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Improving the accuracy of gastrointestinal neuroendocrine tumor grading with deep learning
The Ki-67 index is an established prognostic factor in gastrointestinal neuroendocrine tumors (GI-NETs) and defines tumor grade. It is currently estimated by microscopically examining tumor tissue single-immunostained (SS) for Ki-67 and counting the number of Ki-67-positive and Ki-67-negative tumor...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7338406/ https://www.ncbi.nlm.nih.gov/pubmed/32632119 http://dx.doi.org/10.1038/s41598-020-67880-z |
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author | Govind, Darshana Jen, Kuang-Yu Matsukuma, Karen Gao, Guofeng Olson, Kristin A. Gui, Dorina Wilding, Gregory. E. Border, Samuel P. Sarder, Pinaki |
author_facet | Govind, Darshana Jen, Kuang-Yu Matsukuma, Karen Gao, Guofeng Olson, Kristin A. Gui, Dorina Wilding, Gregory. E. Border, Samuel P. Sarder, Pinaki |
author_sort | Govind, Darshana |
collection | PubMed |
description | The Ki-67 index is an established prognostic factor in gastrointestinal neuroendocrine tumors (GI-NETs) and defines tumor grade. It is currently estimated by microscopically examining tumor tissue single-immunostained (SS) for Ki-67 and counting the number of Ki-67-positive and Ki-67-negative tumor cells within a subjectively picked hot-spot. Intraobserver variability in this procedure as well as difficulty in distinguishing tumor from non-tumor cells can lead to inaccurate Ki-67 indices and possibly incorrect tumor grades. We introduce two computational tools that utilize Ki-67 and synaptophysin double-immunostained (DS) slides to improve the accuracy of Ki-67 index quantitation in GI-NETs: (1) Synaptophysin-KI-Estimator (SKIE), a pipeline automating Ki-67 index quantitation via whole-slide image (WSI) analysis and (2) deep-SKIE, a deep learner-based approach where a Ki-67 index heatmap is generated throughout the tumor. Ki-67 indices for 50 GI-NETs were quantitated using SKIE and compared with DS slide assessments by three pathologists using a microscope and a fourth pathologist via manually ticking off each cell, the latter of which was deemed the gold standard (GS). Compared to the GS, SKIE achieved a grading accuracy of 90% and substantial agreement (linear-weighted Cohen’s kappa 0.62). Using DS WSIs, deep-SKIE displayed a training, validation, and testing accuracy of 98.4%, 90.9%, and 91.0%, respectively, significantly higher than using SS WSIs. Since DS slides are not standard clinical practice, we also integrated a cycle generative adversarial network into our pipeline to transform SS into DS WSIs. The proposed methods can improve accuracy and potentially save a significant amount of time if implemented into clinical practice. |
format | Online Article Text |
id | pubmed-7338406 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-73384062020-07-07 Improving the accuracy of gastrointestinal neuroendocrine tumor grading with deep learning Govind, Darshana Jen, Kuang-Yu Matsukuma, Karen Gao, Guofeng Olson, Kristin A. Gui, Dorina Wilding, Gregory. E. Border, Samuel P. Sarder, Pinaki Sci Rep Article The Ki-67 index is an established prognostic factor in gastrointestinal neuroendocrine tumors (GI-NETs) and defines tumor grade. It is currently estimated by microscopically examining tumor tissue single-immunostained (SS) for Ki-67 and counting the number of Ki-67-positive and Ki-67-negative tumor cells within a subjectively picked hot-spot. Intraobserver variability in this procedure as well as difficulty in distinguishing tumor from non-tumor cells can lead to inaccurate Ki-67 indices and possibly incorrect tumor grades. We introduce two computational tools that utilize Ki-67 and synaptophysin double-immunostained (DS) slides to improve the accuracy of Ki-67 index quantitation in GI-NETs: (1) Synaptophysin-KI-Estimator (SKIE), a pipeline automating Ki-67 index quantitation via whole-slide image (WSI) analysis and (2) deep-SKIE, a deep learner-based approach where a Ki-67 index heatmap is generated throughout the tumor. Ki-67 indices for 50 GI-NETs were quantitated using SKIE and compared with DS slide assessments by three pathologists using a microscope and a fourth pathologist via manually ticking off each cell, the latter of which was deemed the gold standard (GS). Compared to the GS, SKIE achieved a grading accuracy of 90% and substantial agreement (linear-weighted Cohen’s kappa 0.62). Using DS WSIs, deep-SKIE displayed a training, validation, and testing accuracy of 98.4%, 90.9%, and 91.0%, respectively, significantly higher than using SS WSIs. Since DS slides are not standard clinical practice, we also integrated a cycle generative adversarial network into our pipeline to transform SS into DS WSIs. The proposed methods can improve accuracy and potentially save a significant amount of time if implemented into clinical practice. Nature Publishing Group UK 2020-07-06 /pmc/articles/PMC7338406/ /pubmed/32632119 http://dx.doi.org/10.1038/s41598-020-67880-z Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Govind, Darshana Jen, Kuang-Yu Matsukuma, Karen Gao, Guofeng Olson, Kristin A. Gui, Dorina Wilding, Gregory. E. Border, Samuel P. Sarder, Pinaki Improving the accuracy of gastrointestinal neuroendocrine tumor grading with deep learning |
title | Improving the accuracy of gastrointestinal neuroendocrine tumor grading with deep learning |
title_full | Improving the accuracy of gastrointestinal neuroendocrine tumor grading with deep learning |
title_fullStr | Improving the accuracy of gastrointestinal neuroendocrine tumor grading with deep learning |
title_full_unstemmed | Improving the accuracy of gastrointestinal neuroendocrine tumor grading with deep learning |
title_short | Improving the accuracy of gastrointestinal neuroendocrine tumor grading with deep learning |
title_sort | improving the accuracy of gastrointestinal neuroendocrine tumor grading with deep learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7338406/ https://www.ncbi.nlm.nih.gov/pubmed/32632119 http://dx.doi.org/10.1038/s41598-020-67880-z |
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