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Computed tomography Hounsfield units can predict breast cancer metastasis to axillary lymph nodes
BACKGROUND: Axillary lymph node (ALN) status is an important prognostic factor for breast cancer. We retrospectively used contrast-enhanced computed tomography (CE-CT) to evaluate the presence of ALN, metastasis based on size, shape, and contrasting effects. METHODS: Of 131 consecutive patients who...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4193134/ https://www.ncbi.nlm.nih.gov/pubmed/25266250 http://dx.doi.org/10.1186/1471-2407-14-730 |
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author | Urata, Masakazu Kijima, Yuko Hirata, Munetsugu Shinden, Yoshiaki Arima, Hideo Nakajo, Akihiro Koriyama, Chihaya Arigami, Takaaki Uenosono, Yoshikazu Okumura, Hiroshi Maemura, Kosei Ishigami, Sumiya Yoshinaka, Heiji Natsugoe, Shoji |
author_facet | Urata, Masakazu Kijima, Yuko Hirata, Munetsugu Shinden, Yoshiaki Arima, Hideo Nakajo, Akihiro Koriyama, Chihaya Arigami, Takaaki Uenosono, Yoshikazu Okumura, Hiroshi Maemura, Kosei Ishigami, Sumiya Yoshinaka, Heiji Natsugoe, Shoji |
author_sort | Urata, Masakazu |
collection | PubMed |
description | BACKGROUND: Axillary lymph node (ALN) status is an important prognostic factor for breast cancer. We retrospectively used contrast-enhanced computed tomography (CE-CT) to evaluate the presence of ALN, metastasis based on size, shape, and contrasting effects. METHODS: Of 131 consecutive patients who underwent CE-CT followed by surgery for breast cancer between 2005 and 2012 in our institution, 49 were histologically diagnosed with lymph node metastasis. Maximum Hounsfield units (HU) and mean HU were measured in non-contrasting CT (NC-CT) and CE-CT of ALNs. RESULTS: Of 12 examined measurements, we found significant differences between negative and metastatic ALNs in mean and maximum NC-CT HU, and mean and maximum CE-CT HU (P < 0.05). We used a receiver operating curve, to determine cut-off values of four items in which significant differences were observed. The highest accuracy rate was noted for the cut-off value of 54 as maximum NC-CT HU for which sensitivity, specificity, and accuracy rate were 79.6%, 80.5% and 80.2%, respectively. CONCLUSIONS: CT HU of a patient with breast cancer are absolute values that offer objective disease management data that are not influenced by the screener’s ability. |
format | Online Article Text |
id | pubmed-4193134 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-41931342014-10-11 Computed tomography Hounsfield units can predict breast cancer metastasis to axillary lymph nodes Urata, Masakazu Kijima, Yuko Hirata, Munetsugu Shinden, Yoshiaki Arima, Hideo Nakajo, Akihiro Koriyama, Chihaya Arigami, Takaaki Uenosono, Yoshikazu Okumura, Hiroshi Maemura, Kosei Ishigami, Sumiya Yoshinaka, Heiji Natsugoe, Shoji BMC Cancer Research Article BACKGROUND: Axillary lymph node (ALN) status is an important prognostic factor for breast cancer. We retrospectively used contrast-enhanced computed tomography (CE-CT) to evaluate the presence of ALN, metastasis based on size, shape, and contrasting effects. METHODS: Of 131 consecutive patients who underwent CE-CT followed by surgery for breast cancer between 2005 and 2012 in our institution, 49 were histologically diagnosed with lymph node metastasis. Maximum Hounsfield units (HU) and mean HU were measured in non-contrasting CT (NC-CT) and CE-CT of ALNs. RESULTS: Of 12 examined measurements, we found significant differences between negative and metastatic ALNs in mean and maximum NC-CT HU, and mean and maximum CE-CT HU (P < 0.05). We used a receiver operating curve, to determine cut-off values of four items in which significant differences were observed. The highest accuracy rate was noted for the cut-off value of 54 as maximum NC-CT HU for which sensitivity, specificity, and accuracy rate were 79.6%, 80.5% and 80.2%, respectively. CONCLUSIONS: CT HU of a patient with breast cancer are absolute values that offer objective disease management data that are not influenced by the screener’s ability. BioMed Central 2014-09-30 /pmc/articles/PMC4193134/ /pubmed/25266250 http://dx.doi.org/10.1186/1471-2407-14-730 Text en © Urata et al.; licensee BioMed Central Ltd. 2014 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Urata, Masakazu Kijima, Yuko Hirata, Munetsugu Shinden, Yoshiaki Arima, Hideo Nakajo, Akihiro Koriyama, Chihaya Arigami, Takaaki Uenosono, Yoshikazu Okumura, Hiroshi Maemura, Kosei Ishigami, Sumiya Yoshinaka, Heiji Natsugoe, Shoji Computed tomography Hounsfield units can predict breast cancer metastasis to axillary lymph nodes |
title | Computed tomography Hounsfield units can predict breast cancer metastasis to axillary lymph nodes |
title_full | Computed tomography Hounsfield units can predict breast cancer metastasis to axillary lymph nodes |
title_fullStr | Computed tomography Hounsfield units can predict breast cancer metastasis to axillary lymph nodes |
title_full_unstemmed | Computed tomography Hounsfield units can predict breast cancer metastasis to axillary lymph nodes |
title_short | Computed tomography Hounsfield units can predict breast cancer metastasis to axillary lymph nodes |
title_sort | computed tomography hounsfield units can predict breast cancer metastasis to axillary lymph nodes |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4193134/ https://www.ncbi.nlm.nih.gov/pubmed/25266250 http://dx.doi.org/10.1186/1471-2407-14-730 |
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