Fat deposition in the human body can be imaged using MR. MR fat imaging is used in studies ranging from examining body habitus to evaluating organ composition. A detailed method of segmenting and quantifying fat by post-processing MR image volumes using Synapse 3D and custom Matlab softwares is presented.
Clinicians can now objectively quantify tumor necrosis by Hounsfield units and enhancement characteristics from multiphase contrast enhanced CT imaging. NecroQuant has been designed to work as part of a radiomics pipelines. The software is a departure from the conventional qualitative assessment of tumor necrosis, as it provides the user (radiologists and researchers) a simple interface to precisely and interactively define and measure necrosis in contrast-enhanced CT images. Although, the software is tested here on renal masses, it can be re-configured to assess tumor necrosis across variety of tumors from different body sites, providing a generalized, open, portable, and extensible quantitative analysis platform that is widely applicable across cancer types to quantify tumor necrosis.
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.
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