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Automated localization and segmentation of cervical lymph nodes on contrast-enhanced CT using a 3D foveal fully convolutional neural network

BACKGROUND: In the management of cancer patients, determination of TNM status is essential for treatment decision-making and therefore closely linked to clinical outcome and survival. Here, we developed a tool for automatic three-dimensional (3D) localization and segmentation of cervical lymph nodes...

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Autores principales: Rinneburger, Miriam, Carolus, Heike, Iuga, Andra-Iza, Weisthoff, Mathilda, Lennartz, Simon, Hokamp, Nils Große, Caldeira, Liliana, Shahzad, Rahil, Maintz, David, Laqua, Fabian Christopher, Baeßler, Bettina, Klinder, Tobias, Persigehl, Thorsten
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Vienna 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10382409/
https://www.ncbi.nlm.nih.gov/pubmed/37505296
http://dx.doi.org/10.1186/s41747-023-00360-x
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author Rinneburger, Miriam
Carolus, Heike
Iuga, Andra-Iza
Weisthoff, Mathilda
Lennartz, Simon
Hokamp, Nils Große
Caldeira, Liliana
Shahzad, Rahil
Maintz, David
Laqua, Fabian Christopher
Baeßler, Bettina
Klinder, Tobias
Persigehl, Thorsten
author_facet Rinneburger, Miriam
Carolus, Heike
Iuga, Andra-Iza
Weisthoff, Mathilda
Lennartz, Simon
Hokamp, Nils Große
Caldeira, Liliana
Shahzad, Rahil
Maintz, David
Laqua, Fabian Christopher
Baeßler, Bettina
Klinder, Tobias
Persigehl, Thorsten
author_sort Rinneburger, Miriam
collection PubMed
description BACKGROUND: In the management of cancer patients, determination of TNM status is essential for treatment decision-making and therefore closely linked to clinical outcome and survival. Here, we developed a tool for automatic three-dimensional (3D) localization and segmentation of cervical lymph nodes (LNs) on contrast-enhanced computed tomography (CECT) examinations. METHODS: In this IRB-approved retrospective single-center study, 187 CECT examinations of the head and neck region from patients with various primary diseases were collected from our local database, and 3656 LNs (19.5 ± 14.9 LNs/CECT, mean ± standard deviation) with a short-axis diameter (SAD) ≥ 5 mm were segmented manually by expert physicians. With these data, we trained an independent fully convolutional neural network based on 3D foveal patches. Testing was performed on 30 independent CECTs with 925 segmented LNs with an SAD ≥ 5 mm. RESULTS: In total, 4,581 LNs were segmented in 217 CECTs. The model achieved an average localization rate (LR), i.e., percentage of localized LNs/CECT, of 78.0% in the validation dataset. In the test dataset, average LR was 81.1% with a mean Dice coefficient of 0.71. For enlarged LNs with a SAD ≥ 10 mm, LR was 96.2%. In the test dataset, the false-positive rate was 2.4 LNs/CECT. CONCLUSIONS: Our trained AI model demonstrated a good overall performance in the consistent automatic localization and 3D segmentation of physiological and metastatic cervical LNs with a SAD ≥ 5 mm on CECTs. This could aid clinical localization and automatic 3D segmentation, which can benefit clinical care and radiomics research. RELEVANCE STATEMENT: Our AI model is a time-saving tool for 3D segmentation of cervical lymph nodes on contrast-enhanced CT scans and serves as a solid base for N staging in clinical practice and further radiomics research. KEY POINTS: • Determination of N status in TNM staging is essential for therapy planning in oncology. • Segmenting cervical lymph nodes manually is highly time-consuming in clinical practice. • Our model provides a robust, automated 3D segmentation of cervical lymph nodes. • It achieves a high accuracy for localization especially of enlarged lymph nodes. • These segmentations should assist clinical care and radiomics research. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s41747-023-00360-x.
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spelling pubmed-103824092023-07-30 Automated localization and segmentation of cervical lymph nodes on contrast-enhanced CT using a 3D foveal fully convolutional neural network Rinneburger, Miriam Carolus, Heike Iuga, Andra-Iza Weisthoff, Mathilda Lennartz, Simon Hokamp, Nils Große Caldeira, Liliana Shahzad, Rahil Maintz, David Laqua, Fabian Christopher Baeßler, Bettina Klinder, Tobias Persigehl, Thorsten Eur Radiol Exp Original Article BACKGROUND: In the management of cancer patients, determination of TNM status is essential for treatment decision-making and therefore closely linked to clinical outcome and survival. Here, we developed a tool for automatic three-dimensional (3D) localization and segmentation of cervical lymph nodes (LNs) on contrast-enhanced computed tomography (CECT) examinations. METHODS: In this IRB-approved retrospective single-center study, 187 CECT examinations of the head and neck region from patients with various primary diseases were collected from our local database, and 3656 LNs (19.5 ± 14.9 LNs/CECT, mean ± standard deviation) with a short-axis diameter (SAD) ≥ 5 mm were segmented manually by expert physicians. With these data, we trained an independent fully convolutional neural network based on 3D foveal patches. Testing was performed on 30 independent CECTs with 925 segmented LNs with an SAD ≥ 5 mm. RESULTS: In total, 4,581 LNs were segmented in 217 CECTs. The model achieved an average localization rate (LR), i.e., percentage of localized LNs/CECT, of 78.0% in the validation dataset. In the test dataset, average LR was 81.1% with a mean Dice coefficient of 0.71. For enlarged LNs with a SAD ≥ 10 mm, LR was 96.2%. In the test dataset, the false-positive rate was 2.4 LNs/CECT. CONCLUSIONS: Our trained AI model demonstrated a good overall performance in the consistent automatic localization and 3D segmentation of physiological and metastatic cervical LNs with a SAD ≥ 5 mm on CECTs. This could aid clinical localization and automatic 3D segmentation, which can benefit clinical care and radiomics research. RELEVANCE STATEMENT: Our AI model is a time-saving tool for 3D segmentation of cervical lymph nodes on contrast-enhanced CT scans and serves as a solid base for N staging in clinical practice and further radiomics research. KEY POINTS: • Determination of N status in TNM staging is essential for therapy planning in oncology. • Segmenting cervical lymph nodes manually is highly time-consuming in clinical practice. • Our model provides a robust, automated 3D segmentation of cervical lymph nodes. • It achieves a high accuracy for localization especially of enlarged lymph nodes. • These segmentations should assist clinical care and radiomics research. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s41747-023-00360-x. Springer Vienna 2023-07-28 /pmc/articles/PMC10382409/ /pubmed/37505296 http://dx.doi.org/10.1186/s41747-023-00360-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Original Article
Rinneburger, Miriam
Carolus, Heike
Iuga, Andra-Iza
Weisthoff, Mathilda
Lennartz, Simon
Hokamp, Nils Große
Caldeira, Liliana
Shahzad, Rahil
Maintz, David
Laqua, Fabian Christopher
Baeßler, Bettina
Klinder, Tobias
Persigehl, Thorsten
Automated localization and segmentation of cervical lymph nodes on contrast-enhanced CT using a 3D foveal fully convolutional neural network
title Automated localization and segmentation of cervical lymph nodes on contrast-enhanced CT using a 3D foveal fully convolutional neural network
title_full Automated localization and segmentation of cervical lymph nodes on contrast-enhanced CT using a 3D foveal fully convolutional neural network
title_fullStr Automated localization and segmentation of cervical lymph nodes on contrast-enhanced CT using a 3D foveal fully convolutional neural network
title_full_unstemmed Automated localization and segmentation of cervical lymph nodes on contrast-enhanced CT using a 3D foveal fully convolutional neural network
title_short Automated localization and segmentation of cervical lymph nodes on contrast-enhanced CT using a 3D foveal fully convolutional neural network
title_sort automated localization and segmentation of cervical lymph nodes on contrast-enhanced ct using a 3d foveal fully convolutional neural network
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10382409/
https://www.ncbi.nlm.nih.gov/pubmed/37505296
http://dx.doi.org/10.1186/s41747-023-00360-x
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