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Quantitative nucleic features are effective for discrimination of intraductal proliferative lesions of the breast

BACKGROUND: Intraductal proliferative lesions (IDPLs) of the breast are recognized as a risk factor for subsequent invasive carcinoma development. Although opportunities for IDPL diagnosis have increased, these lesions are difficult to diagnose correctly, especially atypical ductal hyperplasia (ADH)...

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Autores principales: Yamada, Masatoshi, Saito, Akira, Yamamoto, Yoichiro, Cosatto, Eric, Kurata, Atsushi, Nagao, Toshitaka, Tateishi, Ayako, Kuroda, Masahiko
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Medknow Publications & Media Pvt Ltd 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4763509/
https://www.ncbi.nlm.nih.gov/pubmed/26955499
http://dx.doi.org/10.4103/2153-3539.175380
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author Yamada, Masatoshi
Saito, Akira
Yamamoto, Yoichiro
Cosatto, Eric
Kurata, Atsushi
Nagao, Toshitaka
Tateishi, Ayako
Kuroda, Masahiko
author_facet Yamada, Masatoshi
Saito, Akira
Yamamoto, Yoichiro
Cosatto, Eric
Kurata, Atsushi
Nagao, Toshitaka
Tateishi, Ayako
Kuroda, Masahiko
author_sort Yamada, Masatoshi
collection PubMed
description BACKGROUND: Intraductal proliferative lesions (IDPLs) of the breast are recognized as a risk factor for subsequent invasive carcinoma development. Although opportunities for IDPL diagnosis have increased, these lesions are difficult to diagnose correctly, especially atypical ductal hyperplasia (ADH) and low-grade ductal carcinoma in situ (LG-DCIS). In order to define the difference between these lesions, many molecular pathological approaches have been performed. However, still we do not have a molecular marker and objective histological index about IDPLs of the breast. METHODS: We generated full digital pathology archives from 175 female IDPL patients, including usual ductal hyperplasia (UDH), ADH, LG-DCIS, intermediate-grade (IM)-DCIS, and high-grade (HG)-DCIS. After total 2,035,807 nucleic segmentations were extracted, we evaluated nuclear features using step-wise linear discriminant analysis (LDA) and a support vector machine. RESULTS: High diagnostic accuracy (81.8–99.3%) was achieved between pathologists’ diagnoses and two-group LDA predictions from nucleic features for IDPL discrimination. Grouping of nuclear features as size and shape-related or intranuclear texture-related revealed that the latter group was more important when distinguishing between normal duct, UDH, ADH, and LG-DCIS. However, these two groups were equally important when discriminating between LG-DCIS and HG-DCIS. The Mahalanobis distances between each group showed that the smallest distance values occurred between LG-DCIS and IM-DCIS and between ADH and Normal. On the other hand, the distance value between ADH and LG-DCIS was larger than this distance. CONCLUSIONS: In this study, we have presented a practical and useful digital pathological method that incorporates nuclear morphological and textural features for IDPL prediction. We expect that this novel algorithm is used for the automated diagnosis assisting system for breast cancer.
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spelling pubmed-47635092016-03-07 Quantitative nucleic features are effective for discrimination of intraductal proliferative lesions of the breast Yamada, Masatoshi Saito, Akira Yamamoto, Yoichiro Cosatto, Eric Kurata, Atsushi Nagao, Toshitaka Tateishi, Ayako Kuroda, Masahiko J Pathol Inform Research Article BACKGROUND: Intraductal proliferative lesions (IDPLs) of the breast are recognized as a risk factor for subsequent invasive carcinoma development. Although opportunities for IDPL diagnosis have increased, these lesions are difficult to diagnose correctly, especially atypical ductal hyperplasia (ADH) and low-grade ductal carcinoma in situ (LG-DCIS). In order to define the difference between these lesions, many molecular pathological approaches have been performed. However, still we do not have a molecular marker and objective histological index about IDPLs of the breast. METHODS: We generated full digital pathology archives from 175 female IDPL patients, including usual ductal hyperplasia (UDH), ADH, LG-DCIS, intermediate-grade (IM)-DCIS, and high-grade (HG)-DCIS. After total 2,035,807 nucleic segmentations were extracted, we evaluated nuclear features using step-wise linear discriminant analysis (LDA) and a support vector machine. RESULTS: High diagnostic accuracy (81.8–99.3%) was achieved between pathologists’ diagnoses and two-group LDA predictions from nucleic features for IDPL discrimination. Grouping of nuclear features as size and shape-related or intranuclear texture-related revealed that the latter group was more important when distinguishing between normal duct, UDH, ADH, and LG-DCIS. However, these two groups were equally important when discriminating between LG-DCIS and HG-DCIS. The Mahalanobis distances between each group showed that the smallest distance values occurred between LG-DCIS and IM-DCIS and between ADH and Normal. On the other hand, the distance value between ADH and LG-DCIS was larger than this distance. CONCLUSIONS: In this study, we have presented a practical and useful digital pathological method that incorporates nuclear morphological and textural features for IDPL prediction. We expect that this novel algorithm is used for the automated diagnosis assisting system for breast cancer. Medknow Publications & Media Pvt Ltd 2016-01-29 /pmc/articles/PMC4763509/ /pubmed/26955499 http://dx.doi.org/10.4103/2153-3539.175380 Text en Copyright: © 2016 Journal of Pathology Informatics http://creativecommons.org/licenses/by-nc-sa/3.0 This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 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 Research Article
Yamada, Masatoshi
Saito, Akira
Yamamoto, Yoichiro
Cosatto, Eric
Kurata, Atsushi
Nagao, Toshitaka
Tateishi, Ayako
Kuroda, Masahiko
Quantitative nucleic features are effective for discrimination of intraductal proliferative lesions of the breast
title Quantitative nucleic features are effective for discrimination of intraductal proliferative lesions of the breast
title_full Quantitative nucleic features are effective for discrimination of intraductal proliferative lesions of the breast
title_fullStr Quantitative nucleic features are effective for discrimination of intraductal proliferative lesions of the breast
title_full_unstemmed Quantitative nucleic features are effective for discrimination of intraductal proliferative lesions of the breast
title_short Quantitative nucleic features are effective for discrimination of intraductal proliferative lesions of the breast
title_sort quantitative nucleic features are effective for discrimination of intraductal proliferative lesions of the breast
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4763509/
https://www.ncbi.nlm.nih.gov/pubmed/26955499
http://dx.doi.org/10.4103/2153-3539.175380
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