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Fast Fourier Transform Based Analysis of Renal Masses on Contrast-Enhanced Computed Tomography Images for Grading of Tumor

机译:基于快速傅里叶变换对肿瘤分级对比增强计算断层摄影图像的肾肿块分析

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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.
机译:目的:评估光谱分析,特别是快速傅里叶变换(FFT)的可行性,帮助临床医生使用对比增强的计算断层扫描(CECT)图像分化透明细胞肾细胞癌(CCRCC)肿瘤等级,定量,并比较他们的对Fuhrman分级系统的表现。材料和方法:手动细分整个病变的感兴趣区域,并从CCRCC的95例患者的多相CT采集中共同登记。这里,使用FFT通过评估组织灰度模式并自动测量基于频率的纹理度量来客观地量化肿瘤表面的纹理。使用独立的T检验或Wilcoxon等级和测试(取决于数据分布)来确定光谱分析度量是否会产生肿瘤等级之间的统计上显着的差异。接收器操作特征(ROC)曲线分析用于评估光谱度量在预测CCRCC等级中的有用性。结果:Wilcoxon试验表明,不同肿瘤等级之间的复杂性指数差异显着,P <0.01在CECT采集的所有四个阶段。在所有情况下,肿瘤级和复杂性指数之间观察到阳性相关性。 ROC分析揭示了FFT幅度,FFT相和复杂性指数熵的重要性及其识别来自其​​余人口的1级和4级肿瘤的能力。结论:我们的研究表明,基于FFT的光谱度量可以区分CCRCC等级,以及其他度量组合改善肾群的患者管理和预后。

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