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Context.-Distinguishing chromophobe renal cell carcinoma (chRCC), especially in the presence of eosinophilic cytoplasm, from oncocytoma on hematoxylin-eosin can be difficult and often requires time-consuming ancillary procedures that ultimately may not be informative. Objective.-To explore the potential of multiphoton microscopy (MPM) as an alternative and rapid diagnostic tool in differentiating oncocytoma from chRCC at subcellular resolution without tissue processing. Design.-Unstained, deparaffinized tissue sections from 27 tumors (oncocytoma [n = 12], chRCC [n = 12], eosinophilic variant of chRCC [n = 1], and atypical oncocytic renal neoplasm [n = 2]) were imaged with MPM. Morphologic evaluation and automated quantitative morphometric analysis were conducted to distinguish between chRCC and oncocytoma. Results.-The typical cases of oncocytomas (12 of 12) and chRCC (12 of 12) could be readily differentiated on MPM based on the morphologic features similar to hematoxylin-eosin. The most striking MPM signature of both of the tumors was the presence of autofluorescent intracytoplasmic granules, which are not seen on hematoxylin-eosin-stained slides. Although we saw these granules in both types of tumors, they appeared distinct, based on their size, shape, cytoplasmic distribution, and autofluorescence wavelengths, and were valuable in arriving at a definitive diagnosis. For oncocytomas and chRCC, high diagnostic accuracies of 100% and 83.3% were achieved on blinded MPM and morphometric analysis, respectively. Conclusions.-To the best of our knowledge, this is the first demonstration of MPM to distinguish chRCC from oncocytoma in fixed tissues. Our study was limited by small sample size and only a few variants of oncocytic tumors. Prospective studies are warranted to assess the utility of MPM as a diagnostic aid in oncocytic renal tumors. © Copyright 2018 College of American Pathologists.
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A clear distinction between oncocytoma and chromophobe renal cell carcinoma (chRCC) is critically important for clinical management of patients. But it may often be difficult to distinguish the two entities based on hematoxylin and eosin (H and E) stained sections alone. In this study, second harmonic generation (SHG) signals which are very specific to collagen were used to image collagen fibril structure. We conduct a pilot study to develop a new diagnostic method based on the analysis of collagen associated with kidney tumors using convolutional neural networks (CNNs). CNNs comprise a type of machine learning process well-suited for drawing information out of images. This study examines a CNN model's ability to differentiate between oncocytoma (benign), and chRCC (malignant) kidney tumor images acquired with second harmonic generation (SHG), which is very specific for collagen matrix. To the best of our knowledge, this is the first study that attempts to distinguish the two entities based on their collagen structure. The model developed from this study demonstrated an overall classification accuracy of 68.7% with a specificity of 66.3% and sensitivity of 74.6%. While these results reflect an ability to classify the kidney tumors better than chance, further studies will be carried out to (a) better realize the tumor classification potential of this method with a larger sample size and (b) combining SHG with two-photon excited intrinsic fluorescence signal to achieve better classification. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.
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