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Deep Learning-Based Segmentation and Volume Calculation of Pediatric Lymphoma on Contrast-Enhanced Computed Tomographies

Lymphomas are the ninth most common malignant neoplasms as of 2020 and the most common blood malignancies in the developed world. There are multiple approaches to lymphoma staging and monitoring, but all of the currently available ones, generally based either on 2-dimensional measurements performed...

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Autores principales: Klimont, Michał, Oronowicz-Jaśkowiak, Agnieszka, Flieger, Mateusz, Rzeszutek, Jacek, Juszkat, Robert, Jończyk-Potoczna, Katarzyna
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9963803/
https://www.ncbi.nlm.nih.gov/pubmed/36836418
http://dx.doi.org/10.3390/jpm13020184
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author Klimont, Michał
Oronowicz-Jaśkowiak, Agnieszka
Flieger, Mateusz
Rzeszutek, Jacek
Juszkat, Robert
Jończyk-Potoczna, Katarzyna
author_facet Klimont, Michał
Oronowicz-Jaśkowiak, Agnieszka
Flieger, Mateusz
Rzeszutek, Jacek
Juszkat, Robert
Jończyk-Potoczna, Katarzyna
author_sort Klimont, Michał
collection PubMed
description Lymphomas are the ninth most common malignant neoplasms as of 2020 and the most common blood malignancies in the developed world. There are multiple approaches to lymphoma staging and monitoring, but all of the currently available ones, generally based either on 2-dimensional measurements performed on CT scans or metabolic assessment on FDG PET/CT, have some disadvantages, including high inter- and intraobserver variability and lack of clear cut-off points. The aim of this paper was to present a novel approach to fully automated segmentation of thoracic lymphoma in pediatric patients. Manual segmentations of 30 CT scans from 30 different were prepared by the authors. nnU-Net, an open-source deep learning-based segmentation method, was used for the automatic segmentation. The highest Dice score achieved by the model was 0.81 (SD = 0.17) on the test set, which proves the potential feasibility of the method, albeit it must be underlined that studies on larger datasets and featuring external validation are required. The trained model, along with training and test data, is shared publicly to facilitate further research on the topic.
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spelling pubmed-99638032023-02-26 Deep Learning-Based Segmentation and Volume Calculation of Pediatric Lymphoma on Contrast-Enhanced Computed Tomographies Klimont, Michał Oronowicz-Jaśkowiak, Agnieszka Flieger, Mateusz Rzeszutek, Jacek Juszkat, Robert Jończyk-Potoczna, Katarzyna J Pers Med Article Lymphomas are the ninth most common malignant neoplasms as of 2020 and the most common blood malignancies in the developed world. There are multiple approaches to lymphoma staging and monitoring, but all of the currently available ones, generally based either on 2-dimensional measurements performed on CT scans or metabolic assessment on FDG PET/CT, have some disadvantages, including high inter- and intraobserver variability and lack of clear cut-off points. The aim of this paper was to present a novel approach to fully automated segmentation of thoracic lymphoma in pediatric patients. Manual segmentations of 30 CT scans from 30 different were prepared by the authors. nnU-Net, an open-source deep learning-based segmentation method, was used for the automatic segmentation. The highest Dice score achieved by the model was 0.81 (SD = 0.17) on the test set, which proves the potential feasibility of the method, albeit it must be underlined that studies on larger datasets and featuring external validation are required. The trained model, along with training and test data, is shared publicly to facilitate further research on the topic. MDPI 2023-01-20 /pmc/articles/PMC9963803/ /pubmed/36836418 http://dx.doi.org/10.3390/jpm13020184 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Klimont, Michał
Oronowicz-Jaśkowiak, Agnieszka
Flieger, Mateusz
Rzeszutek, Jacek
Juszkat, Robert
Jończyk-Potoczna, Katarzyna
Deep Learning-Based Segmentation and Volume Calculation of Pediatric Lymphoma on Contrast-Enhanced Computed Tomographies
title Deep Learning-Based Segmentation and Volume Calculation of Pediatric Lymphoma on Contrast-Enhanced Computed Tomographies
title_full Deep Learning-Based Segmentation and Volume Calculation of Pediatric Lymphoma on Contrast-Enhanced Computed Tomographies
title_fullStr Deep Learning-Based Segmentation and Volume Calculation of Pediatric Lymphoma on Contrast-Enhanced Computed Tomographies
title_full_unstemmed Deep Learning-Based Segmentation and Volume Calculation of Pediatric Lymphoma on Contrast-Enhanced Computed Tomographies
title_short Deep Learning-Based Segmentation and Volume Calculation of Pediatric Lymphoma on Contrast-Enhanced Computed Tomographies
title_sort deep learning-based segmentation and volume calculation of pediatric lymphoma on contrast-enhanced computed tomographies
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9963803/
https://www.ncbi.nlm.nih.gov/pubmed/36836418
http://dx.doi.org/10.3390/jpm13020184
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