A generative adversarial approach to facilitate archival-quality histopathologic diagnoses from frozen tissue sections View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2021-12-28

AUTHORS

Kianoush Falahkheirkhah, Tao Guo, Michael Hwang, Pheroze Tamboli, Christopher G. Wood, Jose A. Karam, Kanishka Sircar, Rohit Bhargava

ABSTRACT

In clinical diagnostics and research involving histopathology, formalin-fixed paraffin-embedded (FFPE) tissue is almost universally favored for its superb image quality. However, tissue processing time (>24 h) can slow decision-making. In contrast, fresh frozen (FF) processing (<1 h) can yield rapid information but diagnostic accuracy is suboptimal due to lack of clearing, morphologic deformation and more frequent artifacts. Here, we bridge this gap using artificial intelligence. We synthesize FFPE-like images (“virtual FFPE”) from FF images using a generative adversarial network (GAN) from 98 paired kidney samples derived from 40 patients. Five board-certified pathologists evaluated the results in a blinded test. Image quality of the virtual FFPE data was assessed to be high and showed a close resemblance to real FFPE images. Clinical assessments of disease on the virtual FFPE images showed a higher inter-observer agreement compared to FF images. The nearly instantaneously generated virtual FFPE images can not only reduce time to information but can facilitate more precise diagnosis from routine FF images without extraneous costs and effort. More... »

PAGES

554-559

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41374-021-00718-y

DOI

http://dx.doi.org/10.1038/s41374-021-00718-y

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1144228957

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/34963688


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