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Utility of Tissue Classification in Invasive Ductal Carcinoma using Dynamic Magnetic Resonance Imaging of the Mammary Gland
OBJECTIVES: In Japan, invasive ductal carcinomas, which account for 75% of breast cancer cases, are sub-classified as solid, tubule-forming, scirrhous, and other types based on the histopathological findings. Although time-intensity curve (TIC) analysis of magnetic resonance (MR) images has shown di...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Scientific Scholar
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7881501/ https://www.ncbi.nlm.nih.gov/pubmed/33598361 http://dx.doi.org/10.25259/JCIS_173_2020 |
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author | Miyazaki, Yoshiaki Tabata, Nobuyuki Kubo, Yuichiro Shinozaki, Kenji |
author_facet | Miyazaki, Yoshiaki Tabata, Nobuyuki Kubo, Yuichiro Shinozaki, Kenji |
author_sort | Miyazaki, Yoshiaki |
collection | PubMed |
description | OBJECTIVES: In Japan, invasive ductal carcinomas, which account for 75% of breast cancer cases, are sub-classified as solid, tubule-forming, scirrhous, and other types based on the histopathological findings. Although time-intensity curve (TIC) analysis of magnetic resonance (MR) images has shown diagnostic ability in differentiating benign and malignant tumors, its ability to diagnose different tumor tissue types has not yet been achieved. In this study, we report a histological classification of invasive ductal carcinoma using the TIC analysis of dynamic MR images of the mammary gland. MATERIAL AND METHODS: A total of 312 invasive ductal carcinomas were analyzed, and each tissue type that indicated malignancy in the washout parts of the tumors was classified and characterized using the TIC. RESULTS: The tissue was classified, and the results were then compared to the pathohistological diagnosis. Using this method, the accuracy of tissue classification by quantitative analysis of TIC-MR images was 86.9% (271/312), which was higher than that obtained by ultrasonography 68.9% (215/312). CONCLUSION: This method is effective for classifying tissue types in invasive ductal carcinoma. |
format | Online Article Text |
id | pubmed-7881501 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Scientific Scholar |
record_format | MEDLINE/PubMed |
spelling | pubmed-78815012021-02-16 Utility of Tissue Classification in Invasive Ductal Carcinoma using Dynamic Magnetic Resonance Imaging of the Mammary Gland Miyazaki, Yoshiaki Tabata, Nobuyuki Kubo, Yuichiro Shinozaki, Kenji J Clin Imaging Sci Original Research OBJECTIVES: In Japan, invasive ductal carcinomas, which account for 75% of breast cancer cases, are sub-classified as solid, tubule-forming, scirrhous, and other types based on the histopathological findings. Although time-intensity curve (TIC) analysis of magnetic resonance (MR) images has shown diagnostic ability in differentiating benign and malignant tumors, its ability to diagnose different tumor tissue types has not yet been achieved. In this study, we report a histological classification of invasive ductal carcinoma using the TIC analysis of dynamic MR images of the mammary gland. MATERIAL AND METHODS: A total of 312 invasive ductal carcinomas were analyzed, and each tissue type that indicated malignancy in the washout parts of the tumors was classified and characterized using the TIC. RESULTS: The tissue was classified, and the results were then compared to the pathohistological diagnosis. Using this method, the accuracy of tissue classification by quantitative analysis of TIC-MR images was 86.9% (271/312), which was higher than that obtained by ultrasonography 68.9% (215/312). CONCLUSION: This method is effective for classifying tissue types in invasive ductal carcinoma. Scientific Scholar 2021-01-18 /pmc/articles/PMC7881501/ /pubmed/33598361 http://dx.doi.org/10.25259/JCIS_173_2020 Text en © 2020 Published by Scientific Scholar on behalf of Journal of Clinical Imaging Science https://creativecommons.org/licenses/by-nc-sa/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-Share Alike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as the author is credited and the new creations are licensed under the identical terms. |
spellingShingle | Original Research Miyazaki, Yoshiaki Tabata, Nobuyuki Kubo, Yuichiro Shinozaki, Kenji Utility of Tissue Classification in Invasive Ductal Carcinoma using Dynamic Magnetic Resonance Imaging of the Mammary Gland |
title | Utility of Tissue Classification in Invasive Ductal Carcinoma using Dynamic Magnetic Resonance Imaging of the Mammary Gland |
title_full | Utility of Tissue Classification in Invasive Ductal Carcinoma using Dynamic Magnetic Resonance Imaging of the Mammary Gland |
title_fullStr | Utility of Tissue Classification in Invasive Ductal Carcinoma using Dynamic Magnetic Resonance Imaging of the Mammary Gland |
title_full_unstemmed | Utility of Tissue Classification in Invasive Ductal Carcinoma using Dynamic Magnetic Resonance Imaging of the Mammary Gland |
title_short | Utility of Tissue Classification in Invasive Ductal Carcinoma using Dynamic Magnetic Resonance Imaging of the Mammary Gland |
title_sort | utility of tissue classification in invasive ductal carcinoma using dynamic magnetic resonance imaging of the mammary gland |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7881501/ https://www.ncbi.nlm.nih.gov/pubmed/33598361 http://dx.doi.org/10.25259/JCIS_173_2020 |
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