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Trial record 1 of 1 for:    NCT06171607
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Contrast Enhanced Ultrasound Medical Imaging for Identifying Breast Masses

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ClinicalTrials.gov Identifier: NCT06171607
Recruitment Status : Recruiting
First Posted : December 14, 2023
Last Update Posted : December 14, 2023
Sponsor:
Collaborator:
National Cancer Institute (NCI)
Information provided by (Responsible Party):
University of Southern California

Study Description
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Brief Summary:
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.

Condition or disease Intervention/treatment Phase
Breast Carcinoma Procedure: Contrast-Enhanced Ultrasound Drug: Perflutren Lipid Microspheres Drug: Sulfur Hexafluoride Lipid Microspheres Phase 1

Detailed Description:

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 the best performing classifier for breast malignancy stratification based on CEUS-derived radiomic metrics (time-intensity curve [TIC] analysis and Radiomics).

Ic. To assess the independent contribution of radiomics classifier and time-intensity curve classifier to the model accuracy in discriminating benign from malignant cases (TIC analysis versus [vs.] Radiomics).

Id. To assess the potential benefit of machine learning classifier in preventing unnecessary biopsy (TIC analysis and Radiomics).

OUTLINE:

Patients receive a contrast agent (Lumason or DEFINITY) intravenously (IV) and then undergo CEUS scan over 60-90 minutes.

Study Design
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Study Type : Interventional  (Clinical Trial)
Estimated Enrollment : 100 participants
Allocation: N/A
Intervention Model: Single Group Assignment
Masking: None (Open Label)
Primary Purpose: Diagnostic
Official Title: Characterizing Breast Masses Using an Integrative Framework of Machine Learning and Radiomics
Actual Study Start Date : November 5, 2020
Estimated Primary Completion Date : November 5, 2024
Estimated Study Completion Date : November 5, 2025

Resource links provided by the National Library of Medicine

Drug Information available for: Perflutren

Arms and Interventions
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Arm Intervention/treatment
Experimental: Diagnostic (contrast agent, CEUS)
Patients receive a contrast tracer (Lumason or DEFINITY) IV and then undergo CEUS scan over 60-90 minutes.
 Procedure: Contrast-Enhanced Ultrasound
Undergo CEUS
Other Name: CEUS

Drug: Perflutren Lipid Microspheres
Given IV
Other Name: Definity

Drug: Sulfur Hexafluoride Lipid Microspheres
Given IV
Other Names:
  • Lumason
  • SF6 Lipid Microspheres
  • Sulfur Hexafluoride Lipid-type A Microspheres


Outcome Measures
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Primary Outcome Measures :
  1. Radiomics-based ML-classifier framework [ Time Frame: Up to 12 months ]
    The performance of radiomics-based ML classifier framework will be compared to the performance of the TIC metrics. The joint performance of radiomics and TIC analysis will be compared to their individual performances. The classifier performance will be assessed using the area under curve (AUC). The Z-test will be used to compare the difference between the area under the curves 1) AUCboth versus (vs.) AUCradiomic 2) AUCboth vs. AUCTIC 3) AUCTIC vs. AUCradiomic.

  2. Performance of radiomics-based ML approach to prevent unnecessary biopsies [ Time Frame: Up to 12 months ]
    Will assess the percentage of benign cases that can be classified as benign by ML (Specificity) thus been prevented from biopsy. Will select the diagnostic cut-off point based on the ROC curve constructed from the predicted probability. Such a cut-off point will result in a maximal sensitivity (100%). Specificity with 95% Clopper Pearson confidence interval will be obtained.


Eligibility Criteria
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Information from the National Library of Medicine

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Ages Eligible for Study:   18 Years and older   (Adult, Older Adult)
Sexes Eligible for Study:   Female
Accepts Healthy Volunteers:   No
Criteria

Inclusion Criteria:

  • Newly diagnosed breast masses assigned as BIRADS 4a, 4b, 4c or 5 by conventional US and recommended for ultrasound guided biopsy
  • Age >= 18 years
  • Female

Exclusion Criteria:

  • Contraindications to microbubble contrast: Patients who have a known pulmonary hypertension and any known hypersensitivity to US contrast agent
  • Women who are pregnant, possibly pregnant, or lactating
  • Women currently undergoing neoadjuvant chemotherapy
  • Women < 18 years of age
  • Masses in the same breast that had prior lumpectomy for cancer
  • Women with cancer in the same breast will be excluded however, women with cancer in the contralateral breast will be eligible to participate in the study
  • Women with an allergy to perflutren
  • Prior history of biopsy for that specific lesion
Contacts and Locations
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Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT06171607


Contacts
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Contact: Janet Jaime 323-865-3205 Janet.jaime@med.usc.edu

Locations
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United States, California
Los Angeles County-USC Medical Center Recruiting
Los Angeles, California, United States, 90033
Contact: Janet Jaime    323-865-3205    Janet.Jaime@med.usc.edu   
Principal Investigator: Bino A. Varghese, PhD         
USC / Norris Comprehensive Cancer Center Recruiting
Los Angeles, California, United States, 90033
Contact: Janet Jaime    323-865-3205    Janet.Jaime@med.usc.edu   
Principal Investigator: Bino A. Varghese, PhD         
Sponsors and Collaborators
University of Southern California
National Cancer Institute (NCI)
Investigators
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Principal Investigator: Bino A Varghese, PhD University of Southern California
More Information
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Responsible Party: University of Southern California
ClinicalTrials.gov Identifier: NCT06171607    
Other Study ID Numbers: 1B-19-5
NCI-2022-03665 ( Registry Identifier: CTRP (Clinical Trial Reporting Program) )
1B-19-5 ( Other Identifier: USC / Norris Comprehensive Cancer Center )
P30CA014089 ( U.S. NIH Grant/Contract )
First Posted: December 14, 2023    Key Record Dates
Last Update Posted: December 14, 2023
Last Verified: December 2023

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Studies a U.S. FDA-regulated Drug Product: Yes
Studies a U.S. FDA-regulated Device Product: No
Product Manufactured in and Exported from the U.S.: No
Additional relevant MeSH terms:
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Breast Neoplasms
Neoplasms by Site
Neoplasms
Breast Diseases
Skin Diseases