About this trial
This phase II trial evaluates the diagnostic performance of contrast-enhanced ultrasound (CEUS) for assessing treatment response in patients undergoing transarterial chemoembolization (TACE) for liver tumors. TACE is a hepatic artery embolization technique involving the injection of a blocking agent and a chemotherapy agent to treat liver cancers. Currently, contrast enhanced magnetic resonance imaging or computed tomography are used to assess disease response 1-2 months after TACE treatment, but ultrasound may be a less expensive, earlier alternative. CEUS is an imaging procedure that uses high-frequency sound waves to generate images of the body after administering Lumason, an imaging agent used to enhance visualization of blood flow on ultrasounds. CEUS is able to be performed during the TACE procedure, making it possible to evaluate treatment response earlier than standard techniques. CEUS may be an effective method to evaluate treatment response more accurately and much earlier than current standard evaluation methods.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
Scheduled for TACE therapy of a liver tumor
Be at least 18 years of age
Be medically stable
If a female of child-bearing age, must have a negative pregnancy test
Disqualifiers
Patients who are medically unstable, patients who are seriously or terminally ill, and patients whose clinical course is unpredictable
Patients with known sensitivities to the components of lumason
Trial design
Single group
Treatments tested in this trial
Sulfur Hexafluoride Lipid Microspheres
DrugGiven IV
Contrast-Enhanced Ultrasound
Procedure/SurgeryUndergo CEUS
Transarterial Chemoembolization
Procedure/SurgeryUndergo TACE
Medical Chart Review
Other interventionAncillary studies
Treatment groups
Trial outcomes
Primary outcomes
Recurrence
Sensitivity
Will be computed using a reference standard. Variables will be summarized with descriptive statistics, such as means with standard deviations or frequency counts with percentage, across the cohort and within group of interest. Diagnostic accuracy will be compared between hepatocellular carcinoma (HCC) and non-HCC and between all tumor subtypes.
Specificity
Will be computed using a reference standard. Variables will be summarized with descriptive statistics, such as means with standard deviations or frequency counts with percentage, across the cohort and within group of interest. Diagnostic accuracy will be compared between HCC and non-HCC and between all tumor subtypes.
Positive predictive value
Will be computed using a reference standard. Variables will be summarized with descriptive statistics, such as means with standard deviations or frequency counts with percentage, across the cohort and within group of interest. Diagnostic accuracy will be compared between HCC and non-HCC and between all tumor subtypes.
Secondary outcomes
Residual tumor vacularity
Will use diagnostic summary statistics and generalized estimating equations (GEE) logistic regression modeling. The bedside (interventional radiologist) versus offline (radiologist) reads will be compared using agreement, kappa statistics, and mixed modeling.
Diagnostic performance for each imaging mode
The diagnostic performance for each reader will be quantified from the volumetric contrast enhanced ultrasound exams and post-processed images. Diagnostic performance for each imaging mode will be compared across all readers using the GEE logistic regression approach. Quantitative H-scan data will be compared between complete and incomplete responders using multiple linear regression or GEE regression modeling, depending on how well assumptions hold for the former.
Ability of the model to predict binary treatment response
Model performance will be calculated using a leave-one-out cross-validation method to assess the ability of the model to predict binary treatment response. Accuracy, sensitivity and specificity will then be quantified and directly compared between 2 dimensional (D) and 3D datasets.
Sponsors and contacts
Click on the lead sponsor to view all of their trials.
john eisenbrey
Lead sponsor
Thomas Jefferson University
Sponsor institution
National Cancer Institute (NCI)
Collaborator