Quantitative Ultrasound Spectroscopy for Differentiation of Hepatocellular Carcinoma from At-Risk and Normal Liver Parenchyma

Abstract

Purpose: Quantitative ultrasound approaches can capture tissue morphologic properties to augment clinical diagnostics. This study aims to clinically assess whether quantitative ultrasound spectroscopy (QUS) parameters measured in hepatocellular carcinoma (HCC) tissues can be differentiated from those measured in at-risk or healthy liver parenchyma.Experimental Design: This prospective Health Insurance Portability and Accountability Act (HIPAA)-compliant study was approved by the Institutional Review Board. Fifteen patients with HCC, 15 non-HCC patients with chronic liver disease, and 15 healthy volunteers were included (31.1% women; 68.9% men). Ultrasound radiofrequency data were acquired in each patient in both liver lobes at two focal depths (3/9 cm). Region of interests (ROIs) were drawn on HCC and liver parenchyma. The average normalized power spectrum for each ROI was extracted, and a linear regression was fit within the À6 dB bandwidth, from which the midband fit (MBF), spectral intercept (SI), and spectral slope (SS) were extracted. Differences in QUS parameters between the ROIs were tested by a mixed-effects regression.Results: There was a significant intraindividual difference in MBF, SS, and SI between HCC and adjacent liver parenchyma (P < 0.001), and a significant interindividual difference between HCC and at-risk and healthy non-HCC parenchyma (P < 0.001). In patients with HCC, cirrhosis (n ¼ 13) did not significantly change any of the three parameters (P > 0.8) in differentiating HCC from non-HCC parenchyma. MBF (P ¼ 0.12), SI (P ¼ 0.33), and SS (P ¼ 0.57) were not significantly different in non-HCC tissue among the groups.Conclusions: The QUS parameters are significantly different in HCC versus non-HCC liver parenchyma, independent of underlying cirrhosis. This could be leveraged for improved HCC detection with ultrasound in the future.

Non-Invasive monitoring of normal tissue radiation damage using quantitative ultrasound spectroscopy

“…Structural and morphological properties impact these parameters, which are related to the scatterer size (SI, SS), the acoustics scatterer concentration (MBF, SI), or difference in acoustic impedance between the scatterer and its surrounding medium (SI). Spatial parametric images of the MBF were generated by displaying the results of a sliding window analysis on a pixel-by-pixel basis within the ROI [ 17, 19, 20 ]. Statistical analysis was done with a multivariate model analysis and a one-way ANOVA as presented in Tables 1 and 2 .…”

“…A Fourier transform was applied to the RF data along each scan line within the ROI to obtain a power spectrum; spectra were averaged over all scan lines in the ROI and normalized to a reference phantom power spectrum [ 15-20 ]. Linear regression analysis was performed on each normalized power spectrum to generate a best-fit-line within a -6dB window to obtain MBF, SS, and SI parameters [ 15-20 ]. SS and SI represent the slope and the 0-MHz intercept of the best-fit-line, respectively.…”

“…Briefly, regions-of-interest (ROIs) were selected to encompass the MFP and exclude image artifacts and skin lines. A Fourier transform was applied to the RF data along each scan line within the ROI to obtain a power spectrum; spectra were averaged over all scan lines in the ROI and normalized to a reference phantom power spectrum [15][16][17][18][19][20]. Linear regression analysis was performed on each normalized power spectrum to generate a best-fit-line within a -6dB window to obtain MBF, SS, and SI parameters [15][16][17][18][19][20].…”

Technical note: Noninvasive monitoring of normal tissue radiation damage using spectral quantitative ultrasound spectroscopy

“…Linear regression analysis was performed on each normalized power spectrum to generate a best-fit-line within a −6 dB window to obtain MBF, SS, and SI parameters. [15][16][17][18][19][20]…”

Targeted Multifunctional Nanoplatform for Imaging-Guided Precision Diagnosis and Photothermal/Photodynamic Therapy of Orthotopic Hepatocellular Carcinoma

“…Ultrasound (US) imaging, X-ray computed tomography (CT) and magnetic resonance (MR) imaging are commonly used in clinical practice to image solid tumors in deep tissues. [6][7][8] However, these technologies are not sensitive enough to detect millimeter-sized SHCC lesions. Optical imaging has received broad attention for its biosafety, high sensitivity, and molecular imaging capabilities, which enables accurate diagnosis of early-stage HCC.…”