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Applying Data-driven Imaging Biomarker in Mammography for Breast Cancer Screening: Preliminary Study

We assessed the feasibility of a data-driven imaging biomarker based on weakly supervised learning (DIB; an imaging biomarker derived from large-scale medical image data with deep learning technology) in mammography (DIB-MG). A total of 29,107 digital mammograms from five institutions (4,339 cancer...

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Autores principales: Kim, Eun-Kyung, Kim, Hyo-Eun, Han, Kyunghwa, Kang, Bong Joo, Sohn, Yu-Mee, Woo, Ok Hee, Lee, Chan Wha
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5807343/
https://www.ncbi.nlm.nih.gov/pubmed/29426948
http://dx.doi.org/10.1038/s41598-018-21215-1
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author Kim, Eun-Kyung
Kim, Hyo-Eun
Han, Kyunghwa
Kang, Bong Joo
Sohn, Yu-Mee
Woo, Ok Hee
Lee, Chan Wha
author_facet Kim, Eun-Kyung
Kim, Hyo-Eun
Han, Kyunghwa
Kang, Bong Joo
Sohn, Yu-Mee
Woo, Ok Hee
Lee, Chan Wha
author_sort Kim, Eun-Kyung
collection PubMed
description We assessed the feasibility of a data-driven imaging biomarker based on weakly supervised learning (DIB; an imaging biomarker derived from large-scale medical image data with deep learning technology) in mammography (DIB-MG). A total of 29,107 digital mammograms from five institutions (4,339 cancer cases and 24,768 normal cases) were included. After matching patients’ age, breast density, and equipment, 1,238 and 1,238 cases were chosen as validation and test sets, respectively, and the remainder were used for training. The core algorithm of DIB-MG is a deep convolutional neural network; a deep learning algorithm specialized for images. Each sample (case) is an exam composed of 4-view images (RCC, RMLO, LCC, and LMLO). For each case in a training set, the cancer probability inferred from DIB-MG is compared with the per-case ground-truth label. Then the model parameters in DIB-MG are updated based on the error between the prediction and the ground-truth. At the operating point (threshold) of 0.5, sensitivity was 75.6% and 76.1% when specificity was 90.2% and 88.5%, and AUC was 0.903 and 0.906 for the validation and test sets, respectively. This research showed the potential of DIB-MG as a screening tool for breast cancer.
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spelling pubmed-58073432018-02-14 Applying Data-driven Imaging Biomarker in Mammography for Breast Cancer Screening: Preliminary Study Kim, Eun-Kyung Kim, Hyo-Eun Han, Kyunghwa Kang, Bong Joo Sohn, Yu-Mee Woo, Ok Hee Lee, Chan Wha Sci Rep Article We assessed the feasibility of a data-driven imaging biomarker based on weakly supervised learning (DIB; an imaging biomarker derived from large-scale medical image data with deep learning technology) in mammography (DIB-MG). A total of 29,107 digital mammograms from five institutions (4,339 cancer cases and 24,768 normal cases) were included. After matching patients’ age, breast density, and equipment, 1,238 and 1,238 cases were chosen as validation and test sets, respectively, and the remainder were used for training. The core algorithm of DIB-MG is a deep convolutional neural network; a deep learning algorithm specialized for images. Each sample (case) is an exam composed of 4-view images (RCC, RMLO, LCC, and LMLO). For each case in a training set, the cancer probability inferred from DIB-MG is compared with the per-case ground-truth label. Then the model parameters in DIB-MG are updated based on the error between the prediction and the ground-truth. At the operating point (threshold) of 0.5, sensitivity was 75.6% and 76.1% when specificity was 90.2% and 88.5%, and AUC was 0.903 and 0.906 for the validation and test sets, respectively. This research showed the potential of DIB-MG as a screening tool for breast cancer. Nature Publishing Group UK 2018-02-09 /pmc/articles/PMC5807343/ /pubmed/29426948 http://dx.doi.org/10.1038/s41598-018-21215-1 Text en © The Author(s) 2018 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
Kim, Eun-Kyung
Kim, Hyo-Eun
Han, Kyunghwa
Kang, Bong Joo
Sohn, Yu-Mee
Woo, Ok Hee
Lee, Chan Wha
Applying Data-driven Imaging Biomarker in Mammography for Breast Cancer Screening: Preliminary Study
title Applying Data-driven Imaging Biomarker in Mammography for Breast Cancer Screening: Preliminary Study
title_full Applying Data-driven Imaging Biomarker in Mammography for Breast Cancer Screening: Preliminary Study
title_fullStr Applying Data-driven Imaging Biomarker in Mammography for Breast Cancer Screening: Preliminary Study
title_full_unstemmed Applying Data-driven Imaging Biomarker in Mammography for Breast Cancer Screening: Preliminary Study
title_short Applying Data-driven Imaging Biomarker in Mammography for Breast Cancer Screening: Preliminary Study
title_sort applying data-driven imaging biomarker in mammography for breast cancer screening: preliminary study
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5807343/
https://www.ncbi.nlm.nih.gov/pubmed/29426948
http://dx.doi.org/10.1038/s41598-018-21215-1
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