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Quantitative Software Analysis of Endoscopic Ultrasound Images of Pancreatic Cystic Lesions

Endoscopic ultrasonography (EUS) is the most accurate imaging modality for the evaluation of different types of pancreatic cystic lesions. Our aim was to analyze EUS images of pancreatic cystic lesions using an image processing software. We specified the echogenicity of the lesions by measuring the...

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Autores principales: Keczer, Bánk, Benke, Márton, Marjai, Tamás, Horváth, Miklós, Miheller, Pál, Szücs, Ákos, Harsányi, László, Szijártó, Attila, Hritz, István
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9498186/
https://www.ncbi.nlm.nih.gov/pubmed/36140506
http://dx.doi.org/10.3390/diagnostics12092105
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author Keczer, Bánk
Benke, Márton
Marjai, Tamás
Horváth, Miklós
Miheller, Pál
Szücs, Ákos
Harsányi, László
Szijártó, Attila
Hritz, István
author_facet Keczer, Bánk
Benke, Márton
Marjai, Tamás
Horváth, Miklós
Miheller, Pál
Szücs, Ákos
Harsányi, László
Szijártó, Attila
Hritz, István
author_sort Keczer, Bánk
collection PubMed
description Endoscopic ultrasonography (EUS) is the most accurate imaging modality for the evaluation of different types of pancreatic cystic lesions. Our aim was to analyze EUS images of pancreatic cystic lesions using an image processing software. We specified the echogenicity of the lesions by measuring the gray value of pixels inside the selected areas. The images were divided into groups (serous cystic neoplasm /SCN/, intraductal papillary mucinous neoplasms and mucinous cystic neoplasms /Non-SCN/ and Pseudocyst) according to the pathology results of the lesions. Overall, 170 images were processed by the software: 81 in Non-SCN, 30 in SCN and 59 in Pseudocyst group. The mean gray value of the entire lesion in the Non-SCN group was significantly higher than in the SCN group (27.8 vs. 18.8; p < 0.0005). The area ratio in the SCN, Non-SCN and Pseudocyst groups was 57%, 39% and 61%, respectively; significantly lower in the Non-SCN group than in the SCN or Pseudocyst groups (p < 0.0005 and p < 0.0005, respectively). The lesion density was also significantly higher in the Non-SCN group compared to the SCN or Pseudocyst groups (4186.6/mm(2) vs. 2833.8/mm(2) vs. 2981.6/mm(2); p < 0.0005 and p < 0.0005, respectively). The EUS image analysis process may have the potential to be a diagnostic tool for the evaluation and differentiation of pancreatic cystic lesions.
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spelling pubmed-94981862022-09-23 Quantitative Software Analysis of Endoscopic Ultrasound Images of Pancreatic Cystic Lesions Keczer, Bánk Benke, Márton Marjai, Tamás Horváth, Miklós Miheller, Pál Szücs, Ákos Harsányi, László Szijártó, Attila Hritz, István Diagnostics (Basel) Article Endoscopic ultrasonography (EUS) is the most accurate imaging modality for the evaluation of different types of pancreatic cystic lesions. Our aim was to analyze EUS images of pancreatic cystic lesions using an image processing software. We specified the echogenicity of the lesions by measuring the gray value of pixels inside the selected areas. The images were divided into groups (serous cystic neoplasm /SCN/, intraductal papillary mucinous neoplasms and mucinous cystic neoplasms /Non-SCN/ and Pseudocyst) according to the pathology results of the lesions. Overall, 170 images were processed by the software: 81 in Non-SCN, 30 in SCN and 59 in Pseudocyst group. The mean gray value of the entire lesion in the Non-SCN group was significantly higher than in the SCN group (27.8 vs. 18.8; p < 0.0005). The area ratio in the SCN, Non-SCN and Pseudocyst groups was 57%, 39% and 61%, respectively; significantly lower in the Non-SCN group than in the SCN or Pseudocyst groups (p < 0.0005 and p < 0.0005, respectively). The lesion density was also significantly higher in the Non-SCN group compared to the SCN or Pseudocyst groups (4186.6/mm(2) vs. 2833.8/mm(2) vs. 2981.6/mm(2); p < 0.0005 and p < 0.0005, respectively). The EUS image analysis process may have the potential to be a diagnostic tool for the evaluation and differentiation of pancreatic cystic lesions. MDPI 2022-08-30 /pmc/articles/PMC9498186/ /pubmed/36140506 http://dx.doi.org/10.3390/diagnostics12092105 Text en © 2022 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
Keczer, Bánk
Benke, Márton
Marjai, Tamás
Horváth, Miklós
Miheller, Pál
Szücs, Ákos
Harsányi, László
Szijártó, Attila
Hritz, István
Quantitative Software Analysis of Endoscopic Ultrasound Images of Pancreatic Cystic Lesions
title Quantitative Software Analysis of Endoscopic Ultrasound Images of Pancreatic Cystic Lesions
title_full Quantitative Software Analysis of Endoscopic Ultrasound Images of Pancreatic Cystic Lesions
title_fullStr Quantitative Software Analysis of Endoscopic Ultrasound Images of Pancreatic Cystic Lesions
title_full_unstemmed Quantitative Software Analysis of Endoscopic Ultrasound Images of Pancreatic Cystic Lesions
title_short Quantitative Software Analysis of Endoscopic Ultrasound Images of Pancreatic Cystic Lesions
title_sort quantitative software analysis of endoscopic ultrasound images of pancreatic cystic lesions
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9498186/
https://www.ncbi.nlm.nih.gov/pubmed/36140506
http://dx.doi.org/10.3390/diagnostics12092105
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