Differentiating clear cell renal cell carcinoma from oncocytoma using curvelet transform analysis of multiphase CT: preliminary study

Chinmay Jog; Bino A. Varghese; Darryl H. Hwang; Steven Y. Cen; Manju Aron; Mihir Desai; Vinay A. Duddalwar

doi:10.1117/12.2540169

3 January 2020 Differentiating clear cell renal cell carcinoma from oncocytoma using curvelet transform analysis of multiphase CT: preliminary study

Chinmay Jog, Bino A. Varghese, Darryl H. Hwang, Steven Y. Cen, Manju Aron, Mihir Desai, Vinay A. Duddalwar

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Proceedings Volume 11330, 15th International Symposium on Medical Information Processing and Analysis; 1133009 (2020) https://doi.org/10.1117/12.2540169
Event: 15th International Symposium on Medical Information Processing and Analysis, 2019, Medelin, Colombia

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.

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Chinmay Jog, Bino A. Varghese, Darryl H. Hwang, Steven Y. Cen, Manju Aron, Mihir Desai, and Vinay A. Duddalwar "Differentiating clear cell renal cell carcinoma from oncocytoma using curvelet transform analysis of multiphase CT: preliminary study", Proc. SPIE 11330, 15th International Symposium on Medical Information Processing and Analysis, 1133009 (3 January 2020); https://doi.org/10.1117/12.2540169

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KEYWORDS

Tumors

Computed tomography

Image segmentation

Cancer

3D image processing

Biopsy

Image filtering

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