Differentiating Clear Cell Renal Cell Carcinoma from Oncocytoma using Curvelet Transform Analysis of Multiphase CT: Preliminary Study

摘要

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 ofintensity making up a region of interest extracted from routine clinical images have shown promising results in achievingthis objective. To explore the relationship between tumor behavior and texture metrics from images, we test theeffectiveness of 2D Curvelet Transform-based texture analysis in differentiating between ccRCC and Oncocytoma usingcontrast-enhanced computed tomography (CECT) images. Whole lesions were manually segmented on the nephrographicphase using Synapse 3D (Fujifilm, CT) and co-registered to other phases of multiphase CT acquisitions for each tumor. Afirst-generation curvelet transform code was used to apply forward, inverse transform to segmented images, and texturemetrics were extracted from each CT phase. Histopathological diagnosis was obtained following surgical resection. AWilcoxon 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 homogenoustextures. 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 curveletbasedenergy metric can differentiate between ccRCC and oncocytoma based on their CECT data. In combination withother metrics, curvelet metrics may advance radiomic analysis in evaluating clinical imaging data.