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Proceedings Paper

Differentiating clear cell renal cell carcinoma from oncocytoma using curvelet transform analysis of multiphase CT: preliminary study
Author(s): Chinmay Jog; Bino A. Varghese; Darryl H. Hwang; Steven Y. Cen; Manju Aron; Mihir Desai; Vinay A. Duddalwar
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Paper Abstract

Clinical imaging techniques have low accuracy in differentiating malignant tumors such as clear cell Renal Cell Carcinoma (ccRCC) and benign tumors such as oncocytoma. Texture metrics i.e., metrics assessing the variations in grey-levels of intensity making up a region of interest extracted from routine clinical images have shown promising results in achieving this objective. To explore the relationship between tumor behavior and texture metrics from images, we test the effectiveness of 2D Curvelet Transform-based texture analysis in differentiating between ccRCC and Oncocytoma using contrast-enhanced computed tomography (CECT) images. Whole lesions were manually segmented on the nephrographic phase using Synapse 3D (Fujifilm, CT) and co-registered to other phases of multiphase CT acquisitions for each tumor. A first-generation curvelet transform code was used to apply forward, inverse transform to segmented images, and texture metrics were extracted from each CT phase. Histopathological diagnosis was obtained following surgical resection. A Wilcoxon rank-sum test showed that curvelet-based metric: energy on corticomedullary phase was significantly (p <0.005) higher in oncocytoma (0.06±0.04) than ccRCC (0.04±0.05). Higher values of energy are associated with homogenous textures. A supportive receiver operator characteristics analysis based on energy metric revealed reasonable discrimination (AUC>0.7, p <0.05) between ccRCC and oncocytoma. We conclude based on these preliminary results that curvelet- based energy metric can differentiate between ccRCC and oncocytoma based on their CECT data. In combination with other metrics, curvelet metrics may advance radiomic analysis in evaluating clinical imaging data.

Paper Details

Date Published: 3 January 2020
PDF: 9 pages
Proc. SPIE 11330, 15th International Symposium on Medical Information Processing and Analysis, 1133009 (3 January 2020); doi: 10.1117/12.2540169
Show Author Affiliations
Chinmay Jog, The Univ. of Southern California (United States)
Bino A. Varghese, The Univ. of Southern California (United States)
Darryl H. Hwang, The Univ. of Southern California (United States)
Steven Y. Cen, The Univ. of Southern California (United States)
Manju Aron, The Univ. of Southern California (United States)
Mihir Desai, The Univ. of Southern California (United States)
Vinay A. Duddalwar, The Univ. of Southern California (United States)


Published in SPIE Proceedings Vol. 11330:
15th International Symposium on Medical Information Processing and Analysis
Eduardo Romero; Natasha Lepore; Jorge Brieva, Editor(s)

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