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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...

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Autores principales: 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.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Ivyspring International Publisher 2019
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.
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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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