TY - JOUR
T1 - A generative adversarial approach to facilitate archival-quality histopathologic diagnoses from frozen tissue sections
AU - Falahkheirkhah, Kianoush
AU - Guo, Tao
AU - Hwang, Michael
AU - Tamboli, Pheroze
AU - Wood, Christopher G.
AU - Karam, Jose A.
AU - Sircar, Kanishka
AU - Bhargava, Rohit
N1 - Publisher Copyright:
© 2021, The Author(s), under exclusive licence to United States and Canadian Academy of Pathology.
PY - 2022/5
Y1 - 2022/5
N2 - 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.
AB - 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.
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U2 - 10.1038/s41374-021-00718-y
DO - 10.1038/s41374-021-00718-y
M3 - Article
C2 - 34963688
AN - SCOPUS:85122071821
SN - 0023-6837
VL - 102
SP - 554
EP - 559
JO - Laboratory Investigation
JF - Laboratory Investigation
IS - 5
ER -