Project 1: Self-Triage by 2D Full-field Digital Mammography or Synthetic Images

NCT05960188 | Completed

• 1 other identifier: 2007P000646-D
• Study Type: interventional
• Target: 16
• Locations: 1 country
• Sites: 1

Brief Summary

One method of breast cancer screening involves radiologists reading digital tomosynthesis (DBT) images. DBT consists of a 3D stack of x-ray "slices" through the breast. The exam is accompanied by a 2D image like a standard mammogram, a single x-ray of the breast. In a screening setting, most cases are normal. Sometimes it is obvious that a case is normal from a quick look at the 2D image. It would speed up the process of screening if readers could dismiss a clearly normal case on the basis of the 2D image, alone, without looking at the DBT images. Obviously, the investigators would only want to "triage" cases in this way if the investigators were almost perfectly sure that no cancers would be missed. In this study, the investigators look at radiologist's willingness to triage cases and on the accuracy of their answers. In addition, the investigators ask about the impact of an Artificial Intelligence (AI) opinion. Would it be possible to triage an image on the basis of the AI opinion, alone? Radiologists will look at each case for up to five seconds and offer an opinion (on a 1-10 scale) about how sure they are that a case is normal. Next, they will see the opinion of the AI. Finally, they will say (using a 1-10) scale, how willing they would be for the AI to triage this case without human intervention. This study is the start of an effort to understand the conditions under which radiologists might be willing to declare a case "normal" with little or no human examination.

Conditions

Breast Cancer Screening

Keywords

Mammography; Digital Breast Tomosynthesis; Artificial Intelligence; Radiology; Medical Image Perception

Outcome Measures

Primary Outcomes (2)

• Abnormality rating accuracy

  Readers rate the abnormality of each image. The outcome measure is the agreement with gold standard truth for that case.

  through study completion, an average of 1 year

• AI Acceptance rating

  Readers state if they would accept the AI rating as definitive. The relevant measure is the acceptance rate as a function of AI rating and the human rating of abnormality.

  through study completion, an average of 1 year

Interventions

AI Opinion BEHAVIORAL

For each case, we give the radiologist a numeric score reflecting the AI's rating of the abnormality of the case.

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Must be Radiologists or radiology trainees
• some experience reading mammography.

You may not qualify if:

• acuity less than 20/25 with correction

Sponsors & Collaborators

• Brigham and Women's Hospital: lead

Study Sites (1)

Visual Attention Lab / Brigham and Women's Hospital

Boston, Massachusetts, 02215, United States

Location

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: SINGLE
• Who Masked: PARTICIPANT
• Masking Details: At the end of the study, the participant is debriefed about their performance.
• Purpose: DIAGNOSTIC
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor

Model Details: We are asking the opinions of a group of clinicians.

Study Record Dates

• First Submitted: July 14, 2023
• First Posted: July 25, 2023
• Study Start: March 1, 2023
• Primary Completion: March 4, 2023
• Study Completion: March 4, 2023
• Last Updated: December 11, 2025

Record last verified: 2025-12

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, ICF
• Time Frame: We will post data on the OSF site at the end of the study and it will remain there indefinitely
• Access Criteria: open, and we will also respond to requests.

Locations

US (1)