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MLCD: A Unified Software Package for Cancer Diagnosis

PURPOSE: Machine Learning Package for Cancer Diagnosis (MLCD) is the result of a National Institutes of Health/National Cancer Institute (NIH/NCI)-sponsored project for developing a unified software package from state-of-the-art breast cancer biopsy diagnosis and machine learning algorithms that can...

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Detalles Bibliográficos
Autores principales: Wu, Wenjun, Li, Beibin, Mercan, Ezgi, Mehta, Sachin, Bartlett, Jamen, Weaver, Donald L., Elmore, Joann G., Shapiro, Linda G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Society of Clinical Oncology 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7113135/
https://www.ncbi.nlm.nih.gov/pubmed/32216637
http://dx.doi.org/10.1200/CCI.19.00129
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author Wu, Wenjun
Li, Beibin
Mercan, Ezgi
Mehta, Sachin
Bartlett, Jamen
Weaver, Donald L.
Elmore, Joann G.
Shapiro, Linda G.
author_facet Wu, Wenjun
Li, Beibin
Mercan, Ezgi
Mehta, Sachin
Bartlett, Jamen
Weaver, Donald L.
Elmore, Joann G.
Shapiro, Linda G.
author_sort Wu, Wenjun
collection PubMed
description PURPOSE: Machine Learning Package for Cancer Diagnosis (MLCD) is the result of a National Institutes of Health/National Cancer Institute (NIH/NCI)-sponsored project for developing a unified software package from state-of-the-art breast cancer biopsy diagnosis and machine learning algorithms that can improve the quality of both clinical practice and ongoing research. METHODS: Whole-slide images of 240 well-characterized breast biopsy cases, initially assembled under R01 CA140560, were used for developing the algorithms and training the machine learning models. This software package is based on the methodology developed and published under our recent NIH/NCI-sponsored research grant (R01 CA172343) for finding regions of interest (ROIs) in whole-slide breast biopsy images, for segmenting ROIs into histopathologic tissue types and for using this segmentation in classifiers that can suggest final diagnoses. RESULT: The package provides an ROI detector for whole-slide images and modules for semantic segmentation into tissue classes and diagnostic classification into 4 classes (benign, atypia, ductal carcinoma in situ, invasive cancer) of the ROIs. It is available through the GitHub repository under the Massachusetts Institute of Technology license and will later be distributed with the Pathology Image Informatics Platform system. A Web page provides instructions for use. CONCLUSION: Our tools have the potential to provide help to other cancer researchers and, ultimately, to practicing physicians and will motivate future research in this field. This article describes the methodology behind the software development and gives sample outputs to guide those interested in using this package.
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spelling pubmed-71131352021-03-27 MLCD: A Unified Software Package for Cancer Diagnosis Wu, Wenjun Li, Beibin Mercan, Ezgi Mehta, Sachin Bartlett, Jamen Weaver, Donald L. Elmore, Joann G. Shapiro, Linda G. JCO Clin Cancer Inform ORIGINAL REPORTS PURPOSE: Machine Learning Package for Cancer Diagnosis (MLCD) is the result of a National Institutes of Health/National Cancer Institute (NIH/NCI)-sponsored project for developing a unified software package from state-of-the-art breast cancer biopsy diagnosis and machine learning algorithms that can improve the quality of both clinical practice and ongoing research. METHODS: Whole-slide images of 240 well-characterized breast biopsy cases, initially assembled under R01 CA140560, were used for developing the algorithms and training the machine learning models. This software package is based on the methodology developed and published under our recent NIH/NCI-sponsored research grant (R01 CA172343) for finding regions of interest (ROIs) in whole-slide breast biopsy images, for segmenting ROIs into histopathologic tissue types and for using this segmentation in classifiers that can suggest final diagnoses. RESULT: The package provides an ROI detector for whole-slide images and modules for semantic segmentation into tissue classes and diagnostic classification into 4 classes (benign, atypia, ductal carcinoma in situ, invasive cancer) of the ROIs. It is available through the GitHub repository under the Massachusetts Institute of Technology license and will later be distributed with the Pathology Image Informatics Platform system. A Web page provides instructions for use. CONCLUSION: Our tools have the potential to provide help to other cancer researchers and, ultimately, to practicing physicians and will motivate future research in this field. This article describes the methodology behind the software development and gives sample outputs to guide those interested in using this package. American Society of Clinical Oncology 2020-03-27 /pmc/articles/PMC7113135/ /pubmed/32216637 http://dx.doi.org/10.1200/CCI.19.00129 Text en © 2020 by American Society of Clinical Oncology https://creativecommons.org/licenses/by/4.0/ Licensed under the Creative Commons Attribution 4.0 License: https://creativecommons.org/licenses/by/4.0/
spellingShingle ORIGINAL REPORTS
Wu, Wenjun
Li, Beibin
Mercan, Ezgi
Mehta, Sachin
Bartlett, Jamen
Weaver, Donald L.
Elmore, Joann G.
Shapiro, Linda G.
MLCD: A Unified Software Package for Cancer Diagnosis
title MLCD: A Unified Software Package for Cancer Diagnosis
title_full MLCD: A Unified Software Package for Cancer Diagnosis
title_fullStr MLCD: A Unified Software Package for Cancer Diagnosis
title_full_unstemmed MLCD: A Unified Software Package for Cancer Diagnosis
title_short MLCD: A Unified Software Package for Cancer Diagnosis
title_sort mlcd: a unified software package for cancer diagnosis
topic ORIGINAL REPORTS
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7113135/
https://www.ncbi.nlm.nih.gov/pubmed/32216637
http://dx.doi.org/10.1200/CCI.19.00129
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