In this prospective study, forty patients with solid renal masses who underwent contrast-enhanced ultrasound (CEUS) examinations were selected. Using the ImageJ software, renal masses and adjacent normal tissue were manually segmented from CEUS cine exams obtained using the built-in RS85 Samsung scanner software. For the radiomics analysis, one frame representing precontrast, early, peak, and delay enhancement phase were selected post segmentation from each CEUS clip. From each region of interest (ROI) within a tumor tissue normalized renal mass, 112 radiomic metrics were extracted using custom Matlab® code. For the time-intensity curve (TIC) analysis, the segmented ROIs were plotted as a function of time, and the data were fit to a washout curve. From these time-signal intensity curves, perfusion quantitative parameters, were generated. Wilcoxon rank sum test or univariate independent t-test depending on data normality were used for descriptive analyses. Agreement was analyzed using Kappa statistic. Of the 40 solid masses, 31 (77.5%) were malignant, 9 (22.5%) were benign based on histopathology. Excellent agreement was found between histopathological confirmation and visual assessment based on CEUS in discriminating solid renal masses into benign vs. malignant categories (κ=0.89 95% confidence interval (CI): (0.77,1)). The total agreement between the two was 92.5%. The sensitivity and specificity of CEUS-based visual assessment was found to be 100% and 66.7%, respectively. Quantitative analysis revealed TIC metrics revealed statistically significant differences between the malignant and benign groups and between clear cell renal cell carcinoma (ccRCC) and papillary renal cell carcinoma (pRCC) subtypes. The study shows excellent agreement between visual assessment and histopathology, but with the room to improve in specificity.
In this Institutional Review Board (IRB) approved, Health Insurance Portability and Accountability Act (HIPAA) compliant, prospective study, uncompressed envelope data (RF data) were collected from 100 patients with focal renal masses using an RS80A ultrasound scanner with B-mode and CEUS. By summing and averaging the Nakagami images formed using sliding windows, we use the average ‘m’ to stratify manually segmented masses, using data from both the B-mode and CEUS scans. Wilcoxon rank sum test using an alpha value of 0.05 was used detect differences between the groups. Logistic regression was used for classification and the area under the receiver operator curve (AUC) was used to assess performance. Among the 100 masses, 40 were benign, 37 were malignant based on histopathology, and 23 were radiologically and clinically presumed malignant but with no pathological proof at the time of data analysis. Univariate analyses showed significant (p<0.01) differences between the benign and non-benign masses on both B-mode and CEUS, with non-benign masses having smaller ‘m’. Predictive models constructed using Nakagami parameters extracted from Bmode and CEUS-based RF scans showed an AUC of 0.67 95% CI: (0.56, 0.78) and 0.61 95% CI: (0.5, 0.73), respectively for discriminating benign from non-benign renal masses. The concordance between the two assessments was 95%. We present a framework for characterizing images using speckle textural properties, for example Nakagami analysis, to aid in objective tissue characterization using ultrasound.
Purpose: Evaluate the feasibility of using a Nakagami model to create an accurate parametric image from ultrasound imaging data for the differentiation of homogenous and heterogeneous texture phantoms. Analysis was done on the raw data i.e., radiofrequency (RF) data collected before any post processing that can affect the images. Materials and methods: The Nakagami parametric image was constructed on demodulated RF data with the sliding window technique to create a map of local parameters. The Nakagami parameter (m) for the entire image was found by averaging all values. By design, when m is greater than 1, the distribution is post-Rayleigh. When m is equal to 1, the distribution is Rayleigh. To test the technique, two agar phantoms were constructed, using varying amounts of flour as the scatterer. The higher amount of flour scatterer was meant to mimic heterogeneous texture and the lesser amount meant to mimic homogeneous texture. Scans were done on each phantom and analyzed for differences in the Nakagami parameter. Results: Phantom 1 displayed a post-Rayleigh distribution (m = 36.1±7.0), while phantom 2 did so, to a lesser extent (m = 1.64±0.12). As the distribution transitions from Rayleigh to post Rayleigh, the scatterers in the sample go from being periodically located/randomly distributed to large numbers of randomly distributed scatterers. Conclusion: Our study suggests that Nakagami parametric based metrics may be used to increase robustness of texture analysis, considering the analysis is done on the raw data before any post processing that can affect the images is introduced.
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