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Proceedings Paper

Wavelets analysis for differentiating solid, non-macroscopic fat containing, enhancing renal masses: a pilot study
Author(s): Bino Varghese; Darryl Hwang; Passant Mohamed; Steven Cen; Christopher Deng; Michael Chang; Vinay Duddalwar
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Paper Abstract

Purpose: To evaluate potential use of wavelets analysis in discriminating benign and malignant renal masses (RM) Materials and Methods: Regions of interest of the whole lesion were manually segmented and co-registered from multiphase CT acquisitions of 144 patients (98 malignant RM: renal cell carcinoma (RCC) and 46 benign RM: oncocytoma, lipid-poor angiomyolipoma). Here, the Haar wavelet was used to analyze the grayscale images of the largest segmented tumor in the axial direction. Six metrics (energy, entropy, homogeneity, contrast, standard deviation (SD) and variance) derived from 3-levels of image decomposition in 3 directions (horizontal, vertical and diagonal) respectively, were used to quantify tumor texture. Independent t-test or Wilcoxon rank sum test depending on data normality were used as exploratory univariate analysis. Stepwise logistic regression and receiver operator characteristics (ROC) curve analysis were used to select predictors and assess prediction accuracy, respectively. Results: Consistently, 5 out of 6 wavelet-based texture measures (except homogeneity) were higher for malignant tumors compared to benign, when accounting for individual texture direction. Homogeneity was consistently lower in malignant than benign tumors irrespective of direction. SD and variance measured in the diagonal direction on the corticomedullary phase showed significant (p<0.05) difference between benign versus malignant tumors. The multivariate model with variance (3 directions) and SD (vertical direction) extracted from the excretory and pre-contrast phase, respectively showed an area under the ROC curve (AUC) of 0.78 (p < 0.05) in discriminating malignant from benign. Conclusion: Wavelet analysis is a valuable texture evaluation tool to add to a radiomics platforms geared at reliably characterizing and stratifying renal masses.

Paper Details

Date Published: 17 November 2017
PDF: 8 pages
Proc. SPIE 10572, 13th International Conference on Medical Information Processing and Analysis, 105720T (17 November 2017); doi: 10.1117/12.2285948
Show Author Affiliations
Bino Varghese, Univ. of Southern California (United States)
Darryl Hwang, Univ. of Southern California (United States)
Passant Mohamed, Univ. of Southern California (United States)
Steven Cen, Univ. of Southern California (United States)
Christopher Deng, Univ. of Southern California (United States)
Michael Chang, Univ. of Southern California (United States)
Vinay Duddalwar, Univ. of Southern California (United States)

Published in SPIE Proceedings Vol. 10572:
13th International Conference on Medical Information Processing and Analysis
Eduardo Romero; Natasha Lepore; Jorge Brieva; Juan David García, Editor(s)

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