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Stratifying Ovarian Cancer Risk Using Personal Health Data
Purpose: Screening the general population for ovarian cancer is not recommended by every major medical or public health organization because the harms from screening outweigh the benefit it provides. To improve ovarian cancer detection and survival many are looking at high-risk populations who would...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7931902/ https://www.ncbi.nlm.nih.gov/pubmed/33693347 http://dx.doi.org/10.3389/fdata.2019.00024 |
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author | Hart, Gregory R. Nartowt, Bradley J. Muhammad, Wazir Liang, Ying Huang, Gloria S. Deng, Jun |
author_facet | Hart, Gregory R. Nartowt, Bradley J. Muhammad, Wazir Liang, Ying Huang, Gloria S. Deng, Jun |
author_sort | Hart, Gregory R. |
collection | PubMed |
description | Purpose: Screening the general population for ovarian cancer is not recommended by every major medical or public health organization because the harms from screening outweigh the benefit it provides. To improve ovarian cancer detection and survival many are looking at high-risk populations who would benefit from screening. Methods: We train a neural network on readily available personal health data to predict and stratify ovarian cancer risk. We use two different datasets to train our network: The National Health Interview Survey and Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. Results: Our model has an area under the receiver operating characteristic curve of 0.71. We further demonstrate how the model could be used to stratify patients into different risk categories. A simple 3-tier scheme classifies 23.8% of those with cancer and 1.0% of those without as high-risk similar to genetic testing, and 1.1% of those with cancer and 24.4% of those without as low risk. Conclusion: The developed neural network offers a cost-effective and non-invasive way to identify those who could benefit from targeted screening. |
format | Online Article Text |
id | pubmed-7931902 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-79319022021-03-09 Stratifying Ovarian Cancer Risk Using Personal Health Data Hart, Gregory R. Nartowt, Bradley J. Muhammad, Wazir Liang, Ying Huang, Gloria S. Deng, Jun Front Big Data Big Data Purpose: Screening the general population for ovarian cancer is not recommended by every major medical or public health organization because the harms from screening outweigh the benefit it provides. To improve ovarian cancer detection and survival many are looking at high-risk populations who would benefit from screening. Methods: We train a neural network on readily available personal health data to predict and stratify ovarian cancer risk. We use two different datasets to train our network: The National Health Interview Survey and Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. Results: Our model has an area under the receiver operating characteristic curve of 0.71. We further demonstrate how the model could be used to stratify patients into different risk categories. A simple 3-tier scheme classifies 23.8% of those with cancer and 1.0% of those without as high-risk similar to genetic testing, and 1.1% of those with cancer and 24.4% of those without as low risk. Conclusion: The developed neural network offers a cost-effective and non-invasive way to identify those who could benefit from targeted screening. Frontiers Media S.A. 2019-07-02 /pmc/articles/PMC7931902/ /pubmed/33693347 http://dx.doi.org/10.3389/fdata.2019.00024 Text en Copyright © 2019 Hart, Nartowt, Muhammad, Liang, Huang and Deng. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Big Data Hart, Gregory R. Nartowt, Bradley J. Muhammad, Wazir Liang, Ying Huang, Gloria S. Deng, Jun Stratifying Ovarian Cancer Risk Using Personal Health Data |
title | Stratifying Ovarian Cancer Risk Using Personal Health Data |
title_full | Stratifying Ovarian Cancer Risk Using Personal Health Data |
title_fullStr | Stratifying Ovarian Cancer Risk Using Personal Health Data |
title_full_unstemmed | Stratifying Ovarian Cancer Risk Using Personal Health Data |
title_short | Stratifying Ovarian Cancer Risk Using Personal Health Data |
title_sort | stratifying ovarian cancer risk using personal health data |
topic | Big Data |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7931902/ https://www.ncbi.nlm.nih.gov/pubmed/33693347 http://dx.doi.org/10.3389/fdata.2019.00024 |
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