In this study, a kernel-based metric based on the Hilbert-Schmidt independence criterion (HSIC) is proposed in a computer-aided-prognosis system to monitor cancer therapy effects. In order to induce tumour cell death, sarcoma xenograft tumour-bearing mice were injected with microbubbles followed by ultrasound and X-ray radiation therapy successively as a new anti-vascular treatment. High frequency (central frequency 30 MHz) ultrasound imaging was performed before and at different times after treatment and using spectroscopy, quantitative ultrasound (QUS) parametric maps were derived from the radiofrequency (RF) signals. The intensity histogram of midband fit parametric maps was computed to represent the pre- and post-treatment images. Subsequently, the HSIC-based metric between preand post-treatment samples were computed for each animal as a measure of distance between the two distributions. The HSIC-based metrics computes the distance between two distributions in a reproducing kernel Hilbert space (RKHS), meaning that by using a kernel, the input vectors are non-linearly mapped into a different, possibly high dimensional feature space. Computing the population means in this new space, enhanced group separability (compared to, e.g., Euclidean distance in the original feature space) is ideally obtained. The pre- and post-treatment parametric maps for each animal were thus represented by a dissimilarity measure, in which a high value of this metric indicated more treatment effect on the animal. It was shown in this research that this metric has a high correlation with cell death and if it was used in supervised learning, a high accuracy classification was obtained using a k-nearest-neighbor (k-NN) classifier.