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BCR-Net: A deep learning framework to predict breast cancer recurrence from histopathology images

Breast cancer is the most common malignancy in women, with over 40,000 deaths annually in the United States alone. Clinicians often rely on the breast cancer recurrence score, Oncotype DX (ODX), for risk stratification of breast cancer patients, by using ODX as a guide for personalized therapy. Howe...

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Autores principales: Su, Ziyu, Niazi, Muhammad Khalid Khan, Tavolara, Thomas E., Niu, Shuo, Tozbikian, Gary H., Wesolowski, Robert, Gurcan, Metin N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10072418/
https://www.ncbi.nlm.nih.gov/pubmed/37014891
http://dx.doi.org/10.1371/journal.pone.0283562
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author Su, Ziyu
Niazi, Muhammad Khalid Khan
Tavolara, Thomas E.
Niu, Shuo
Tozbikian, Gary H.
Wesolowski, Robert
Gurcan, Metin N.
author_facet Su, Ziyu
Niazi, Muhammad Khalid Khan
Tavolara, Thomas E.
Niu, Shuo
Tozbikian, Gary H.
Wesolowski, Robert
Gurcan, Metin N.
author_sort Su, Ziyu
collection PubMed
description Breast cancer is the most common malignancy in women, with over 40,000 deaths annually in the United States alone. Clinicians often rely on the breast cancer recurrence score, Oncotype DX (ODX), for risk stratification of breast cancer patients, by using ODX as a guide for personalized therapy. However, ODX and similar gene assays are expensive, time-consuming, and tissue destructive. Therefore, developing an AI-based ODX prediction model that identifies patients who will benefit from chemotherapy in the same way that ODX does would give a low-cost alternative to the genomic test. To overcome this problem, we developed a deep learning framework, Breast Cancer Recurrence Network (BCR-Net), which automatically predicts ODX recurrence risk from histopathology slides. Our proposed framework has two steps. First, it intelligently samples discriminative features from whole-slide histopathology images of breast cancer patients. Then, it automatically weights all features through a multiple instance learning model to predict the recurrence score at the slide level. On a dataset of H&E and Ki67 breast cancer resection whole slides images (WSIs) from 99 anonymized patients, the proposed framework achieved an overall AUC of 0.775 (68.9% and 71.1% accuracies for low and high risk) on H&E WSIs and overall AUC of 0.811 (80.8% and 79.2% accuracies for low and high risk) on Ki67 WSIs of breast cancer patients. Our findings provide strong evidence for automatically risk-stratify patients with a high degree of confidence. Our experiments reveal that the BCR-Net outperforms the state-of-the-art WSI classification models. Moreover, BCR-Net is highly efficient with low computational needs, making it practical to deploy in limited computational settings.
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spelling pubmed-100724182023-04-05 BCR-Net: A deep learning framework to predict breast cancer recurrence from histopathology images Su, Ziyu Niazi, Muhammad Khalid Khan Tavolara, Thomas E. Niu, Shuo Tozbikian, Gary H. Wesolowski, Robert Gurcan, Metin N. PLoS One Research Article Breast cancer is the most common malignancy in women, with over 40,000 deaths annually in the United States alone. Clinicians often rely on the breast cancer recurrence score, Oncotype DX (ODX), for risk stratification of breast cancer patients, by using ODX as a guide for personalized therapy. However, ODX and similar gene assays are expensive, time-consuming, and tissue destructive. Therefore, developing an AI-based ODX prediction model that identifies patients who will benefit from chemotherapy in the same way that ODX does would give a low-cost alternative to the genomic test. To overcome this problem, we developed a deep learning framework, Breast Cancer Recurrence Network (BCR-Net), which automatically predicts ODX recurrence risk from histopathology slides. Our proposed framework has two steps. First, it intelligently samples discriminative features from whole-slide histopathology images of breast cancer patients. Then, it automatically weights all features through a multiple instance learning model to predict the recurrence score at the slide level. On a dataset of H&E and Ki67 breast cancer resection whole slides images (WSIs) from 99 anonymized patients, the proposed framework achieved an overall AUC of 0.775 (68.9% and 71.1% accuracies for low and high risk) on H&E WSIs and overall AUC of 0.811 (80.8% and 79.2% accuracies for low and high risk) on Ki67 WSIs of breast cancer patients. Our findings provide strong evidence for automatically risk-stratify patients with a high degree of confidence. Our experiments reveal that the BCR-Net outperforms the state-of-the-art WSI classification models. Moreover, BCR-Net is highly efficient with low computational needs, making it practical to deploy in limited computational settings. Public Library of Science 2023-04-04 /pmc/articles/PMC10072418/ /pubmed/37014891 http://dx.doi.org/10.1371/journal.pone.0283562 Text en © 2023 Su et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Su, Ziyu
Niazi, Muhammad Khalid Khan
Tavolara, Thomas E.
Niu, Shuo
Tozbikian, Gary H.
Wesolowski, Robert
Gurcan, Metin N.
BCR-Net: A deep learning framework to predict breast cancer recurrence from histopathology images
title BCR-Net: A deep learning framework to predict breast cancer recurrence from histopathology images
title_full BCR-Net: A deep learning framework to predict breast cancer recurrence from histopathology images
title_fullStr BCR-Net: A deep learning framework to predict breast cancer recurrence from histopathology images
title_full_unstemmed BCR-Net: A deep learning framework to predict breast cancer recurrence from histopathology images
title_short BCR-Net: A deep learning framework to predict breast cancer recurrence from histopathology images
title_sort bcr-net: a deep learning framework to predict breast cancer recurrence from histopathology images
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10072418/
https://www.ncbi.nlm.nih.gov/pubmed/37014891
http://dx.doi.org/10.1371/journal.pone.0283562
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