Histology, the traditional method of examining surgical tissue under a microscope, is a time-consuming process involving the fixation of tissue in formalin, dehydration, embedding in paraffin, and cutting into thin sections for hematoxylin-eosin (H&E) staining. Frozen section analysis is a faster alternative used in surgery to quickly evaluate tissue, but it has limitations, such as the size of the specimens that can be analyzed and difficulties with fatty and bony tissues.


To rapidly examine nonprocessed kidney tumors using nonlinear microscopy (NLM), a fluorescence microscopy technique that can rapidly visualize fresh or fixed, rapidly stained, nonprocessed tissue resembling H&E histology. This technology eliminates the need for fixation, embedding, microtome sectioning, or slide preparation.


In this study, a total of 190 tissue specimens were collected from 46 patients who underwent partial or radical nephrectomy.


Two genitourinary pathologists confirmed that diagnostically important features present in the H&E images could also be identified in the NLM images.


The results of this study demonstrated that NLM had a high degree of correspondence with H&E staining for the classical variants of renal cell carcinoma. NLM offers several clinical benefits, such as facilitating rapid renal cell carcinoma diagnosis, assessment of targeted kidney biopsies for both tumor and medical kidney diseases, and collection of fresh renal cell carcinoma tissue for molecular studies.

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Author notes

Guzmán-Arocho and Weber contributed equally to this work.

Two supplemental digital content files are available for this article. See text for hyperlinks.

This work was supported by the National Cancer Institute of the National Institutes of Health under award number R01CA249151.

The authors have no relevant financial interest in the products or companies described in this article.

The study was presented at the United States and Canadian Academy of Pathology annual meeting in New Orleans, Louisiana, on March 15, 2023.

Supplementary data