Clinical trial NCT03706534: Breast Ultrasound Image Reviewed With Assistance of Deep Learning Algorithms
Official title: Breast Ultrasound Image Reviewed With Assistance of Deep Learning
Study type: INTERVENTIONAL
Overall status: UNKNOWN
Phase: NA
Conditions: Breast Cancer
Breast Lesions
Breast Mass
Keywords: Breast cancer
Breast Imaging
Lead sponsor: Samsung Medison
Sponsor class: INDUSTRY
Collaborators: name: University of Rochester
First posted: 2018-10-16
Enrollment: 300
Sex: ALL
Minimum age (years): 19
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: RANDOMIZED
Intervention model: CROSSOVER
Intervention model description: This clinical study performed by multiple reader multiple case (MRMC) study design, where as set of clinical readers evaulate under multiple reading condition. All Interpreting physician(reader) independently read all of the cases. (fully-crossed design).
Primary purpose: DEVICE_FEASIBILITY

Brief summary: This study evaluates a second review of ultrasound images of breast lesions using an interactive "deep learning" (or artificial intelligence) program developed by Samsung Medical Imaging, to see if this artificial intelligence will help the Radiologist make more accurate diagnoses.

Detailed description: Using ultrasound images prospectively acquired, the purpose of this study entails a second review of ultrasound images with suspicious breast lesions using an interactive "deep learning" (or artificial intelligence) program developed by SamsungMedison Co.,Ltd.

The images will be reviewed by the radiologists twice: first without, and then with assistance of artificial intelligence program by SamsungMedison Co., Ltd.

BIRADS system will be used in this study.

The objectives of the study are twofold: to quantify the statistical equivalence of radiologists' opinion and AI's output (CADe), and to check BIRADS score-based diagnostic accuracy (CADx) that is gained by the Radiologists' use of this interactive tool

Eligibility criteria: 1. Inclusion Criteria:

   * Adult females or males recommended for ultrasound-guided breast lesion biopsy or ultrasound follow-up with at least one suspicious lesion
   * Age \> 18 years
   * Able to provide informed consent
2. Exclusion Criteria:

   * Unable to read and understand English
   * Unable or unwilling to provide informed consent
   * A patient with current or previous diagnosis of breast cancer in the same quadrant
   * Unable or unwilling to undergo study procedures
3. Subject Characteristics

   1. Number of Subjects: 300 subjects from 300 separate breast lesions can be acquired. If a subject has more than 1 suspicious lesion, each may be chosen by the radiologist attending as suitable for "second review".
   2. Gender and Age of Subjects: Adult females or males aged 18 years or older who meet all of the inclusion criteria and none of the exclusion criteria will be considered for enrollment. Minors are excluded as breast cancer is very rare in this age group.
   3. Racial and Ethnic Origin: There are no enrollment exclusions based on economic status, race, or ethnicity. Based on local and United States census data, the expected ethnic distribution will be approximately 26 Hispanic (approx. 16%) and 134 non-Hispanic people. Furthermore, the expected racial distribution is expected to be approximately 126 White (approx. 79% of the whole study), 21 Black or African America (13%), 8 Asian (5%), and 5 of other categories (3%).
   4. Vulnerable Subjects: It is unlikely that any UR students or employees will be enrolled unless their primary physician refers them to UR Medicine Breast Imaging at Red Creek for breast ultrasound and a suspicious lesion is found. We do not expect any of these referrals to be from staffs who work directly with the PIs.

Primary outcomes:
measure: Concordance rate
timeFrame: 2 days
Secondary outcomes:
measure: Reporting time
timeFrame: 2 day
measure: Consensus
timeFrame: 2 day
measure: Accuracy
timeFrame: 7 day
measure: Sensitivity
timeFrame: 7 day
measure: Specificity
timeFrame: 7 day
measure: Area Under Curve
timeFrame: 7 day
Other outcomes:
Locations:
city: Rochester
country: United States
facility: University of Rochester
state: New York
Study officials:
affiliation: Department of Imaging Sciences, University of Rochester
name: Avice O'Connell
role: PRINCIPAL_INVESTIGATOR
affiliation: Department of Electrical & Computer Engineering, University of Rochester
name: Kevin Parker
role: PRINCIPAL_INVESTIGATOR
References: