RGB Three-Channel SWE-Based Ultrasomics Model: Improving the Efficiency in Differentiating Focal Liver Lesions

Abstract

ObjectiveTo explore a new method for color image analysis of ultrasomics and investigate the efficiency in differentiating focal liver lesions (FLLs) by Red, Green, and Blue (RGB) three-channel SWE-based ultrasomics model.MethodsOne hundred thirty FLLs were randomly divided into training set (n = 65) and validation set (n = 65). The RGB three-channel and direct conversion methods were applied to the same color SWE images. Ultrasomics features were extracted from the preprocessing images establishing two feature data sets. The least absolute shrinkage and selection operator (LASSO) logistic regression model was applied for feature selection and model construction. Two models, named RGB model (based on RGB three-channel conversion) and direct model (based on direct conversion), were used to differentiate FLLs. The diagnosis performance of the two models was evaluated by area under the curve (AUC), calibration curves, decision curves, and net reclassification index (NRI).ResultsIn the validation cohort, the AUC of the direct model and RGB model in characterization on FLLs were 0.813 and 0.926, respectively (p = 0.038). Calibration curves and decision curves indicated that the RGB model had better calibration efficiency and provided greater clinical benefits. NRI revealed that the RGB model correctly reclassified 7% of malignant cases and 25% of benign cases compared to the direct model (p = 0.01).ConclusionThe RGB model generated by RGB three-channel method yielded better diagnostic efficiency than the direct model established by direct conversion method. The RGB three-channel method may be promising on ultrasomics analysis of color images in clinical application.

Quantitative Assessment of Breast-Tumor Stiffness Using Shear-Wave Elastography Histograms

“…The approach of crossing the borders of qualitative color maps and extracting RGB histograms from the SWE data appear in previous literature, such as by Cheng et al [ 36 ]. The authors used liver examinations to extract RGB histograms from color maps and merge them to organ “ultrasomics.” Remarkable achievements in breast-tumor classification were published by Zhang et al, Zhou et al, and Jiang et al [ 37 , 38 , 39 ].…”

An Interpretable Radiomics Model Based on Two-Dimensional Shear Wave Elastography for Predicting Symptomatic Post-Hepatectomy Liver Failure in Patients with Hepatocellular Carcinoma

“…After pixel-by-pixel reconstruction, the color elasticity image in the RGB space (Figure 3b) was transformed into a gray image (Figure 3c) whose values varied from 0 to maximum measurement. The hue-match method was compared with other reported methods of RGB-to-gray SWE image conversion, including distance match [18], RGB three-channel methods [19], and direct conversion from RGB to gray via a formula [19]. The hue-match method was chosen because of its superior performance when compared with the other methods in terms of the AUC.…”

“…and direct conversion from RGB to gray via a formula [19]. The hue-match method was chosen because of its superior performance when compared with the other methods in terms of the AUC.…”

“…Radiomics is the high-throughput extraction of quantitative features from medical imaging, converting these into minable data, which can then be analyzed for use in decision support systems [16,17]. Radiomics has shown great potential for the quantitative analysis of SWE images [15,18,19]. Several studies have shown that radiomics models of 2D-SWE images showed a good performance in the classification of liver fibrosis [15,20].…”