Imaging biomarker development based on microbubble perfusion and oxygen saturation in a rat model of liver cancer

Treatment of hepatocellular carcinoma (HCC) with sorafenib, a multikinase inhibitor, results in decreased microvessel density associated with increased levels of tumor hypoxia. However, the response rate is relatively poor, and recently it has been shown that tumor hypoxia and perfusion have predictive correlations with HCC response to sorafenib. In this study, we have investigated the correlation of oxygen saturation (SO2) and perfusion, estimated using photoacoustic-ultrasonic (PAUS) imaging, to the sorafenib treatment response in an orthotopic rat model of HCC. Following spectroscopic photoacoustic (sPA) imaging, microbubble contrast was introduced and harmonic imaging data were acquired for perfusion measurements. An FEM-based fluence correction model based on the diffusion approximation with empirically estimated tissue surface fluence and an SNR-based thresholding approach have been developed and validated on ex vivo and in vivo rat data to estimate SO2 using sPA imaging. The SO2 estimate has been obtained by solving an iterative minimization problem and then thresholded based on a pixel-wise empirically estimated SNR mask. For the treated cohort, the results show that the change in SO2 during an oxygen challenge is positively correlated with disease progression, while it is negatively correlated for the untreated cohort. Additionally, perfusion was significantly decreased in the treated group compared to baseline pretreatment and untreated cohort measurements. The reduced treatment-mediated perfusion leads to lack of oxygen supply and thus reduced oxygen levels. This study shows the potential of PAUS estimation of SO2 and perfusion to monitor and predict HCC sorafenib treatment response, ultimately leading to improved future treatment.