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.

NOTE: This registration is linked to a Human Subjects registration in ASSIST. That, in turn, is part of an NCI Grant, CA207490. The grant describes many proposed experiments and notes that many others might be done as follow-up studies. At the suggestion of the NIH, the investigators grouped these studies into three "studies", each covering multiple experiments. The experiment described here is part of "Study ID 386408 Project 1: Radiologist Studies". It is not possible to register a set of experiments through the PRS system in CT.gov and it is not possible to file an annual report for the grant (RPPR) without an NCT number for projects that have started collecting participants. Accordingly, the investigators are describing one experiment here that would be part of the "Project 1" bundle of studies.

The core idea of Project 1 is that it might be possible for Os to reliably eliminate a set of cases in screening mammography after just a brief look at a 2D image or, potentially, after an AI system takes a brief look at the image. That is, the clinician and/or the AI would look at the image and know "for sure" there is nothing there and would be willing to dismiss or "triage" the case on the basis of this brief look. If the reader was not completely sure, the case would get more scrutiny.

As a start at looking at this issue, the investigators wanted to estimate how willing clinicians would be to triage a case and how they would interact with an AI that was asked to triage cases. A challenge for any implementation of triage will be to get clinicians (to say nothing of lawyers, et al) to accept the idea of not looking at an image/case or of looking briefly at, say, the 2D image and being willing not to look at the 3D digital tomosynthesis (DBT) images. The experiment the investigators report here is intended to be a start on studying this issue. There is a continuum of cases from "obviously normal" to "obviously abnormal". The investigators wanted to estimate the point on that continuum below which a case is so normal, that readers would be willing to let the computer triage the case and/or would be willing to triage a case themselves. It is also possible that there are cases so abnormal that the patients can be recalled for further examination by the computer alone though the investigators are not studying that form of triage in this case. The investigators hypothesize that these triage points will be related to both the computer's rating of normality and the reader's rating.

Method: A bilateral 2D mammogram is presented for 5 seconds. The time limit is intended to limit the normal scrutiny that a radiologist would give to the case. The investigators want a decision based on the "gist" of the case. To mimic the low prevalence of disease in a screening mammography, only 4 of 150 cases are positive. Readers are told that the cases mimic a screening setting so they know that positive cases will be rare, but they are not told the actual prevalence.

The investigators ask radiologists to answer two questions about each of up to 150 single image "cases". ("Up to 150" because radiologists can and do quit at without completing all cases. Using a rating scale method, the investigators ask:

1. How sure are participants that these images are from a normal case?

   Next the investigators tell readers that "The computer rated these images as X out of 10 (10: highest probability of cancer present)." This AI value is a real rating of abnormality, generated by Transpara version 1.7.0 which returns a probability of malignancy score for the examination. The ratings were obtained by Sarah Verboom of Radboud U.

   Then the investigators ask the investigators asked if the reader would think that it was reasonable for the computer to triage the case without further human inspection.

2. How willing would participants be for the computer to make the decision about this case alone without having participants look at it?

Os answered using a sliding bar that served as a rating scale. The selected rating was displayed on top of the sliding bar.

Participants:

The first observers for this study were tested at a 'pop-lab', organized at the European Conference of Radiology (ECR, Vienna, March 2023). The investigators have been organizing these labs as an opportunity for researchers from many labs to come to big meetings like ECR where they might be able to test radiologists in larger numbers than at home. The upside is access to readers. The downside is that the investigators can typically get only 15-30 min of a reader's time. Thus, the readers for this experiment were a population of convenience. The investigators tested 15 readers. These varied widely in experience. The investigators asked how many screening cases they estimated that they read each year. This varied from 0 (students who had learned about mammography but were not in practice) to 8000. Readers also varied in how many cases they were willing to read for us, before running out of time/patience. The range was 19 to 148 (avg 83 cases). At this stage, the investigators are underpowered to say with any conviction if these variables have an important impact of the results. This is a chronic problem with testing experts like radiologists. It is extremely difficult to collect as much data as one would wish. Nevertheless, these data can give us information about the factors that will determine the success or failure of image triage.

Inclusion Criteria:

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

Exclusion Criteria:

• acuity less than 20/25 with correction

We share deidentified on request and, typically, on the Open Science Framework

We will post data on the OSF site at the end of the study and it will remain there indefinitely

open, and we will also respond to requests.

At the end of the study, the participant is debriefed about their performance.