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Derivation of a nuclear heterogeneity image index to grade DCIS
Abnormalities in cell nuclear morphology are a hallmark of cancer. Histological assessment of cell nuclear morphology is frequently used by pathologists to grade ductal carcinoma in situ (DCIS). Objective methods that allow standardization and reproducibility of cell nuclear morphology assessment ha...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Research Network of Computational and Structural Biotechnology
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7744935/ https://www.ncbi.nlm.nih.gov/pubmed/33363702 http://dx.doi.org/10.1016/j.csbj.2020.11.040 |
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author | Hayward, Mary-Kate Louise Jones, J. Hall, Allison King, Lorraine Ironside, Alastair J. Nelson, Andrew C. Shelley Hwang, E. Weaver, Valerie M. |
author_facet | Hayward, Mary-Kate Louise Jones, J. Hall, Allison King, Lorraine Ironside, Alastair J. Nelson, Andrew C. Shelley Hwang, E. Weaver, Valerie M. |
author_sort | Hayward, Mary-Kate |
collection | PubMed |
description | Abnormalities in cell nuclear morphology are a hallmark of cancer. Histological assessment of cell nuclear morphology is frequently used by pathologists to grade ductal carcinoma in situ (DCIS). Objective methods that allow standardization and reproducibility of cell nuclear morphology assessment have potential to improve the criteria needed to predict DCIS progression and recurrence. Aggressive cancers are highly heterogeneous. We asked whether cell nuclear morphology heterogeneity could be incorporated into a metric to classify DCIS. We developed a nuclear heterogeneity image index to objectively, and quantitatively grade DCIS. A whole-tissue cell nuclear morphological analysis, that classified tumors by the worst ten percent in a duct-by-duct manner, identified nuclear size ranges associated with each DCIS grade. Digital image analysis further revealed increasing heterogeneity within ducts or between ducts in tissues of worsening DCIS grade. The findings illustrate how digital image analysis comprises a supplemental tool for pathologists to objectively classify DCIS and in the future, may provide a method to predict patient outcome through analysis of nuclear heterogeneity. |
format | Online Article Text |
id | pubmed-7744935 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Research Network of Computational and Structural Biotechnology |
record_format | MEDLINE/PubMed |
spelling | pubmed-77449352020-12-23 Derivation of a nuclear heterogeneity image index to grade DCIS Hayward, Mary-Kate Louise Jones, J. Hall, Allison King, Lorraine Ironside, Alastair J. Nelson, Andrew C. Shelley Hwang, E. Weaver, Valerie M. Comput Struct Biotechnol J Research Article Abnormalities in cell nuclear morphology are a hallmark of cancer. Histological assessment of cell nuclear morphology is frequently used by pathologists to grade ductal carcinoma in situ (DCIS). Objective methods that allow standardization and reproducibility of cell nuclear morphology assessment have potential to improve the criteria needed to predict DCIS progression and recurrence. Aggressive cancers are highly heterogeneous. We asked whether cell nuclear morphology heterogeneity could be incorporated into a metric to classify DCIS. We developed a nuclear heterogeneity image index to objectively, and quantitatively grade DCIS. A whole-tissue cell nuclear morphological analysis, that classified tumors by the worst ten percent in a duct-by-duct manner, identified nuclear size ranges associated with each DCIS grade. Digital image analysis further revealed increasing heterogeneity within ducts or between ducts in tissues of worsening DCIS grade. The findings illustrate how digital image analysis comprises a supplemental tool for pathologists to objectively classify DCIS and in the future, may provide a method to predict patient outcome through analysis of nuclear heterogeneity. Research Network of Computational and Structural Biotechnology 2020-12-03 /pmc/articles/PMC7744935/ /pubmed/33363702 http://dx.doi.org/10.1016/j.csbj.2020.11.040 Text en © 2020 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Research Article Hayward, Mary-Kate Louise Jones, J. Hall, Allison King, Lorraine Ironside, Alastair J. Nelson, Andrew C. Shelley Hwang, E. Weaver, Valerie M. Derivation of a nuclear heterogeneity image index to grade DCIS |
title | Derivation of a nuclear heterogeneity image index to grade DCIS |
title_full | Derivation of a nuclear heterogeneity image index to grade DCIS |
title_fullStr | Derivation of a nuclear heterogeneity image index to grade DCIS |
title_full_unstemmed | Derivation of a nuclear heterogeneity image index to grade DCIS |
title_short | Derivation of a nuclear heterogeneity image index to grade DCIS |
title_sort | derivation of a nuclear heterogeneity image index to grade dcis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7744935/ https://www.ncbi.nlm.nih.gov/pubmed/33363702 http://dx.doi.org/10.1016/j.csbj.2020.11.040 |
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