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

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. The authors synthesized formalin-fixed paraffin-embedded (FFPE)-like images (virtual FFPE) from frozen (FF) section samples. Five board-certified pathologists evaluated the results in a blinded test. Clinical assessments of disease on the virtual FFPE images showed a higher inter-observer agreement compared to FF images. These findings suggest that virtual FFPE images can increase the quality and speed of histopathologic examinations without adding to cost or effort.

Automated summary

The study synthesized virtual formalin-fixed paraffin-embedded (FFPE)-like images from frozen (FF) section samples using a generative adversarial network (GAN), showing high image quality and increased inter-observer agreement in clinical assessments compared to real FF images. This approach can potentially improve the speed and accuracy of histopathologic examinations without additional costs or efforts.