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Lymph Node Stations of Pancreas Which Are Identified in Real Color Sectioned Images of a Cadaver With Pancreatic Cancer

BACKGROUND: In pancreatic cancer surgery, anatomical understanding of lymph node metastases is required. Distinguishing lymph nodes in computed tomography or magnetic resonance imaging is challenging for novice doctors and medical students because of their small size and similar color to surrounding...

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Autores principales: Kim, Chung Yoh, Jung, Yongwook, Park, Jin Seo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Korean Academy of Medical Sciences 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10681841/
https://www.ncbi.nlm.nih.gov/pubmed/38013647
http://dx.doi.org/10.3346/jkms.2023.38.e392
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author Kim, Chung Yoh
Jung, Yongwook
Park, Jin Seo
author_facet Kim, Chung Yoh
Jung, Yongwook
Park, Jin Seo
author_sort Kim, Chung Yoh
collection PubMed
description BACKGROUND: In pancreatic cancer surgery, anatomical understanding of lymph node metastases is required. Distinguishing lymph nodes in computed tomography or magnetic resonance imaging is challenging for novice doctors and medical students because of their small size and similar color to surrounding tissues. This study aimed to enhance our understanding of the clinical anatomy of lymph node stations relevant to pancreatic cancer using newly sectioned images of a cadaver with true color and high resolution and their three-dimensional (3D) models. METHODS: An 88-year-old female cadaver who died of pancreatic cancer was serially sectioned. Among the sectioned images of the whole body (0.05 mm-sized pixel, 48 bits color), images of the abdomen were selected, and examined to identify lymph nodes and nearby structures. 34 structures (9 in digestive system; 1 in urinary system; 2 in cardiovascular system; 22 in lymphatic system) were segmented on the sectioned images. Based on the sectioned and segmented images, volume and surface models were produced. RESULTS: Among the known 28 lymph node stations, 21 stations were identified through location, size, and color of normal and abnormal structures in the sectioned images and 3D models. Two near the splenic artery could not be separated from the cancer tissue, and the remaining five were not clearly identified. In the surface models, the shape and location of lymph node stations could be confirmed with nearby structures. CONCLUSION: The lymph node stations relevant to pancreatic cancer can be anatomically understood by using the sectioned images and 3D models which contain true color and high resolution.
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spelling pubmed-106818412023-11-27 Lymph Node Stations of Pancreas Which Are Identified in Real Color Sectioned Images of a Cadaver With Pancreatic Cancer Kim, Chung Yoh Jung, Yongwook Park, Jin Seo J Korean Med Sci Original Article BACKGROUND: In pancreatic cancer surgery, anatomical understanding of lymph node metastases is required. Distinguishing lymph nodes in computed tomography or magnetic resonance imaging is challenging for novice doctors and medical students because of their small size and similar color to surrounding tissues. This study aimed to enhance our understanding of the clinical anatomy of lymph node stations relevant to pancreatic cancer using newly sectioned images of a cadaver with true color and high resolution and their three-dimensional (3D) models. METHODS: An 88-year-old female cadaver who died of pancreatic cancer was serially sectioned. Among the sectioned images of the whole body (0.05 mm-sized pixel, 48 bits color), images of the abdomen were selected, and examined to identify lymph nodes and nearby structures. 34 structures (9 in digestive system; 1 in urinary system; 2 in cardiovascular system; 22 in lymphatic system) were segmented on the sectioned images. Based on the sectioned and segmented images, volume and surface models were produced. RESULTS: Among the known 28 lymph node stations, 21 stations were identified through location, size, and color of normal and abnormal structures in the sectioned images and 3D models. Two near the splenic artery could not be separated from the cancer tissue, and the remaining five were not clearly identified. In the surface models, the shape and location of lymph node stations could be confirmed with nearby structures. CONCLUSION: The lymph node stations relevant to pancreatic cancer can be anatomically understood by using the sectioned images and 3D models which contain true color and high resolution. The Korean Academy of Medical Sciences 2023-11-15 /pmc/articles/PMC10681841/ /pubmed/38013647 http://dx.doi.org/10.3346/jkms.2023.38.e392 Text en © 2023 The Korean Academy of Medical Sciences. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Article
Kim, Chung Yoh
Jung, Yongwook
Park, Jin Seo
Lymph Node Stations of Pancreas Which Are Identified in Real Color Sectioned Images of a Cadaver With Pancreatic Cancer
title Lymph Node Stations of Pancreas Which Are Identified in Real Color Sectioned Images of a Cadaver With Pancreatic Cancer
title_full Lymph Node Stations of Pancreas Which Are Identified in Real Color Sectioned Images of a Cadaver With Pancreatic Cancer
title_fullStr Lymph Node Stations of Pancreas Which Are Identified in Real Color Sectioned Images of a Cadaver With Pancreatic Cancer
title_full_unstemmed Lymph Node Stations of Pancreas Which Are Identified in Real Color Sectioned Images of a Cadaver With Pancreatic Cancer
title_short Lymph Node Stations of Pancreas Which Are Identified in Real Color Sectioned Images of a Cadaver With Pancreatic Cancer
title_sort lymph node stations of pancreas which are identified in real color sectioned images of a cadaver with pancreatic cancer
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10681841/
https://www.ncbi.nlm.nih.gov/pubmed/38013647
http://dx.doi.org/10.3346/jkms.2023.38.e392
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