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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...

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Autores principales: Hayward, Mary-Kate, Louise Jones, J., Hall, Allison, King, Lorraine, Ironside, Alastair J., Nelson, Andrew C., Shelley Hwang, E., Weaver, Valerie M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Research Network of Computational and Structural Biotechnology 2020
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.
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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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