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A deep learning model for molecular label transfer that enables cancer cell identification from histopathology images

Deep-learning classification systems have the potential to improve cancer diagnosis. However, development of these computational approaches so far depends on prior pathological annotations and large training datasets. The manual annotation is low-resolution, time-consuming, highly variable and subje...

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Autores principales: Su, Andrew, Lee, HoJoon, Tan, Xiao, Suarez, Carlos J., Andor, Noemi, Nguyen, Quan, Ji, Hanlee P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8891271/
https://www.ncbi.nlm.nih.gov/pubmed/35236916
http://dx.doi.org/10.1038/s41698-022-00252-0
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author Su, Andrew
Lee, HoJoon
Tan, Xiao
Suarez, Carlos J.
Andor, Noemi
Nguyen, Quan
Ji, Hanlee P.
author_facet Su, Andrew
Lee, HoJoon
Tan, Xiao
Suarez, Carlos J.
Andor, Noemi
Nguyen, Quan
Ji, Hanlee P.
author_sort Su, Andrew
collection PubMed
description Deep-learning classification systems have the potential to improve cancer diagnosis. However, development of these computational approaches so far depends on prior pathological annotations and large training datasets. The manual annotation is low-resolution, time-consuming, highly variable and subject to observer variance. To address this issue, we developed a method, H&E Molecular neural network (HEMnet). HEMnet utilizes immunohistochemistry as an initial molecular label for cancer cells on a H&E image and trains a cancer classifier on the overlapping clinical histopathological images. Using this molecular transfer method, HEMnet successfully generated and labeled 21,939 tumor and 8782 normal tiles from ten whole-slide images for model training. After building the model, HEMnet accurately identified colorectal cancer regions, which achieved 0.84 and 0.73 of ROC AUC values compared to p53 staining and pathological annotations, respectively. Our validation study using histopathology images from TCGA samples accurately estimated tumor purity, which showed a significant correlation (regression coefficient of 0.8) with the estimation based on genomic sequencing data. Thus, HEMnet contributes to addressing two main challenges in cancer deep-learning analysis, namely the need to have a large number of images for training and the dependence on manual labeling by a pathologist. HEMnet also predicts cancer cells at a much higher resolution compared to manual histopathologic evaluation. Overall, our method provides a path towards a fully automated delineation of any type of tumor so long as there is a cancer-oriented molecular stain available for subsequent learning. Software, tutorials and interactive tools are available at:https://github.com/BiomedicalMachineLearning/HEMnet
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spelling pubmed-88912712022-03-17 A deep learning model for molecular label transfer that enables cancer cell identification from histopathology images Su, Andrew Lee, HoJoon Tan, Xiao Suarez, Carlos J. Andor, Noemi Nguyen, Quan Ji, Hanlee P. NPJ Precis Oncol Article Deep-learning classification systems have the potential to improve cancer diagnosis. However, development of these computational approaches so far depends on prior pathological annotations and large training datasets. The manual annotation is low-resolution, time-consuming, highly variable and subject to observer variance. To address this issue, we developed a method, H&E Molecular neural network (HEMnet). HEMnet utilizes immunohistochemistry as an initial molecular label for cancer cells on a H&E image and trains a cancer classifier on the overlapping clinical histopathological images. Using this molecular transfer method, HEMnet successfully generated and labeled 21,939 tumor and 8782 normal tiles from ten whole-slide images for model training. After building the model, HEMnet accurately identified colorectal cancer regions, which achieved 0.84 and 0.73 of ROC AUC values compared to p53 staining and pathological annotations, respectively. Our validation study using histopathology images from TCGA samples accurately estimated tumor purity, which showed a significant correlation (regression coefficient of 0.8) with the estimation based on genomic sequencing data. Thus, HEMnet contributes to addressing two main challenges in cancer deep-learning analysis, namely the need to have a large number of images for training and the dependence on manual labeling by a pathologist. HEMnet also predicts cancer cells at a much higher resolution compared to manual histopathologic evaluation. Overall, our method provides a path towards a fully automated delineation of any type of tumor so long as there is a cancer-oriented molecular stain available for subsequent learning. Software, tutorials and interactive tools are available at:https://github.com/BiomedicalMachineLearning/HEMnet Nature Publishing Group UK 2022-03-02 /pmc/articles/PMC8891271/ /pubmed/35236916 http://dx.doi.org/10.1038/s41698-022-00252-0 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Su, Andrew
Lee, HoJoon
Tan, Xiao
Suarez, Carlos J.
Andor, Noemi
Nguyen, Quan
Ji, Hanlee P.
A deep learning model for molecular label transfer that enables cancer cell identification from histopathology images
title A deep learning model for molecular label transfer that enables cancer cell identification from histopathology images
title_full A deep learning model for molecular label transfer that enables cancer cell identification from histopathology images
title_fullStr A deep learning model for molecular label transfer that enables cancer cell identification from histopathology images
title_full_unstemmed A deep learning model for molecular label transfer that enables cancer cell identification from histopathology images
title_short A deep learning model for molecular label transfer that enables cancer cell identification from histopathology images
title_sort deep learning model for molecular label transfer that enables cancer cell identification from histopathology images
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8891271/
https://www.ncbi.nlm.nih.gov/pubmed/35236916
http://dx.doi.org/10.1038/s41698-022-00252-0
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