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Computed tomography-based deep-learning prediction of lymph node metastasis risk in locally advanced gastric cancer
PURPOSE: Preoperative evaluation of lymph node metastasis (LNM) is the basis of personalized treatment of locally advanced gastric cancer (LAGC). We aim to develop and evaluate CT-based model using deep learning features to preoperatively predict LNM in LAGC. METHODS: A combined size of 523 patients...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9537615/ https://www.ncbi.nlm.nih.gov/pubmed/36212443 http://dx.doi.org/10.3389/fonc.2022.969707 |
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author | Zhang, An-qi Zhao, Hui-ping Li, Fei Liang, Pan Gao, Jian-bo Cheng, Ming |
author_facet | Zhang, An-qi Zhao, Hui-ping Li, Fei Liang, Pan Gao, Jian-bo Cheng, Ming |
author_sort | Zhang, An-qi |
collection | PubMed |
description | PURPOSE: Preoperative evaluation of lymph node metastasis (LNM) is the basis of personalized treatment of locally advanced gastric cancer (LAGC). We aim to develop and evaluate CT-based model using deep learning features to preoperatively predict LNM in LAGC. METHODS: A combined size of 523 patients who had pathologically confirmed LAGC were retrospectively collected between August 2012 and July 2019 from our hospital. Five pre-trained convolutional neural networks were exploited to extract deep learning features from pretreatment CT images. And the support vector machine (SVM) was employed as the classifier. We assessed the performance using the area under the receiver operating characteristics curve (AUC) and selected an optimal model, which was compared with a radiomics model developed from the training cohort. A clinical model was built with clinical factors only for baseline comparison. RESULTS: The optimal model with features extracted from ResNet yielded better performance with AUC of 0.796 [95% confidence interval (95% CI), 0.715-0.865] and accuracy of 75.2% (95% CI, 67.2%-81.5%) in the testing cohort, compared with 0.704 (0.625-0.783) and 61.8% (54.5%-69.9%) for the radiomics model. The predictive performance of all the radiological models were significantly better than the clinical model. CONCLUSION: The novel and noninvasive deep learning approach could provide efficient and accurate prediction of lymph node metastasis in LAGC, and benefit clinical decision making of therapeutic strategy. |
format | Online Article Text |
id | pubmed-9537615 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95376152022-10-08 Computed tomography-based deep-learning prediction of lymph node metastasis risk in locally advanced gastric cancer Zhang, An-qi Zhao, Hui-ping Li, Fei Liang, Pan Gao, Jian-bo Cheng, Ming Front Oncol Oncology PURPOSE: Preoperative evaluation of lymph node metastasis (LNM) is the basis of personalized treatment of locally advanced gastric cancer (LAGC). We aim to develop and evaluate CT-based model using deep learning features to preoperatively predict LNM in LAGC. METHODS: A combined size of 523 patients who had pathologically confirmed LAGC were retrospectively collected between August 2012 and July 2019 from our hospital. Five pre-trained convolutional neural networks were exploited to extract deep learning features from pretreatment CT images. And the support vector machine (SVM) was employed as the classifier. We assessed the performance using the area under the receiver operating characteristics curve (AUC) and selected an optimal model, which was compared with a radiomics model developed from the training cohort. A clinical model was built with clinical factors only for baseline comparison. RESULTS: The optimal model with features extracted from ResNet yielded better performance with AUC of 0.796 [95% confidence interval (95% CI), 0.715-0.865] and accuracy of 75.2% (95% CI, 67.2%-81.5%) in the testing cohort, compared with 0.704 (0.625-0.783) and 61.8% (54.5%-69.9%) for the radiomics model. The predictive performance of all the radiological models were significantly better than the clinical model. CONCLUSION: The novel and noninvasive deep learning approach could provide efficient and accurate prediction of lymph node metastasis in LAGC, and benefit clinical decision making of therapeutic strategy. Frontiers Media S.A. 2022-09-23 /pmc/articles/PMC9537615/ /pubmed/36212443 http://dx.doi.org/10.3389/fonc.2022.969707 Text en Copyright © 2022 Zhang, Zhao, Li, Liang, Gao and Cheng 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 Zhang, An-qi Zhao, Hui-ping Li, Fei Liang, Pan Gao, Jian-bo Cheng, Ming Computed tomography-based deep-learning prediction of lymph node metastasis risk in locally advanced gastric cancer |
title | Computed tomography-based deep-learning prediction of lymph node metastasis risk in locally advanced gastric cancer |
title_full | Computed tomography-based deep-learning prediction of lymph node metastasis risk in locally advanced gastric cancer |
title_fullStr | Computed tomography-based deep-learning prediction of lymph node metastasis risk in locally advanced gastric cancer |
title_full_unstemmed | Computed tomography-based deep-learning prediction of lymph node metastasis risk in locally advanced gastric cancer |
title_short | Computed tomography-based deep-learning prediction of lymph node metastasis risk in locally advanced gastric cancer |
title_sort | computed tomography-based deep-learning prediction of lymph node metastasis risk in locally advanced gastric cancer |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9537615/ https://www.ncbi.nlm.nih.gov/pubmed/36212443 http://dx.doi.org/10.3389/fonc.2022.969707 |
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