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Proceedings Paper

Fast Fourier transform-based analysis of renal masses on contrast-enhanced computed tomography images for grading of tumor
Author(s): Bino A. Varghese; Darryl H. Hwang; Steven Y. Cen; Bhushan B. Desai; Felix Y. Yap; Inderbir Gill; Mihir Desai; Manju Aron; Gangning Liang; Michael Chang; Christopher Deng; Mike Kwon; Chidubem Ugweze; Frank Chen; Vinay A. Duddalwar
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Paper Abstract

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.

Paper Details

Date Published: 26 January 2017
PDF: 8 pages
Proc. SPIE 10160, 12th International Symposium on Medical Information Processing and Analysis, 101600J (26 January 2017); doi: 10.1117/12.2256871
Show Author Affiliations
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)
Bhushan B. Desai, The Univ. of Southern California (United States)
Felix Y. Yap, The Univ. of Southern California (United States)
Inderbir Gill, The Univ. of Southern California (United States)
Mihir Desai, The Univ. of Southern California (United States)
Manju Aron, The Univ. of Southern California (United States)
Gangning Liang, The Univ. of Southern California (United States)
Michael Chang, The Univ. of Southern California (United States)
Christopher Deng, The Univ. of Southern California (United States)
Mike Kwon, The Univ. of Southern California (United States)
Chidubem Ugweze, The Univ. of Southern California (United States)
Frank Chen, The Univ. of Southern California (United States)
Vinay A. Duddalwar, The Univ. of Southern California (United States)


Published in SPIE Proceedings Vol. 10160:
12th International Symposium on Medical Information Processing and Analysis
Eduardo Romero; Natasha Lepore; Jorge Brieva; Jorge Brieva; Ignacio Larrabide; , Editor(s)

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