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Theranostics 2019; 9(26):8438-8447. doi:10.7150/thno.37187 This issue Cite

Research Paper

Point-of-care cervical cancer screening using deep learning-based microholography

Divya Pathania1#, Christian Landeros1,2#, Lucas Rohrer1,3, Victoria D'Agostino1,4, Seonki Hong1, Ismail Degani1,5, Maria Avila-Wallace6, Misha Pivovarov1, Thomas Randall6, Ralph Weissleder1,7,8, Hakho Lee1,8, Hyungsoon Im1,8✉, Cesar M. Castro1,9✉

1. Center for Systems Biology, Massachusetts General Hospital, Boston, MA 02114, USA
2. Harvard-MIT Program in Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
3. Department of Health Sciences, Northeastern University, Boston, MA 02115, USA
4. Department of Bioengineering, Northeastern University, Boston, MA 02115, USA
5. Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
6. Department of Obstetrics and Gynecology, Massachusetts General Hospital, Boston, MA 02114, USA
7. Department of Systems Biology, Harvard Medical School, 200 Longwood Ave, Boston, MA 02115, USA
8. Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA
9. Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, MA 02114, USA
# These authors contributed equally to the manuscript

✉ Corresponding author: Hyungsoon Im, PhD. im.hyungsoonharvard.edu. or Cesar M. Castro, MD. castro.cesarharvard.edu Center for Systems Biology, Massachusetts General Hospital, 185 Cambridge St, 5th floor, Boston, MA 02114 (617) 643-3778

Citation:
Pathania D, Landeros C, Rohrer L, D'Agostino V, Hong S, Degani I, Avila-Wallace M, Pivovarov M, Randall T, Weissleder R, Lee H, Im H, Castro CM. Point-of-care cervical cancer screening using deep learning-based microholography. Theranostics 2019; 9(26):8438-8447. doi:10.7150/thno.37187. https://www.thno.org/v09p8438.htm
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Abstract

Graphic abstract

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.

Keywords: Cervical cancer, point-of-care screening, global oncology, microholography, deep learning

Citation styles

APA
Pathania, D., Landeros, C., Rohrer, L., D'Agostino, V., Hong, S., Degani, I., Avila-Wallace, M., Pivovarov, M., Randall, T., Weissleder, R., Lee, H., Im, H., Castro, C.M. (2019). Point-of-care cervical cancer screening using deep learning-based microholography. Theranostics, 9(26), 8438-8447. https://doi.org/10.7150/thno.37187.

ACS
Pathania, D.; Landeros, C.; Rohrer, L.; D'Agostino, V.; Hong, S.; Degani, I.; Avila-Wallace, M.; Pivovarov, M.; Randall, T.; Weissleder, R.; Lee, H.; Im, H.; Castro, C.M. Point-of-care cervical cancer screening using deep learning-based microholography. Theranostics 2019, 9 (26), 8438-8447. DOI: 10.7150/thno.37187.

NLM
Pathania D, Landeros C, Rohrer L, D'Agostino V, Hong S, Degani I, Avila-Wallace M, Pivovarov M, Randall T, Weissleder R, Lee H, Im H, Castro CM. Point-of-care cervical cancer screening using deep learning-based microholography. Theranostics 2019; 9(26):8438-8447. doi:10.7150/thno.37187. https://www.thno.org/v09p8438.htm

CSE
Pathania D, Landeros C, Rohrer L, D'Agostino V, Hong S, Degani I, Avila-Wallace M, Pivovarov M, Randall T, Weissleder R, Lee H, Im H, Castro CM. 2019. Point-of-care cervical cancer screening using deep learning-based microholography. Theranostics. 9(26):8438-8447.

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