Chronic liver inflammation progressively evokes fibrosis and cirrhosis resulting in compromised liver function, and often leading to cancer. Early diagnosis and staging of fibrosis is crucial because the five-year survival rate of early-stage liver cancer is high. This study investigates the progression of hepatic fibrosis induced in rats following ingestion of diethylnitrosamine (DEN). Changes in oxygen saturation and hemoglobin concentration resulting from chronic inflammation were assayed longitudinally during DEN ingestion by photoacoustic imaging (PAI). Accompanying liver tissue changes were monitored simultaneously by B-mode sonographic imaging. Oxygen saturation and hemoglobin levels in the liver increased over 5 weeks and peaked at 10 weeks before decreasing at 13 weeks of DEN ingestion. The oxygenation changes were accompanied by an increase in hepatic echogenicity and coarseness in the ultrasound image. Histology at 13 weeks confirmed the development of severe fibrosis and cirrhosis. The observed increase in PA signal representing enhanced blood oxygenation is likely an inflammatory physiological response to the dietary DEN insult that increases blood flow by the development of neovasculature to supply oxygen to a fibrotic liver during the progression of hepatic fibrosis. Assessment of oxygenation by PAI may play an important role in the future assessment of hepatic fibrosis.

Hepatocellular carcinoma (HCC) is a highly vascular solid tumor. We have previously shown that ultrasound (US) therapy significantly reduces tumor vascularity. This study monitors US-induced changes in tumor oxygenation on murine HCC by photoacoustic imaging (PAI). Oxygen saturation and total hemoglobin were assessed by PAI before and after US treatments performed at different intensities of continuous wave (CW) bursts and pulsed wave (PW) bursts US. PAI revealed significant reduction both in HCC oxygen saturation and in total hemoglobin, proportional to the US intensity. Both CW bursts US (1.6 W/cm²) and the PW bursts US (0.8 W/cm²) significantly reduced HCC oxygen saturation and total hemoglobin which continued to diminish with time following the US treatment. The effects of US therapy were confirmed by power Doppler and histological examination of the hemorrhage in tumors. By each measure, the changes observed in US-treated HCC were more prevalent than those in sham-treated tumors and were statistically significant. In conclusion, the results show that US is an effective vascular-targeting therapy for HCC. The changes in oxygenation induced by the US treatment can be noninvasively monitored longitudinally by PAI without the use of exogenous image-enhancing agents. The combined use of PAI and the therapeutic US has potential for image-guided vascular therapy for HCC.

Background and Objective: Hepatocellular carcinoma (HCC) is the most common primary liver malignancy, and its current management relies heavily on locoregional therapy for curative therapy, bridge to transplant, and palliative therapy. Locoregional therapies include ablation and hepatic artery directed therapies such as embolization and radioembolization. In this study we evaluate the feasibility of using novel antivascular ultrasound (AVUS) as a noninvasive locoregional therapy to reduce perfusion in HCC lesions in a rat model and monitor the effect with contrast-enhanced ultrasound imaging.

Objective: The study evaluates quantitative ultrasound (QUS) texture features with machine learning (ML) to enhance the sensitivity of B-mode ultrasound (US) for the detection of fibrosis at an early stage and distinguish it from advanced fibrosis. Different ML methods were evaluated to determine the best diagnostic model. Methods: 233 B-mode images of liver lobes with early and advanced-stage fibrosis induced in a rat model were analyzed. Sixteen features describing liver texture were measured from regions of interest (ROIs) drawn on B-mode images. The texture features included a first-order statistics run length (RL) and gray-level co-occurrence matrix (GLCM). The features discriminating between early and advanced fibrosis were used to build diagnostic models with logistic regression (LR), naïve Bayes (nB), and multi-class perceptron (MLP). The diagnostic performances of the models were compared by ROC analysis using different train-test sampling approaches, including leave-one-out, 10-fold cross-validation, and varying percentage splits. METAVIR scoring was used for histological fibrosis staging of the liver. Results: 15 features showed a significant difference between the advanced and early liver fibrosis groups, p < 0.05. Among the individual features, first-order statics features led to the best classification with a sensitivity of 82.1–90.5% and a specificity of 87.1–89.8%. For the features combined, the diagnostic performances of nB and MLP were high, with the area under the ROC curve (AUC) approaching 0.95–0.96. LR also yielded high diagnostic performance (AUC = 0.91–0.92) but was lower than nB and MLP. The diagnostic variability between test-train trials, measured by the coefficient-of-variation (CV), was higher for LR (3–5%) than nB and MLP (1–2%). Conclusion: Quantitative ultrasound with machine learning differentiated early and advanced fibrosis. Ultrasound B-mode images contain a high level of information to enable accurate diagnosis with relatively straightforward machine learning methods like naïve Bayes and logistic regression. Implementing simple ML approaches with QUS features in clinical settings could reduce the user-dependent limitation of ultrasound in detecting early-stage liver fibrosis.