score differences between the AI (artificial intelligence) group and the GT (ground truth) group showed no statistically significant difference for either clinician. However, the score differences in the AI group were significantly different between the two clinicians. The Kappa consistency index was 0.259. It took 3.45 s to delineate the chest wall CTV using the model. CONCLUSION: Our model could automatically generate the CTVs for breast cancer. AI-generated structures of the proposed model showed a trend that was comparable, or was even better, than those of human-generated structures. Additional multicentre evaluations should be performed for adequate validation before the model can be completely applied in clinical practice. Frontiers Media S.A. 2021-02-16 /pmc/articles/PMC7921705/ /pubmed/33665160 http://dx.doi.org/10.3389/fonc.2020.581347 Text en Copyright © 2021 Liu, Liu, Chen, Liu, Hou, Shen, Guan, Zhen, Wang, Chen, Chen and Zhang http://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 Liu, Zhikai Liu, Fangjie Chen, Wanqi Liu, Xia Hou, Xiaorong Shen, Jing Guan, Hui Zhen, Hongnan Wang, Shaobin Chen, Qi Chen, Yu Zhang, Fuquan Automatic Segmentation of Clinical Target Volumes for Post-Modified Radical Mastectomy Radiotherapy Using Convolutional Neural Networks |
title | Automatic Segmentation of Clinical Target Volumes for Post-Modified Radical Mastectomy Radiotherapy Using Convolutional Neural Networks |
title_full | Automatic Segmentation of Clinical Target Volumes for Post-Modified Radical Mastectomy Radiotherapy Using Convolutional Neural Networks |
title_fullStr | Automatic Segmentation of Clinical Target Volumes for Post-Modified Radical Mastectomy Radiotherapy Using Convolutional Neural Networks |
title_full_unstemmed | Automatic Segmentation of Clinical Target Volumes for Post-Modified Radical Mastectomy Radiotherapy Using Convolutional Neural Networks |
title_short | Automatic Segmentation of Clinical Target Volumes for Post-Modified Radical Mastectomy Radiotherapy Using Convolutional Neural Networks |
title_sort | automatic segmentation of clinical target volumes for post-modified radical mastectomy radiotherapy using convolutional neural networks |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7921705/ https://www.ncbi.nlm.nih.gov/pubmed/33665160 http://dx.doi.org/10.3389/fonc.2020.581347 |
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