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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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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. |
format | Online Article Text |
id | pubmed-9963803 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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