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Distinguishing chromophobe renal cell carcinoma (chRCC) from oncocytoma on hematoxylin and eosin images may be difficult and require time-consuming ancillary procedures. Multiphoton microscopy (MPM), an optical imaging modality, was used to rapidly generate sub-cellular histological resolution images from formalin-fixed unstained tissue sections from chRCC and oncocytoma. Tissues were excited using 780nm wavelength and emission signals (including second harmonic generation and autofluorescence) were collected in different channels between 390 nm and 650 nm. Granular structure in the cell cytoplasm was observed in both chRCC and oncocytoma. Quantitative morphometric analysis was conducted to distinguish chRCC and oncocytoma. To perform the analysis, cytoplasm and granules in tumor cells were segmented from the images. Their area and fluorescence intensity were found in different channels. Multiple features were measured to quantify the morphological and fluorescence properties. Linear support vector machine (SVM) was used for classification. Re-substitution validation, cross validation and receiver operating characteristic (ROC) curve were implemented to evaluate the efficacy of the SVM classifier. A wrapper feature algorithm was used to select the optimal features which provided the best predictive performance in separating the two tissue types (classes). Statistical measures such as sensitivity, specificity, accuracy and area under curve (AUC) of ROC were calculated to evaluate the efficacy of the classification. Over 80% accuracy was achieved as the predictive performance. This method, if validated on a larger and more diverse sample set, may serve as an automated rapid diagnostic tool to differentiate between chRCC and oncocytoma. An advantage of such automated methods are that they are free from investigator bias and variability. Copyright © 2016 SPIE.

Aims: Atherosclerotic plaques vulnerable to rupture are almost always inflamed, and carry a large lipid core covered by a thin fibrous cap. The other components may include neovascularisation, intraplaque haemorrhage and spotty calcification. In contrast, stable plaques are characterised by a predominance of smooth muscle cells and collagen, and lipid core is usually deep seated or absent. This study is a proof of principle experiment to evaluate the feasibility of multiphoton microscopy (MPM) to identify aforementioned plaque components. Methods and Results: MPM is a nonlinear optical technique that allows imaging based on intrinsic tissue signals including autofluorescence and higher-order scattering. In our study, MPM imaging was performed on morphologically diverse aortic and coronary artery plaques obtained during autopsy. Various histologically verified plaque components including macrophages, cholesterol crystals, haemorrhage, collagen and calcification were recognised by MPM. Conclusions: Recognition of the distinct signatures of various plaque components suggests that MPM has the potential to offer next-generation characterisation of atherosclerotic plaques. The higher lateral resolution (comparable to histology) images generated by MPM for identifying plaque components might complement larger field of view and greater imaging depth currently available with optical coherence tomography imaging. As the next step MPM would need to be evaluated for intact vessel imaging ex vivo and in vivo. © 2017 The Authors Journal of Microscopy © 2017 Royal Microscopical Society

Convolutional neural networks (CNN) are a class of machine learning model that are especially well suited for imagebased tasks. In this study, we design and train a CNN on tissue samples imaged using Multi-Photon Microscopy (MPM) and show that the model can distinguish between chromophobe renal cell carcinoma (chRCC) and oncocytoma. We demonstrate the method to train a model using simple max-pooling vote fusion, and use the model to highlight regions of the input that cause a positive classification. The model can be tuned for higher sensitivity at the cost of specificity with a constant threshold and little impact to accuracy overall. Several numerical experiments were run to measure the model’s accuracy on both image and patient level analysis. Our models were designed with a dropout parameter that biases the model towards higher sensitivity or specificity. Our best performance model, as measured by area under the receiver operating characteristic curve (AUC of ROC, or AUROC) on patient level classification, is measured with a 94% AUROC and 88% accuracy, along with 100% sensitivity and 75% specificity.

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.

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.