A pilot study for distinguishing chromophobe renal cell carcinoma and oncocytoma using second harmonic generation imaging and convolutional neural network analysis of collagen fibrillar structure

Nicolas Judd; Jason Smith; Manu Jain; Sushmita Mukherjee; Michael Icaza; Ryan Gallagher; Richard Szeligowski; Binlin Wu

doi:10.1117/12.2288088

21 February 2018 A pilot study for distinguishing chromophobe renal cell carcinoma and oncocytoma using second harmonic generation imaging and convolutional neural network analysis of collagen fibrillar structure

Nicolas Judd, Jason Smith, Manu Jain, Sushmita Mukherjee, Michael Icaza, Ryan Gallagher, Richard Szeligowski, Binlin Wu

Author Affiliations +

Proceedings Volume 10489, Optical Biopsy XVI: Toward Real-Time Spectroscopic Imaging and Diagnosis; 1048919 (2018) https://doi.org/10.1117/12.2288088
Event: SPIE BiOS, 2018, San Francisco, California, United States

Abstract

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.

Citation Download Citation

Nicolas Judd, Jason Smith, Manu Jain, Sushmita Mukherjee, Michael Icaza, Ryan Gallagher, Richard Szeligowski, and Binlin Wu "A pilot study for distinguishing chromophobe renal cell carcinoma and oncocytoma using second harmonic generation imaging and convolutional neural network analysis of collagen fibrillar structure", Proc. SPIE 10489, Optical Biopsy XVI: Toward Real-Time Spectroscopic Imaging and Diagnosis, 1048919 (21 February 2018); https://doi.org/10.1117/12.2288088

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