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Sigmoid model analysis of breast dynamic contrast‐enhanced MRI: Distinguishing between benign and malignant breast masses and breast cancer subtype prediction

Dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI) is performed to distinguish between benign and malignant lesions by evaluating the changes in signal intensity of the acquired image (kinetic curve). This study aimed to verify whether the existing breast DCE‐MRI analyzed by the sigmoid...

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Autores principales: Koori, Norikazu, Miyati, Tosiaki, Ohno, Naoki, Kawashima, Hiroko, Nishikawa, Hiroko
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9195041/
https://www.ncbi.nlm.nih.gov/pubmed/35594028
http://dx.doi.org/10.1002/acm2.13651
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author Koori, Norikazu
Miyati, Tosiaki
Ohno, Naoki
Kawashima, Hiroko
Nishikawa, Hiroko
author_facet Koori, Norikazu
Miyati, Tosiaki
Ohno, Naoki
Kawashima, Hiroko
Nishikawa, Hiroko
author_sort Koori, Norikazu
collection PubMed
description Dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI) is performed to distinguish between benign and malignant lesions by evaluating the changes in signal intensity of the acquired image (kinetic curve). This study aimed to verify whether the existing breast DCE‐MRI analyzed by the sigmoid model can accurately distinguish between benign and invasive ductal carcinoma (IDC) and predict the subtype. A total of 154 patients who underwent breast MRI for detailed breast mass examinations were included in this study (38 with benign masses and 116 with IDC. The sigmoid model involved the acquisition of images at seven timepoints in 1‐min intervals to determine the change in signal intensity before and after contrast injection. From this curve, the magnitude of the increase in signal intensity in the early phase, the time to reach the maximum increase, and the slopes in the early and late phases were calculated. The Mann–Whitney U‐test was used for the statistical analysis. The IDC group exhibited a significantly larger and faster signal increase in the early phase and a significantly smaller rate of increase in the late phase than the benign group (P < 0.001). The luminal A‐like group demonstrated a significantly longer time to reach the maximum signal increase rate than other IDC subtypes (P < 0.05). The sigmoid model analysis of breast DCE‐MRI can distinguish between benign lesions and IDC and may also help in predicting luminal A‐like breast cancer.
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spelling pubmed-91950412022-06-21 Sigmoid model analysis of breast dynamic contrast‐enhanced MRI: Distinguishing between benign and malignant breast masses and breast cancer subtype prediction Koori, Norikazu Miyati, Tosiaki Ohno, Naoki Kawashima, Hiroko Nishikawa, Hiroko J Appl Clin Med Phys Medical Imaging Dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI) is performed to distinguish between benign and malignant lesions by evaluating the changes in signal intensity of the acquired image (kinetic curve). This study aimed to verify whether the existing breast DCE‐MRI analyzed by the sigmoid model can accurately distinguish between benign and invasive ductal carcinoma (IDC) and predict the subtype. A total of 154 patients who underwent breast MRI for detailed breast mass examinations were included in this study (38 with benign masses and 116 with IDC. The sigmoid model involved the acquisition of images at seven timepoints in 1‐min intervals to determine the change in signal intensity before and after contrast injection. From this curve, the magnitude of the increase in signal intensity in the early phase, the time to reach the maximum increase, and the slopes in the early and late phases were calculated. The Mann–Whitney U‐test was used for the statistical analysis. The IDC group exhibited a significantly larger and faster signal increase in the early phase and a significantly smaller rate of increase in the late phase than the benign group (P < 0.001). The luminal A‐like group demonstrated a significantly longer time to reach the maximum signal increase rate than other IDC subtypes (P < 0.05). The sigmoid model analysis of breast DCE‐MRI can distinguish between benign lesions and IDC and may also help in predicting luminal A‐like breast cancer. John Wiley and Sons Inc. 2022-05-20 /pmc/articles/PMC9195041/ /pubmed/35594028 http://dx.doi.org/10.1002/acm2.13651 Text en © 2022 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, LLC on behalf of The American Association of Physicists in Medicine. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Medical Imaging
Koori, Norikazu
Miyati, Tosiaki
Ohno, Naoki
Kawashima, Hiroko
Nishikawa, Hiroko
Sigmoid model analysis of breast dynamic contrast‐enhanced MRI: Distinguishing between benign and malignant breast masses and breast cancer subtype prediction
title Sigmoid model analysis of breast dynamic contrast‐enhanced MRI: Distinguishing between benign and malignant breast masses and breast cancer subtype prediction
title_full Sigmoid model analysis of breast dynamic contrast‐enhanced MRI: Distinguishing between benign and malignant breast masses and breast cancer subtype prediction
title_fullStr Sigmoid model analysis of breast dynamic contrast‐enhanced MRI: Distinguishing between benign and malignant breast masses and breast cancer subtype prediction
title_full_unstemmed Sigmoid model analysis of breast dynamic contrast‐enhanced MRI: Distinguishing between benign and malignant breast masses and breast cancer subtype prediction
title_short Sigmoid model analysis of breast dynamic contrast‐enhanced MRI: Distinguishing between benign and malignant breast masses and breast cancer subtype prediction
title_sort sigmoid model analysis of breast dynamic contrast‐enhanced mri: distinguishing between benign and malignant breast masses and breast cancer subtype prediction
topic Medical Imaging
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9195041/
https://www.ncbi.nlm.nih.gov/pubmed/35594028
http://dx.doi.org/10.1002/acm2.13651
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