This clinical trial investigates the role of contrast enhanced ultrasound (CEUS) in identifying cystic breast masses as benign or malignant. Ultrasound is a diagnostic imaging test that uses sound waves to make pictures of the body without using radiation (x-rays). Ultrasounds are widely used to diagnose many diseases in the body. This trial may help researchers learn if using CEUS will help in determining whether or not an ultrasound guided biopsy is necessary.
Additional locations may be listed on ClinicalTrials.gov for NCT06171607.
Locations matching your search criteria
United States
California
Los Angeles
USC / Norris Comprehensive Cancer CenterStatus: Active
Contact: Bino A. Varghese
Phone: 323-865-3231
Los Angeles General Medical CenterStatus: Active
Contact: Bino A. Varghese
Phone: 323-865-3231
PRIMARY OBJECTIVES:
I. To examine and compare the distribution of CEUS parameters in breast masses that were evaluated as Breast Imaging Reporting and Data System (BI-RADS) 4a, 4b, 4c or 5 by conventional ultrasound (US) and were recommended for ultrasound guided biopsy, and to evaluate whether these parameters can be used to classify suspicious cystic-appearing breast masses as benign or malignant.
Ia. To develop a CEUS-based radiomics workflow to extract radiomic metrics (> 1600 features) in classifying breast mass malignancy (Radiomics).
Ib. To develop a systematic and rigorous machine learning (ML)-based framework comprised of classification, cross-validation and statistical analyses to identify classifiers for breast malignancy stratification based on CEUS-derived radiomic metrics (time-intensity curve [TIC] analysis and Radiomics) and deep learning approaches.
Ic. To assess the independent contribution of radiomics classifier, time-intensity curve and deep learning classifier to the model accuracy in discriminating benign from malignant cases using CEUS.
Id. To develop a contrast-enhanced spectral mammography (CESM)-based radiomics workflow to extract radiomic metrics (> 1600 features) in classifying breast mass malignancy (Radiomics).
Ie. To develop a systematic and rigorous machine learning (ML)-based framework comprised of classification, cross-validation and statistical analyses to identify classifiers for breast malignancy stratification based on CESM-derived radiomic metrics (TIC analysis and Radiomics) and deep learning approaches.
If. To assess the independent contribution of radiomics classifier, time-intensity curve and deep learning classifier to the model accuracy in discriminating benign from malignant cases using CESM.
Ig. To assess the potential benefit of an ensemble framework to integrate the different artificial intelligence (Al) classifiers (TIC analysis, Radiomics and Deep learning) in preventing unnecessary biopsy.
OUTLINE:
Patients receive a contrast agent (Lumason or DEFINITY) intravenously (IV) and then undergo CEUS scan over 60-90 minutes and/or receive iohexol IV and undergo CESM on study.
Trial PhaseNo phase specified
Trial Typediagnostic
Lead OrganizationUSC / Norris Comprehensive Cancer Center
Principal InvestigatorBino A. Varghese