Voxel-based whole-lesion enhancement parameters: a study of its clinical value in differentiating clear cell renal cell carcinoma from renal oncocytoma

Abstract

Our study suggests that voxel-based WL enhancement parameters provide only a slight improvement over single ROI-based enhancement techniques in differentiating between ccRCC and renal oncocytoma.

Differentiating solid, non-macroscopic fat containing, enhancing renal masses using fast Fourier transform analysis of multiphase CT

“…Similar outcomes of predicting tumor heterogeneity have been reported in literature where increased lesion heterogeneity using CT and MRI texture analysis has been associated to malignant as compared to benign masses. 8,28,29 The increased heterogeneity may be attributed to the altered recruitment of cells to counter genomic instability. Also, the best separation between malignant and benign renal masses was observed in the corticomedullary phase.…”

“…2 Apart from lipid-rich angiomyolipomas (lp-AMLs) and simple or minimally complex cysts, it is difficult to differentiate a localized renal lesion as benign or malignant or characterize its subtype based on imaging. 3 Quantitative metrics derived from cross-sectional imaging, including pixel mapping, 4 contrast enhancement pattern analysis, 5 chemical shift MRI, 6 and histogram analysis, 7,8 have been studied as potential markers to differentiate renal lesions. However, to date, none of these metrics been proven to be a consistent or reliable discriminator as benign and malignant lesions demonstrate significant overlap in quantitative imaging characteristics.…”

Radiomics and Bladder Cancer: Current Status

“…This is the second step of the radiomics workflow, and the end-product of this step is the isolation of a region of interest (ROI), which can be either a volume (if 3D) or an area (if 2D). While it is intuitive to expect 3D radiomics to outperform 2D radiomics, this is not always the case [32]. As in the case of image acquisition, there are no established guidelines or consensus across centers with regards to image segmentation.…”

A Decision-Support Tool for Renal Mass Classification

“…We derived features using various techniques ranging from simple histogram analysis to more advanced techniques such as gray-level co-occurrence matrix (GLCM) and gray-level difference matrix (GLDM) [8,9,25,26]. The techniques (summarized in Table 3) have been described in greater detail in previous studies [27][28][29][30][31][32].…”