Developing an Accurate and Robust Targeting Technique to Enable Transcostal Histotripsy Treatment of Liver Tumors

PROJECT SUMMARY The overall goal of this proposal is to improve histotripsy treatments of liver tumors by developing an accurate, clinically translatable workflow for targeting liver tumors transcostally. Globally, there are ~780,000 deaths from liver cancer each year and in the US, this is expected to rise by more than 150% in the next 30 years. Histotripsy is an emerging noninvasive, nonthermal and nonionizing focal tumor treatment that has recently undergone a successful Phase I human clinical trial for treatment of liver tumors. It uses focused, short duration ultrasound pulses to produce tissue cavitation at a focal point. Cavitation occurs from nanometer scale gas pockets present in tissues, which rapidly expand and collapse, causing a high stress and strain that mechanically disrupts adjacent tissues and cells. Histotripsy damage is a binary process where tissue is either destroyed or it isn’t, providing many benefits over current thermal ablation techniques. A critical limitation of histotripsy is the sole use of ultrasound to visualize and target the tumors. Targeting with diagnostic US limits treatments to a subcostal approach due acoustic blockage from the rib cage, leaving an estimated ~50% of liver tumors non-visualizable and therefore, untreatable. With the recent development of cone-beam CT-guided histotripsy, tumors can be visualized throughout the liver, including beneath ribs, but cannot be accurately targeted due to acoustic aberrations from intervening ribs potentially altering treatment size and location. In this proposal, we will develop an accurate, clinically translatable workflow for targeting liver tumors for transcostal histotripsy. This will be accomplished by first quantifying the effect intervening ribs have on targeting accuracy through ex-vivo and in- vivo experiments (porcine model) (Aim 1). Secondly, a robust machine learning model will be developed to predict targeting offsets, which will be trained and validated with ex-vivo, in-vivo, and in-silico datasets (Aim 2). Finally, the model will be incorporated and tested to deliver patient-specific treatment plans in rabbit VX2 liver tumors (Aim 3). This proposal includes radiographic image analysis of ex-vivo and in-vivo data, histopathologic assessment of in-vivo treatments (Aims 1 and 3) and development and validation of a robust machine learning algorithm to create a multi-dimensional analysis of a novel targeting method for histotripsy treatments. An accurate and robust targeting method for performing transcostal histotripsy of liver tumors is crucial to expanding the cohort of patients eligible for the therapy. It has the potential to almost double the number of patients with liver tumors eligible for the treatment and the technique is translatable to tumors in other organs (eg, kidney and pancreas) as well.