Morphological metrics such as fractal dimension (FD) have shown value as diagnostic and prognostic markers in diverse cancers. A lack of procedural consensus on fractal techniques may lead to a non-generalization of results across different studies. This study reports variations of Computed Tomography (CT) derived FD renal masses across different fractal analysis implementations. The Fraclac grayscale pixel size 512x512 pixel setting Area Under Curve (AUC) showed the highest AUC value (0.59) among all pixel settings in classifying clear cell renal cell carcinoma (ccRCC) vs. Oncocytoma and liquid poor angiomyolipoma (AML). Similarly, for the multiphase analysis, we also explored MATLAB grayscale pixel sizes from 7x7 to 256x256 pixels. Results showed that the 64x64 pixel setting had the highest AUC of 0.60-0.72 for ccRCC vs. Oncocytoma and AML and AUC of 0.58-0.69 for chromophobe renal cell carcinoma (RCC) vs Oncocytoma.
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.
Purpose: Evaluate the feasibility of spectral analysis, particularly fast fourier transform (FFT), to help clinicians differentiate clear cell renal cell carcinoma (ccRCC) tumor grades using contrast-enhanced computed tomography (CECT) images of renal masses, quantitatively, and compare their performance to the Fuhrman grading system. Materials and Methods: Regions of interest of the whole lesion were manually segmented and co-registered from multiphase CT acquisitions of 95 patients with ccRCC. Here, FFT is employed to objectively quantify the texture of a tumor surface by evaluating tissue gray-level patterns and automatically measure frequency-based texture metrics. An independent t-test or a Wilcoxon rank sum test (depending on the data distribution) was used to determine if the spectral analysis metrics would produce statistically significant differences between the tumor grades. Receiver operating characteristic (ROC) curve analysis was used to evaluate the usefulness of spectral metrics in predicting the ccRCC grade. Results: The Wilcoxon test showed that there was a significant difference in complexity index between the different tumor grades, p < 0.01 at all the four phases of CECT acquisition. In all cases a positive correlation was observed between tumor grade and complexity index. ROC analysis revealed the importance of the entropy of FFT amplitude, FFT phase and complexity index and its ability to identify grade 1 and grade 4 tumors from the rest of the population. Conclusion: Our study suggests that FFT-based spectral metrics can differentiate between ccRCC grades, and in combination with other metrics improve patient management and prognosis of renal masses.
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