Clinical trial NCT06171607: Contrast Enhanced Ultrasound Medical Imaging for Identifying Breast Masses
Official title: Characterizing Breast Masses Using an Integrative Framework of Machine Learning and Radiomics
Study type: INTERVENTIONAL
Overall status: RECRUITING
Phase: PHASE1
Conditions: Breast Carcinoma
Lead sponsor: University of Southern California
Sponsor class: OTHER
Collaborators: name: National Cancer Institute (NCI)
First posted: 2023-12-14
Enrollment: 100
Sex: FEMALE
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: NA
Intervention model: SINGLE_GROUP
Primary purpose: DIAGNOSTIC

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.

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.

Eligibility 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

Primary outcomes:
measure: Radiomics-based ML-classifier framework
timeFrame: Up to 12 months
measure: Performance of radiomics-based ML approach to prevent unnecessary biopsies
timeFrame: Up to 12 months
Secondary outcomes:
Other outcomes:
Locations:
city: Los Angeles
country: United States
facility: Los Angeles County-USC Medical Center
state: California
city: Los Angeles
country: United States
facility: USC / Norris Comprehensive Cancer Center
state: California
Study officials:
affiliation: University of Southern California
name: Bino A Varghese, PhD
role: PRINCIPAL_INVESTIGATOR
References: