Quantitative Microvasculature Imaging for Breast Cancer Detection and Monitoring
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age18+
SponsorMayo Clinic
About this trial
This study evaluates the effectiveness of a breast ultrasound method for the diagnosis of breast cancer. Diagnostic procedures, such as breast ultrasound, may help find and diagnose breast cancer, and may help measure a patient's response to earlier treatment. The purpose of this research is to test the effectiveness of a new investigational breast ultrasound method to detect an abnormality in the breast, and assess response to breast cancer treatment.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
AIM 1: Patient volunteers, ages >= 18 with suspicious breast masses scheduled for breast biopsy, or at least two weeks or more after breast biopsy
AIM 2: Patient volunteers, ages >= 18 who have biopsy proven breast cancer and are going under neoadjuvant chemotherapy and/or endocrine therapy had their baseline MRI and/or ultrasound
Disqualifiers
Patients with breast implants or any condition that does not allow proper use of ultrasound (US)
Trial population
Volunteers for Aim 1 are without a history of mastectomy or implants, and at least a single suspicious breast mass that is scheduled for biopsy. Volunteers for Aim 2 have biopsy proven breast cancer and are scheduled for chemotherapy and/or endocrine therapy.
Trial design
Design model
Case-control
Time perspective
Prospective
Treatments tested in this trial
Ultrasound
Procedure/SurgeryUndergo breast ultrasound
Treatment groups
550 Participants
are divided into 1 treatment groupGroup A: Observational (ultrasound)1 intervention
Trial outcomes
Primary outcomes
1
Specificity of ultrasound in detection of breast cancer
Will be reported including 95% confidence intervals derived from the cross-validation procedure.
Time frame
Up to study completion, an average of 1 yearSecondary outcomes
1
Accuracy in prediction of treatment response
Morphological parameters of the lesion's microvasculature will be assessed at each visit. Correlation between changes in the morphological parameters and the clinical measure of lesion size will be assessed. Analyses will be done by time periods separately, change from pre-chemotherapy to halfway through chemotherapy and again from pre to post-chemotherapy. The diagnostic accuracy estimates will be reported along with 95% confidence interval. Univariate associations between changes in microvasculature parameters assessments during a time period with the magnetic resonance imaging (MRI) assessment for change in the same time period will be assessed using logistic regression, reporting the odds ratio and 95% confidence interval. Multi-variable logistic regression will be used to assess independent associations of change in assessments with MRI assessment for change. T-tests will be used to compare microvasculature information between chemotherapy responders and non-responders.
Time frame
Up to study completion, an average of 1 yearOther outcomes
1
Specificity of ultrasound in prediction of NUMMA parameters - tortuosity
The diagnostic accuracy of the NUMMA parameters, such as tortuosity will be assessed using an automated quantification tool. Logistic regression models will examine these collective parameters as predictors of metastatic axillary lymph node in breast cancer patients. Area under the receiver operating characteristic curve (area under the curve) will be examined for univariate and multivariable models. Statistical evaluation of the performance of 2 dimensional (D) and 3D images will be performed with the McNemar's test separately for metastatic and reactive lymph nodes.
Time frame
Up to study completion, an average of 1 year2
Specificity of ultrasound in prediction of NUMMA parameters - vessel diameter
The diagnostic accuracy of the NUMMA parameters, such as vessel diameter will be assessed using an automated quantification tool. Logistic regression models will examine these collective parameters as predictors of metastatic axillary lymph node in breast cancer patients. Area under the receiver operating characteristic curve (area under the curve) will be examined for univariate and multivariable models. Statistical evaluation of the performance of 2 dimensional (D) and 3D images will be performed with the McNemar's test separately for metastatic and reactive lymph nodes.
Time frame
Up to study completion, an average of 1 year3
Specificity of ultrasound in prediction of NUMMA parameters - numbers of vessel segments
The diagnostic accuracy of the NUMMA parameters, such as numbers of vessel segments will be assessed using an automated quantification tool. Logistic regression models will examine these collective parameters as predictors of metastatic axillary lymph node in breast cancer patients. Area under the receiver operating characteristic curve (area under the curve) will be examined for univariate and multivariable models. Statistical evaluation of the performance of 2 dimensional (D) and 3D images will be performed with the McNemar's test separately for metastatic and reactive lymph nodes.
Time frame
Up to study completion, an average of 1 year4
Specificity of ultrasound in prediction of NUMMA parameters - vessel density
The diagnostic accuracy of the NUMMA parameters, such as vessel density will be assessed using an automated quantification tool. Logistic regression models will examine these collective parameters as predictors of metastatic axillary lymph node in breast cancer patients. Area under the receiver operating characteristic curve (area under the curve) will be examined for univariate and multivariable models. Statistical evaluation of the performance of 2 dimensional (D) and 3D images will be performed with the McNemar's test separately for metastatic and reactive lymph nodes.
Time frame
Up to study completion, an average of 1 yearSponsors and contacts
Click on the lead sponsor to view all of their trials.
Mayo Clinic
Lead sponsor
National Cancer Institute (NCI)
Collaborator
Source ClinicalTrials.gov