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Prediction of sentinel lymph node metastasis in breast cancer by using deep learning radiomics based on ultrasound images

Sentinel lymph node metastasis (SLNM) is a crucial predictor for breast cancer treatment and survival. This study was designed to propose deep learning (DL) models based on grayscale ultrasound, color Doppler flow imaging (CDFI), and elastography images, and to evaluate how DL radiomics can be used...

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Autores principales: Wang, Chujun, Zhao, Yu, Wan, Min, Huang, Long, Liao, Lingmin, Guo, Liangyun, Zhang, Jing, Zhang, Chun-Quan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10627679/
https://www.ncbi.nlm.nih.gov/pubmed/37933063
http://dx.doi.org/10.1097/MD.0000000000035868
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author Wang, Chujun
Zhao, Yu
Wan, Min
Huang, Long
Liao, Lingmin
Guo, Liangyun
Zhang, Jing
Zhang, Chun-Quan
author_facet Wang, Chujun
Zhao, Yu
Wan, Min
Huang, Long
Liao, Lingmin
Guo, Liangyun
Zhang, Jing
Zhang, Chun-Quan
author_sort Wang, Chujun
collection PubMed
description Sentinel lymph node metastasis (SLNM) is a crucial predictor for breast cancer treatment and survival. This study was designed to propose deep learning (DL) models based on grayscale ultrasound, color Doppler flow imaging (CDFI), and elastography images, and to evaluate how DL radiomics can be used to classify SLNM in breast cancer. Clinical and ultrasound data of 317 patients diagnosed with breast cancer at the Second Affiliated Hospital of Nanchang University were collected from January 2018 to December 2021 and randomly divided into training and internal validation cohorts at a ratio of 7:3. An external validation cohort comprising data from Nanchang Third Hospital with 42 patients collected. Three DL models, namely DL-grayscale, DL-CDFI, and DL-elastography, were proposed to predict SLNM by analyzing grayscale ultrasound, CDFI, and elastography images. Three DL models were compared and evaluated to assess diagnostic performance based on the area under the curve (AUC). The AUCs of the DL-grayscale were 0.855 and 0.788 in the internal and external validation cohorts, respectively. For the DL-CDFI model, the AUCs were 0.761 and 0.728, respectively. The diagnostic performance of DL-elastography was superior to that of the DL-grayscale and DL-CDFI. The AUC of the DL-elastography model was 0.879 in the internal validation cohort, with a classification accuracy of 86.13%, sensitivity of 91.60%, and specificity of 82.79%. The generalization capability of DL-elastography remained high in the external cohort, with an AUC of 0.876, and an accuracy of 85.00%. DL radiomics can be used to classify SLNM in breast cancer using ultrasound images. The proposed DL-elastography model based on elastography images achieved the best diagnostic performance and holds good potential for the management of patients with SLNM.
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spelling pubmed-106276792023-11-07 Prediction of sentinel lymph node metastasis in breast cancer by using deep learning radiomics based on ultrasound images Wang, Chujun Zhao, Yu Wan, Min Huang, Long Liao, Lingmin Guo, Liangyun Zhang, Jing Zhang, Chun-Quan Medicine (Baltimore) 6800 Sentinel lymph node metastasis (SLNM) is a crucial predictor for breast cancer treatment and survival. This study was designed to propose deep learning (DL) models based on grayscale ultrasound, color Doppler flow imaging (CDFI), and elastography images, and to evaluate how DL radiomics can be used to classify SLNM in breast cancer. Clinical and ultrasound data of 317 patients diagnosed with breast cancer at the Second Affiliated Hospital of Nanchang University were collected from January 2018 to December 2021 and randomly divided into training and internal validation cohorts at a ratio of 7:3. An external validation cohort comprising data from Nanchang Third Hospital with 42 patients collected. Three DL models, namely DL-grayscale, DL-CDFI, and DL-elastography, were proposed to predict SLNM by analyzing grayscale ultrasound, CDFI, and elastography images. Three DL models were compared and evaluated to assess diagnostic performance based on the area under the curve (AUC). The AUCs of the DL-grayscale were 0.855 and 0.788 in the internal and external validation cohorts, respectively. For the DL-CDFI model, the AUCs were 0.761 and 0.728, respectively. The diagnostic performance of DL-elastography was superior to that of the DL-grayscale and DL-CDFI. The AUC of the DL-elastography model was 0.879 in the internal validation cohort, with a classification accuracy of 86.13%, sensitivity of 91.60%, and specificity of 82.79%. The generalization capability of DL-elastography remained high in the external cohort, with an AUC of 0.876, and an accuracy of 85.00%. DL radiomics can be used to classify SLNM in breast cancer using ultrasound images. The proposed DL-elastography model based on elastography images achieved the best diagnostic performance and holds good potential for the management of patients with SLNM. Lippincott Williams & Wilkins 2023-11-03 /pmc/articles/PMC10627679/ /pubmed/37933063 http://dx.doi.org/10.1097/MD.0000000000035868 Text en Copyright © 2023 the Author(s). Published by Wolters Kluwer Health, Inc. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY) (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle 6800
Wang, Chujun
Zhao, Yu
Wan, Min
Huang, Long
Liao, Lingmin
Guo, Liangyun
Zhang, Jing
Zhang, Chun-Quan
Prediction of sentinel lymph node metastasis in breast cancer by using deep learning radiomics based on ultrasound images
title Prediction of sentinel lymph node metastasis in breast cancer by using deep learning radiomics based on ultrasound images
title_full Prediction of sentinel lymph node metastasis in breast cancer by using deep learning radiomics based on ultrasound images
title_fullStr Prediction of sentinel lymph node metastasis in breast cancer by using deep learning radiomics based on ultrasound images
title_full_unstemmed Prediction of sentinel lymph node metastasis in breast cancer by using deep learning radiomics based on ultrasound images
title_short Prediction of sentinel lymph node metastasis in breast cancer by using deep learning radiomics based on ultrasound images
title_sort prediction of sentinel lymph node metastasis in breast cancer by using deep learning radiomics based on ultrasound images
topic 6800
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10627679/
https://www.ncbi.nlm.nih.gov/pubmed/37933063
http://dx.doi.org/10.1097/MD.0000000000035868
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