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Proceedings Paper

A new method using multiphoton imaging and morphometric analysis for differentiating chromophobe renal cell carcinoma and oncocytoma kidney tumors
Author(s): Binlin Wu; Sushmita Mukherjee; Manu Jain
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Paper Abstract

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.

Paper Details

Date Published: 27 April 2016
PDF: 8 pages
Proc. SPIE 9712, Multiphoton Microscopy in the Biomedical Sciences XVI, 97121O (27 April 2016); doi: 10.1117/12.2213681
Show Author Affiliations
Binlin Wu, Southern Connecticut State Univ. (United States)
Weill Cornell Medical College (United States)
Sushmita Mukherjee, Weill Cornell Medical College (United States)
Manu Jain, Weill Cornell Medical College (United States)

Published in SPIE Proceedings Vol. 9712:
Multiphoton Microscopy in the Biomedical Sciences XVI
Ammasi Periasamy; Peter T. C. So; Karsten König, Editor(s)

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