Hepatic tumors represent a major health care problem in the U.S. and worldwide. In 2020, there is an estimated 42,810 new cases of primary liver cancer in U.S. with over 30,160 related deaths (worldwide, this is 20 fold greater). In addition, primary liver cancer has tripled in the U.S. since 1980 and has risen 3% each year from 2006-2015. Much of this rise has been attributed to obesity, diabetes, and fatty liver disease (FLD), which are rapidly replacing viral- and alcohol-related liver disease as a major factor of hepatocellular carcinoma (HCC). Due to limited surgical candidacy, and lack of transplant availability, locoregional therapies for the management of disease and as a bridge-to-transplant are a highly engaged area of investigation. One locoregional therapy in particular, thermal ablation, has a long history in local liver cancer control in both percutaneous procedures and within surgery. Among thermal therapies for liver, microwave ablation (MWA) is rapidly becoming a favored treatment. MWA has the advantage of creating a large spatial extent of power deposition, can penetrate through charred tissues, and has the capacity to ablate up to and around large vessels. However, common to locoregional therapies, ablation volumes can be difficult to predict for any given patient, i.e. commercial guidance information is quite generic, and recurrence and complications are still problems. The basic scientific premise underpinning this application is that improving microwave ablation (MWA) therapy is intrinsically dependent on the precise localization and determination of dose extent in relation to spatially-encoded disease and anatomic information. The overall hypothesis that the candidate will pursue is that quantitative magnetic resonance (MR) imaging can be used to create both anatomically and materially subject-specific computational models such that accurate microwave probe ablation thermal dosing can be forecast to improve ablation outcomes. The primary aim will be to create a multi-physics modeling capability to appropriately plan and forecast a patient-specific thermal ablative dose. In this aim, we propose three tasks to be accomplished: (1) develop and achieve software goals for microwave ablation modeling and planning, (2) using fat quantification data from the literature to estimate the influence that FLD could have on dose prediction, and (3) begin a feasibility study to demonstrate improved thermal dose prediction as driven by pre-procedural imaging (fat quantification, and perfusion imaging). If fully realized, this work could profoundly shape the planning, delivery, and control of microwave ablative therapy. The image-data-driven approach would allow for pre-procedural patient-specific ablation plans, intra-procedurally enable enhanced localization, and the prediction/adjustment of MWA thermal dose extent.
In 2020 in the US, 42,810 cases of primary liver cancer (30,160 deaths) are estimated and incidences have tripled since 1980. Microwave ablation is a minimally invasive thermal therapy that improves survival times. This work is focused at creating a dose forecasting system to improve planning and delivery of microwave ablation.
