Liver cancer, including hepatocellular carcinoma (HCC) as well as metastatic tumors, is a major public health problem. For patients with unresectable liver cancer, thermal ablation, including radiofrequency ablation (RFA) and microwave ablation (MWA), is the current clinical standard of care; however, thermal ablation methods are limited by non-uniform and inconsistent treatment, leading to adverse side effects, local cancer recurrence, and decreased survival, severely limiting their clinical utility. Echo decorrelation imaging is a novel ultrasound approach that mitigates these problems by mapping ultrasound echo changes caused by tissue heating during thermal ablation. Research to date on echo decorrelation imaging has shown that this method reliably predicts ablation-induced cell death in vivo for rabbit and porcine liver tissue as well as VX2 liver cancer. Although echo decorrelation imaging is an extremely promising approach to thermal ablation monitoring and control, its translation to clinical practice will require validation of real-time, 3D ablation monitoring and control for human liver cancer ablation, including different tumor types (HCC and metastatic), as well as normal and abnormal human liver parenchyma (e.g., cirrhotic liver). Our central hypothesis for this study is that real-time monitoring and control by 3D echo decorrelation imaging will improve reliability of human liver tumor ablation. To test this hypothesis, we will implement 3D, real-time echo decorrelation imaging using a clinical ultrasound scanner and matrix array transducers, then validate the application of 3D echo decorrelation imaging to RFA and MWA. Methods for controlled ablation will be implemented for RFA using standard clinical electrodes and a clinical radiofrequency generator, with echo decorrelation serving as a treatment end point. To test the performance of clinical echo decorrelation imaging in human primary and metastatic cancer as well as diseased liver tissue, 3D echo decorrelation images will be formed from echo data recorded during open surgical RFA and percutaneous MWA procedures, then compared with follow-up contrast MRI and CT to assess prediction of ablated volume margins as well as local recurrence. Control of thermal ablation by 3D echo decorrelation imaging will first be studied in resected specimens of human metastatic liver cancer with normal liver tissue margins and specimens of cirrhotic human liver tissue, as well as in vivo swine liver. The study will then culminate in a treat-and-resect trial of echo decorrelation-controlled radiofrequency ablation in liver tumor patients, demonstrating the direct application of echo decorrelation imaging to improving clinical tumor ablation. This research will thus show feasibility for both effective prediction and real-time control of liver cancer ablation in human liver cancer and concomitant diseased liver tissue.

Public Health Relevance

Liver cancer is a major public health problem, accounting for the largest cancer-related mortality in the world, with only a small fraction of patients eligible for curative resection or transplantation. Minimally invasive thermal ablation methods are the best available treatment for patients with unresectable liver cancer, but have significant problems with incomplete treatment, tumor recurrence, and complications caused by collateral tissue damage. Real-time three-dimensional ablation monitoring and control by echo decorrelation imaging will significantly improve the safety and efficacy of minimally invasive thermal ablation procedures, ultimately resulting in fewer complications, reduced tumor recurrence, and improved outcomes for cancer patients, and potentially allowing minimally invasive thermal ablation to replace surgical resection as the gold standard for liver cancer treatment.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
2R01CA158439-05A1
Application #
9531604
Study Section
Biomedical Imaging Technology Study Section (BMIT)
Program Officer
Farahani, Keyvan
Project Start
2012-08-10
Project End
2023-05-31
Budget Start
2018-06-01
Budget End
2019-05-31
Support Year
5
Fiscal Year
2018
Total Cost
Indirect Cost
Name
University of Cincinnati
Department
Engineering (All Types)
Type
Biomed Engr/Col Engr/Engr Sta
DUNS #
041064767
City
Cincinnati
State
OH
Country
United States
Zip Code
45221
Fosnight, Tyler R; Hooi, Fong Ming; Keil, Ryan D et al. (2017) Echo Decorrelation Imaging of Rabbit Liver and VX2 Tumor during In Vivo Ultrasound Ablation. Ultrasound Med Biol 43:176-186
Haworth, Kevin J; Bader, Kenneth B; Rich, Kyle T et al. (2017) Quantitative Frequency-Domain Passive Cavitation Imaging. IEEE Trans Ultrason Ferroelectr Freq Control 64:177-191
Subramanian, Swetha; Schmidt, Daniel T; Rao, Marepalli B et al. (2016) Dependence of ultrasound echo decorrelation on local tissue temperature during ex vivo radiofrequency ablation. Phys Med Biol 61:2356-71
Haworth, Kevin J; Salgaonkar, Vasant A; Corregan, Nicholas M et al. (2015) Using passive cavitation images to classify high-intensity focused ultrasound lesions. Ultrasound Med Biol 41:2420-34
Rich, Kyle T; Mast, T Douglas (2015) Accuracy of a bistatic scattering substitution technique for calibration of focused receivers. J Acoust Soc Am 138:EL469-73
Hooi, Fong Ming; Nagle, Anna; Subramanian, Swetha et al. (2015) Analysis of tissue changes, measurement system effects, and motion artifacts in echo decorrelation imaging. J Acoust Soc Am 137:585-97
Subramanian, Swetha; Mast, T Douglas (2015) Optimization of tissue physical parameters for accurate temperature estimation from finite-element simulation of radiofrequency ablation. Phys Med Biol 60:N345-55
Rich, Kyle T; Mast, T Douglas (2015) Methods to calibrate the absolute receive sensitivity of single-element, focused transducers. J Acoust Soc Am 138:EL193-8
Subramanian, Swetha; Rudich, Steven M; Alqadah, Amel et al. (2014) In vivo thermal ablation monitoring using ultrasound echo decorrelation imaging. Ultrasound Med Biol 40:102-14
Hoerig, Cameron L; Serrone, Joseph C; Burgess, Mark T et al. (2014) Prediction and suppression of HIFU-induced vessel rupture using passive cavitation detection in an ex vivo model. J Ther Ultrasound 2:14

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