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Point-of-care cervical cancer screening using deep learning-based microholography
Most deaths (80%) from cervical cancer occur in regions lacking adequate screening infrastructures or ready access to them. In contrast, most developed countries now embrace human papillomavirus (HPV) analyses as standalone screening; this transition threatens to further widen the resource gap. Meth...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Ivyspring International Publisher
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6924258/ https://www.ncbi.nlm.nih.gov/pubmed/31879529 http://dx.doi.org/10.7150/thno.37187 |
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author | Pathania, Divya Landeros, Christian Rohrer, Lucas D'Agostino, Victoria Hong, Seonki Degani, Ismail Avila-Wallace, Maria Pivovarov, Misha Randall, Thomas Weissleder, Ralph Lee, Hakho Im, Hyungsoon Castro, Cesar M. |
author_facet | Pathania, Divya Landeros, Christian Rohrer, Lucas D'Agostino, Victoria Hong, Seonki Degani, Ismail Avila-Wallace, Maria Pivovarov, Misha Randall, Thomas Weissleder, Ralph Lee, Hakho Im, Hyungsoon Castro, Cesar M. |
author_sort | Pathania, Divya |
collection | PubMed |
description | Most deaths (80%) from cervical cancer occur in regions lacking adequate screening infrastructures or ready access to them. In contrast, most developed countries now embrace human papillomavirus (HPV) analyses as standalone screening; this transition threatens to further widen the resource gap. Methods: We describe the development of a DNA-focused digital microholography platform for point-of-care HPV screening, with automated readouts driven by customized deep-learning algorithms. In the presence of high-risk HPV 16 or 18 DNA, microbeads were designed to bind the DNA targets and form microbead dimers. The resulting holographic signature of the microbeads was recorded and analyzed. Results: The HPV DNA assay showed excellent sensitivity (down to a single cell) and specificity (100% concordance) in detecting HPV 16 and 18 DNA from cell lines. Our deep learning approach was 120-folder faster than the traditional reconstruction method and completed the analysis in < 2 min using a single CPU. In a blinded clinical study using patient cervical brushings, we successfully benchmarked our platform's performance to an FDA-approved HPV assay. Conclusions: Reliable and decentralized HPV testing will facilitate cataloguing the high-risk HPV landscape in underserved populations, revealing HPV coverage gaps in existing vaccination strategies and informing future iterations. |
format | Online Article Text |
id | pubmed-6924258 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Ivyspring International Publisher |
record_format | MEDLINE/PubMed |
spelling | pubmed-69242582019-12-26 Point-of-care cervical cancer screening using deep learning-based microholography Pathania, Divya Landeros, Christian Rohrer, Lucas D'Agostino, Victoria Hong, Seonki Degani, Ismail Avila-Wallace, Maria Pivovarov, Misha Randall, Thomas Weissleder, Ralph Lee, Hakho Im, Hyungsoon Castro, Cesar M. Theranostics Research Paper Most deaths (80%) from cervical cancer occur in regions lacking adequate screening infrastructures or ready access to them. In contrast, most developed countries now embrace human papillomavirus (HPV) analyses as standalone screening; this transition threatens to further widen the resource gap. Methods: We describe the development of a DNA-focused digital microholography platform for point-of-care HPV screening, with automated readouts driven by customized deep-learning algorithms. In the presence of high-risk HPV 16 or 18 DNA, microbeads were designed to bind the DNA targets and form microbead dimers. The resulting holographic signature of the microbeads was recorded and analyzed. Results: The HPV DNA assay showed excellent sensitivity (down to a single cell) and specificity (100% concordance) in detecting HPV 16 and 18 DNA from cell lines. Our deep learning approach was 120-folder faster than the traditional reconstruction method and completed the analysis in < 2 min using a single CPU. In a blinded clinical study using patient cervical brushings, we successfully benchmarked our platform's performance to an FDA-approved HPV assay. Conclusions: Reliable and decentralized HPV testing will facilitate cataloguing the high-risk HPV landscape in underserved populations, revealing HPV coverage gaps in existing vaccination strategies and informing future iterations. Ivyspring International Publisher 2019-11-26 /pmc/articles/PMC6924258/ /pubmed/31879529 http://dx.doi.org/10.7150/thno.37187 Text en © The author(s) This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/). See http://ivyspring.com/terms for full terms and conditions. |
spellingShingle | Research Paper Pathania, Divya Landeros, Christian Rohrer, Lucas D'Agostino, Victoria Hong, Seonki Degani, Ismail Avila-Wallace, Maria Pivovarov, Misha Randall, Thomas Weissleder, Ralph Lee, Hakho Im, Hyungsoon Castro, Cesar M. Point-of-care cervical cancer screening using deep learning-based microholography |
title | Point-of-care cervical cancer screening using deep learning-based microholography |
title_full | Point-of-care cervical cancer screening using deep learning-based microholography |
title_fullStr | Point-of-care cervical cancer screening using deep learning-based microholography |
title_full_unstemmed | Point-of-care cervical cancer screening using deep learning-based microholography |
title_short | Point-of-care cervical cancer screening using deep learning-based microholography |
title_sort | point-of-care cervical cancer screening using deep learning-based microholography |
topic | Research Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6924258/ https://www.ncbi.nlm.nih.gov/pubmed/31879529 http://dx.doi.org/10.7150/thno.37187 |
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