Contrast Enhanced Ultrasound Medical Imaging for Identifying Breast Masses

Breast Carcinoma Clinical Trial

Official title:

Characterizing Breast Masses Using an Integrative Framework of Machine Learning and Radiomics

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT06171607
• Other study ID #: 1B-19-5
• Secondary ID: NCI-2022-036651B
• Status: Recruiting
• Phase: Phase 1
• Start date: November 5, 2020
• Est. completion date: November 5, 2025

Study information

• Verified date: December 2023
• Source: University of Southern California
• Contact: Janet Jaime
• Phone: 323-865-3205
• Email: Janet.jaime[@]med.usc.edu
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial 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.

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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 100
• Est. completion date: November 5, 2025
• Est. primary completion date: November 5, 2024
• Accepts healthy volunteers: No
• Gender: Female
• Age group: 18 Years and older

Eligibility

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

Related Conditions & MeSH terms

• Breast Carcinoma
• Breast Neoplasms

Intervention

Procedure:
• Contrast-Enhanced Ultrasound
Undergo CEUS

Drug:
• Perflutren Lipid Microspheres
Given IV
• Sulfur Hexafluoride Lipid Microspheres
Given IV

Locations

Country	Name	City	State
United States	Los Angeles County-USC Medical Center	Los Angeles	California
United States	USC / Norris Comprehensive Cancer Center	Los Angeles	California

Sponsors (2)

Lead Sponsor	Collaborator
University of Southern California	National Cancer Institute (NCI)

Country where clinical trial is conducted

United States, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Radiomics-based ML-classifier framework	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.	Up to 12 months
Primary	Performance of radiomics-based ML approach to prevent unnecessary biopsies	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.	Up to 12 months