Evaluation of Liver Cancer With Magnetic Resonance Imaging (MRI)

Icahn School of Medicine at Mount Sinai84 enrolled

Overview

The incidence of hepatocellular carcinoma (HCC) has recently increased in the United States. Although imaging plays a major role in HCC screening and staging, the possibility of predicting HCC tumor grade, aggressiveness, angiogenesis and hypoxia with imaging are unmet needs. In addition, new antiangiogenic drugs now available to treat advanced HCC necessitate the use of new imaging criteria beyond size. The investigators would like to develop and validate non-invasive magnetic resonance imaging (MRI) methods based on advanced diffusion-weighted imaging (DWI), MR Elastography, BOLD (blood oxygen level dependent) MRI and perfusion-weighted imaging (PWI, using gadolinium contrast) to be used as non-invasive markers of major histopathologic features of HCC, and to predict and assess early response of HCC to systemic therapy. The investigators also would like to develop quality control tools to improve the quality and decrease variability of quantitative MRI metrics. These techniques combined could represent non-invasive correlates of histologic findings in HCC, could enable individualized therapy, and provide prognosis in patients with HCC.

The incidence of hepatocellular carcinoma (HCC) has recently increased in the US mostly due to an increase in chronic hepatitis C infection. Angiogenesis is critical for the growth and metastatic progression of HCC. With the development of new antiangiogenic drugs such as sorafenib, imaging methods to predict and assess therapeutic response beyond changes in size become critical. However, validated imaging methods to predict and assess early HCC response to targeted agents are lacking. In this study, the investigators would like to develop quantitative MRI methods interrogating different features of HCC tumor biology and pathology, including tumor cellularity, grade, angiogenesis and hypoxia. The investigators propose a multiparametric approach combining advanced DWI (IVIM: intravoxel incoherent motion diffusion measuring perfusion fraction and true diffusion coefficient), DCE-MRI (dynamic contrast-enhanced MRI, which measures arterial and portal flow, mean transit time, blood volume and distribution volume), and BOLD MRI using oxygen or carbogen challenge. This protocol will be performed in patients with HCC undergoing hepatic resection. Routine and advanced histopathologic methods will be performed (tumor grade, CK19 expression, presence of microvascular invasion, VEGF expression, microvessel density, HIF 1-alpha expression). MRI metrics will be correlated with histopathologic metrics. The first portion of the proposal involves the development of a QC algorithm assessing MR data quality and test-retest. The investigators will propose solutions to improve data acquisition and processing. The last 2 years of the study will be dedicated to a prospective randomized study comparing Yttrium 90 radioembolization to sorafenib, assessing the role of baseline MRI metrics and early changes (at 2 weeks) in these metrics as markers of tumor response and time to progression in patients with unresectable HCC.

Eligibility

Sex: ALLMin age: 18 YearsHealthy volunteers:

Medical Language ↔ Plain English

Inclusion Criteria: Study group * Patients diagnosed with HCC, who will undergo resection or transplantation within 6 months, as part of routine clinical care and patients diagnosed with unresectable HCC * 18 years of age and older * Patient is able to give informed consent for this study Control group * Healthy volunteers 18 years of age and older * Subject is able to give informed consent for this study Exclusion Criteria: * Age less than 18 years * Unable or unwilling to give informed consent * Contra-indications to MRI: 1. Electrical implants such as cardiac pacemakers or perfusion pumps 2. Ferromagnetic implants such as aneurysm clips, surgical clips, prostheses, artificial hearts, valves with steel parts, metal fragments, shrapnel, tattoos near the eye, or steel implants 3. Ferromagnetic objects such as jewelry or metal clips in clothing 4. Pregnant subjects 5. Pre-existing medical conditions including a likelihood of developing seizures or claustrophobic reactions

Outcomes

Primary Outcomes

SubStudy 1: Apparent Diffusion Coefficient (ADC)

Tumor diffusion (apparent diffusion coefficient) measured with diffusion-weighted imaging sequence

Time frame: Day 1

SubStudy 1: Total Tumor Perfusion (Ft)

Perfusion/flow measured with dynamic contrast-enhanced imaging using gadolinium contrast

Time frame: Day 1

SubStudy 1: Tumor Arterial Perfusion Fraction (ART)

Perfusion/flow measured with dynamic contrast-enhanced imaging using gadolinium contrast

Time frame: Day 1

SubStudy 1: Tumor Mean Transit Time (MTT)

Tumor mean transit time (MTT) of contrast agent. Perfusion/flow measured with dynamic contrast-enhanced imaging using gadolinium contrast

Time frame: Day 1

SubStudy 1: Tumor Distribution Volume (DV)

Tumor distribution volume (DV) of contrast agent. Perfusion/flow measured with dynamic contrast-enhanced imaging using gadolinium contrast

Time frame: Day 1

SubStudy 1: Oxygen Uptake

Oxygen uptake measured with T2\* and T1-weighted imaging

Time frame: Day 1

SubStudy 1: Percent Change in Oxygen Uptake

Oxygen uptake measured with T2\* and T1-weighted imaging. Oxygen uptake (% change pre and post O2 administration) calculated by Liver ΔR2\*=100 x (R2\* post O2-R2\* pre O2)/R2\* pre O2. The healthy participants breathed 100% medical O2 through a mask for 10 min., and were imaged before and after O2 administration with the MRI methods that are sensitive to oxygen uptake in tumors.

Time frame: Day 1, pre-oxygen administration and 10 min. post-oxygen administration

SubStudy 2: ADC

Tumor diffusion measured with diffusion-weighted imaging sequence. In diffusion weighted MR imaging (DWI), the signal is proportional to the Brownian motion diffusion of free water protons in tissues. Deposition of collagen in tissue (as in fibrotic disease), or cellularity in tumors act as impediments to free water diffusion. Using different mathematical models, the degree of diffusion can be quantified from the MRI signal, to provide information on diffusion restriction due to disease. From mono exponential fit of diffusion signal, one can obtain the apparent diffusion coefficient (ADC). However, this coefficient reflects free water proton diffusion, as well as transport of water protons in the capillary vessels (capillary perfusion).