A Randomized Controlled Trial Comparing Screening Mammography With and Without Assistance From Artificial Intelligence for Breast Cancer Detection and Recall Rates in Adult Patients
Phase: PHASE4
Conditions: Breast Cancer Screening, Artificial Intelligence (AI)
Sponsor: Jonsson Comprehensive Cancer Center

Protocol NCT06934239 
 
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A Randomized Controlled Trial Comparing Screening Mammography With and Without 
Assistance from Artificial Intelligence for Breast Cancer Detection and Recall Rates in 
Adult Patients: Study Protocol 
 
CLINICAL INVESTIGATION PROTOCOL 
 
Principle Investigators: 
Joann G. Elmore, MD, MPH 
David Geﬀen School of Medicine, 
University of California, Los Angeles 
 
Diana L. Miglioretti, PhD 
University of California, Davis 
 
Sponsored by: 
University of California, Los Angeles, Jonsson Comprehensive Cancer Center 
Version Number: v. 1.0 
 
Day Month Year 
August 26, 2025 
 
 
Protocol Approved by DSMB: August 7, 2025 
 
This protocol contains conﬁdential information for use by the Investigators and their designated 
representatives participating in this clinical investigation. The protocol should be held conﬁdential and 
maintained in a secure location. 
Do not copy or distribute without written permission from the investigators, their designated 
representatives, or Patient-Centered Outcomes Research Institute (PCORI). 
 
 

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SPONSOR PROTOCOL APPROVAL 
Protocol Title: 
A Randomized Controlled Trial Comparing Screening Mammography 
With and Without Assistance from Artiﬁcial Intelligence for Breast 
Cancer Detection and Recall Rates in Adult Patients: Study Protocol 
 
Protocol Number: 
NCT06934239 
Version: 
Version 1.0 
Name and Title 
Date 
Signature 
 
 
 
 
 
 
 

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PROTOCOL REVISION HISTORY 
Protocol version # 
Release date 
Reason for change 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

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INVESTIGATOR SIGNATURE PAGE 
By signing below, the Investigators acknowledges that they have read the NCT06934239 Protocol and 
agree to conduct the study as outlined. The Investigators agree to maintain the conﬁdentiality of all the 
information received or developed with this protocol. 
 
____________________________ 
                     ____________________________ 
Joann G. Elmore, MD, MPH                           Diana L. Miglioretti, PhD   
Printed Name of Dual Principal Investigators 
 
 
 

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Table of Contents (Following SPIRIT 2025 guidelines for protocols for randomized trials) ................ Page 
Administrative Information .................................................................................................................. 7 
1a, 1b. Title and structured summary ....................................................................................................... 7 
2. Protocol version .................................................................................................................................. 11 
3a, 3b, 3c, 3d. Roles and responsibilities ................................................................................................ 11 
Open science ..................................................................................................................................... 13 
4. Trial registration .................................................................................................................................. 13 
5. Protocol and statistical analysis plan ................................................................................................... 14 
6. Data sharing ........................................................................................................................................ 14 
7a, 7b. Funding and conﬂicts of interest ................................................................................................. 14 
8. Dissemination policy ........................................................................................................................... 15 
Introduction ....................................................................................................................................... 16 
9a, 9b. Background and rationale ........................................................................................................... 16 
10. Objectives .......................................................................................................................................... 17 
Methods: patient and public involvement, trial design ....................................................................... 18 
11. Patient and public involvement ........................................................................................................ 18 
12. Trial design ........................................................................................................................................ 18 
Methods: participants, interventions, and outcomes ......................................................................... 19 
13. Trial setting ........................................................................................................................................ 19 
14a, 14b. Eligibility criteria ...................................................................................................................... 20 
15a, 15b, 15c, 15d. Intervention and comparator .................................................................................. 21 
16. Outcomes .......................................................................................................................................... 22 
17. Harms ................................................................................................................................................ 24 
18. Participant timeline ........................................................................................................................... 24 
19. Sample size ........................................................................................................................................ 27 
20. Recruitment ...................................................................................................................................... 28 
Methods: assignment of interventions ............................................................................................... 29 
Randomization ........................................................................................................................................ 29 
21a, 21b. Sequence generation .......................................................................................................... 29 
22. Allocation concealment mechanism ............................................................................................. 29 
23. Implementation ............................................................................................................................. 29 
24a, 24b, 24c. Blinding ............................................................................................................................ 29 
Methods: data collection, management, and analysis ........................................................................ 31 

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25a, 25b. Data collection methods ......................................................................................................... 31 
26. Data management ............................................................................................................................. 32 
27a, 27b, 27c, 27d. Statistical methods .................................................................................................. 33 
Methods: monitoring ......................................................................................................................... 36 
28a, 28b. Data monitoring committee .................................................................................................... 36 
29. Trial monitoring ................................................................................................................................. 36 
Ethics ................................................................................................................................................. 38 
30. Research ethics approval .................................................................................................................. 38 
31. Protocol amendments ....................................................................................................................... 38 
32a, 32b. Consent or assent .................................................................................................................... 38 
33. Conﬁdentiality ................................................................................................................................... 39 
34. Ancillary and post-trial care .............................................................................................................. 39 
References ......................................................................................................................................... 40 

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Administrative Information 
1a, 1b. Title and structured summary 
1a. Title stating the trial design, population, and interventions, with identiﬁcation as a protocol. 
A Randomized Controlled Trial Comparing Screening Mammography With and Without 
Assistance from Artificial Intelligence for Breast Cancer Detection and Recall Rates in 
Adult Patients: Study Protocol  
1b. Structured summary of trial design and methods, including items from the World Health Organization 
Trial Registration Data Set. 
We will conduct a randomized clinical trial (RCT) comparing screening outcomes when Digital Breast 
Tomosynthesis (DBT or 3D) mammograms are interpreted with vs. without an FDA-cleared AI tool, 
Transpara. 
Primary Registry and Trial Identifying Number: 
clinicaltrials.gov; NCT06934239 
Date of Registration in Primary Registry: 
April 18, 2025 
Secondary Identifying Numbers: 
N/A 
Source(s) of Monetary or Material Support: 
Patient-Centered Outcomes Research Institute 
(PCORI); California Breast Cancer Research 
Program  of the University of California 
Primary Sponsor:  
Jonsson Comprehensive Cancer Center, University 
of California, Los Angeles 
Secondary Sponsor(s): 
 
 
 
Contact for Public Queries:  
 
Contact person: Dr. Michelle L’Hommedieu, UCLA 
Email address: mlhommedieu@mednet.ucla.edu  
Telephone: (310) 592-9454 
Postal Address: 1100 Glendon Ave., Suite 900, Los 
Angeles, CA 90024 
 
Contact for Scientiﬁc Queries: 
Principal Investigator: Dr. Joann Elmore, UCLA 
Email address: jelmore@mednet.ucla.edu 
Telephone: (310) 794-3763 
Postal Address: 1100 Glendon Ave., Suite 900, Los 
Angeles, CA 90024 
 
Principal Investigator:  Dr. Diana Miglioretti 
Email address: dmiglioretti@ucdavis.edu 
Telephone: (530) 752-7168 
Postal Address: One Shields Ave., Med Sci 1C, 
Davis, CA 95616 
 
Contact person: Dr. Hannah Milch, UCLA 
Email address: hmilch@mednet.ucla.edu 
Telephone: (424) 440-3266 

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Postal Address: 1250 16th Street, Suite 2340, 
Santa Monica, CA, 90404 
Public Title:  
 
Pragmatic Randomized trial of artiﬁcial 
Intelligence for Screening Mammography (PRISM) 
Scientiﬁc Title: 
A Randomized Controlled Trial Comparing 
Screening Mammography With and Without 
Assistance from Artiﬁcial Intelligence for Breast 
Cancer Detection and Recall Rates in Adult 
Patients: Study Protocol 
 
 
Countries of Recruitment: 
United States (US) 
Health Condition(s) or Problem(s) Studied: 
Breast Cancer 
Intervention(s): 
For usual care arm, exams will be reviewed and 
interpreted as done in many usual clinical 
practices (radiologist alone). For the AI arm 
(radiologist assisted by AI), exams will be scored 
using ScreenPoint Medical’s trademarked and 
FDA-cleared AI decision support tool for 3D 
mammography, Transpara (referred to as “AI” in 
this protocol) and  radiologists will be presented 
with the AI scoring information before and during 
their interpretation of the mammogram images. 
 
We will start the trial using the Transpara version 
2.1 and transition to the Transpara version 2.1 
with temporal comparison once available. 
 
Key Inclusion and Exclusion Criteria: 
This trial will include all radiologists interpreting 
screening mammography and all adult patients 
undergoing screening mammography at any of 
the participating breast imaging facilities across 6 
regional health systems (UCLA, UC San Diego, 
University of Washington-Seattle, University of 
Wisconsin-Madison, Boston Medical Center, and 
University of Miami) during the trial period. 
Individuals must meet all the following inclusion 
criteria: 
 
1. Be at least 18 years of age or older 
 
2. Receive a screening mammogram at one of 
the participating breast imaging facilities OR 
be a radiologist who interprets screening 
mammograms at one of the participating 
breast imaging facilities. 

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Exclusion criteria: 
1. Patients who have opted out of all 
research at the health system. 
 
Study Type: 
Interventional, phase IV, parallel-group 
randomized controlled trial comparing screening 
outcomes when digital breast tomosynthesis 
(DBT) mammograms are interpreted with vs. 
without an FDA-cleared AI tool. Screening 
mammography exams will be randomized after 
image acquisition to be interpreted either with or 
without an AI decision-support tool. Radiologists 
cannot be blinded to study arm during screening 
mammography interpretation. However, 
interpreting radiologists and facility staﬀ (e.g., 
those scheduling the exams and those acquiring 
the images) will not know in advance which 
patients will be randomized to the AI tool. 
Radiology department staﬀ, including schedulers 
and clinical support staﬀ, and patients will remain 
blinded throughout the trial. Patients will not be 
informed of the use of AI, because the AI program 
is an FDA-cleared support tool currently in use at 
imaging facilities throughout the US.  
Date of First Enrollment: 
Pending – 
Sample Size: 
400,000 screening mammogram exams 
interpreted by approximately 108 radiologists. 
Recruitment Status: 
Pending (participants are not yet being recruited 
or enrolled at any site). 
Primary Outcomes: 
1. Outcome name: Cancer detection rate 
Metric/method of measurement: Number of 
screening exams recommended for breast 
biopsy (ﬁnal BI-RADS assessment of 4 or 5) 
resulting in detected cancer, per 1,000 
screening exams 
Timepoint: Cancer diagnosed within 90 days 
of positive study entry screening 
mammogram 
 
2. Outcome name: Recall rate 
Metric/method of measurement: Number of 
screening exams recalled for diagnostic work-
up (initial BI-RADS assessment of 0, 3, 4, or 
5), per 1,000 screening exams 
Timepoint: Day of interpretation 

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Key Secondary Outcomes: 
1. Outcome name: Interval cancer rate (i.e., 
false-negative rate) 
Metric/method of measurement: Number of 
screening exams with a negative assessment 
(ﬁnal BI-RADS assessment of 1 or 2), and 
breast cancer diagnosed within 1 year, per 
1,000 screening exams 
Timepoint: Cancer diagnosed within 365 days 
of a negative study entry screening 
mammogram 
 
2. Outcome name: False positive recall rate 
Metric/method of measurement: Proportion 
of screening exams recalled for additional 
imaging (ﬁnal BI-RADS assessment of 1, 2, or 
3), with no breast cancer diagnosed within 1 
year 
Timepoint: No cancer diagnosed within 365 
days of a positive study entry screening 
mammogram 
 
3. Outcome name: False positive short-interval 
follow-up recommendation rate 
Metric/method of measurement: Proportion 
of screening exams recalled for short-interval 
follow-up (ﬁnal BI-RADS assessment of 3) 
with no breast cancer diagnosed within 1 
year 
Timepoint: No cancer diagnosed within 365 
days of a positive study entry screening 
mammogram 
 
4. Outcome name: False positive biopsy 
recommendation rate 
Metric/method of measurement: Proportion 
of screening exams recalled for breast biopsy 
(ﬁnal BI-RADS assessment of 4 or 5) with no 
breast cancer diagnosed within 1 year 
Timepoint: No cancer diagnosed within 365 
days of a positive study entry screening 
mammogram 
 
5. Outcome name: Trust and conﬁdence in AI 
Metric/method of measurement: Focus group 
and survey data 
Timepoint: Years 1,2 and Years 4,5 
 

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6. Outcome name: Eﬃciency metrics (only for 
the UCLA site) 
Metric/method of measurement: 
Interpretation time required for radiologists to 
interpret each mammogram with versus 
without AI; Delivery time, using time stamp 
data from exam acquisition to delivery of 
results to patients (aka turnaround time) 
Timepoint: Timepoints as described above 
 
Ethics Review Main Trial: 
Date of approval: 
Approved (UCLA  IRB 24-1192) 
11/27/2024 
Ethics Review Radiologists’ Data: 
Date of approval: 
Approved (UCLA  IRB 24-0730) 
6/6/2024 
Name and contact details of ethics 
committee(s): 
UCLA Oﬃce of Human Research Protection 
Program (OHRPP). 
(310) 825-5344 
ohrpp@research.ucla.edu 
Completion date: 
N/A 
Summary results: 
N/A 
IPD sharing statement: 
Plan to share IPD: We will follow mandates of 
PCORI and individual state laws.   
 
2. Protocol version 
Version date and identiﬁer. 
Version date: 
August 26, 2025 
Identiﬁer: 
Version 1.0 
 
3a, 3b, 3c, 3d. Roles and responsibilities 
3a. Names, aﬃliations, and roles of protocol contributors. 
Joann Elmore, MD, MPH 
Professor of Medicine, University 
of California, Los Angeles 
Dual-PI of PCORI award 
(administrative PI) 
Diana L. Miglioretti, PhD 
Professor and Division Chief of 
Biostatistics, University of 
California, Davis 
Dual-PI of PCORI award 
Hannah Milch, MD 
Assistant Professor of Radiology, 
University of California, Los 
Angeles 
Co-PI of PCORI award, 
UCLA Site Lead 
Radiologist 
Christoph Lee, MD, MS, 
MBA 
Professor of Radiology, University 
of Wisconsin-Madison 
Co-PI of PCORI award 
Michelle L’Hommedieu, PhD 
Project Director, University of 
California, Los Angeles 
Project Director 
 
3b. Name and contact information for the trial sponsor. 

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Jonsson Comprehensive Cancer Center, University of California, Los Angeles 
 
3c. Role of trial sponsor and funders in design, conduct, analysis, and reporting of trial; including any 
authority over these activities. 
The principal investigators of this investigator-initiated trial have full responsibility for the design, 
conduct, analysis, and reporting of the trial.  
The trial funders are not involved in study design, conduct, analyses, or reporting. They do not have any 
authority over these activities. 
 
3d. Composition, roles, and responsibilities of the coordinating site, steering committee, end point 
adjudication committee, data management team, and other individuals or groups overseeing the trial, if 
applicable. 
Administrative Coordinating Site (UCLA): Dr. Elmore will serve as the Contact PI and lead of the 
Administrative Coordinating Site. She will oversee all aspects of the design, conduct, analyses, and 
reporting of the project and provide input on interpretation and translation of ﬁndings into clinical 
practice. She will oversee progress on the implementation of the technical infrastructure required for the 
project, guide the scientiﬁc direction, and ensure scientiﬁc rigor. In addition, she will oversee successful 
completion of all project-related administrative tasks. Dr. Elmore will oversee the involvement of the 
Data and Safety Monitoring Board and the engagement with stakeholder partners and patients to ensure 
that their perspectives are at the forefront of the design and implementation of this study. 
Data Coordinating site (UCD): Dr. Miglioretti will serve as Dual PI and the lead of the Data Coordinating 
Center (DCC).  She will oversee data collection and management, guide the scientiﬁc direction, and 
ensure scientiﬁc rigor. The DCC will be responsible for maintaining data integrity, security, and 
protections throughout the study. They will develop a comprehensive data management plan, including 
manuals of operations for clinical data collection and associated data dictionaries. A REDCap database 
will be developed to collect aggregate data from participating facilities for monthly enrollment reports. 
They will also facilitate the collection of precise data on which Transpara version and functionality is in 
use at each site, including dates of implementation. Additionally, the DCC will design and implement a 
data submission process and procedures for receiving de-identiﬁed exam-level data—including patient, 
exam, radiologist, and facility characteristics; radiologists’ interpretations; AI results; breast cancer 
outcomes; and radiologist survey data—from participating facilities. The DCC will review submitted data 
for completeness and quality, document and resolve data issues, and pool the cleaned data for analysis. 
The DCC will also assist participating sites with cancer registry linkages. The DCC biostatisticians will 
create reports for DSMB meetings and conduct analyses for reports and manuscripts. 
Executive Committee: This core decision-making body will include the two Dual-PIs, two Co-PIs, the 
project director, and the DCC project manager. The committee will meet twice monthly to monitor 
progress, resolve issues, and make decisions on scientiﬁc strategy and resource allocation. 
Steering Committee:   The Steering committee (SC) will include the dual PIs, two Co-PIs, all site PIs, and a 
patient representative. The SC will provide a forum for sharing best practices across sites and advise 
about all matters related to implementing study procedures at the local sites. The SC will be the primary 

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decision-making body for all matters related to the conduct of the study at the individual local sites. The 
SC will meet virtually approximately monthly in year one, and then quarterly thereafter. 
Endpoint Adjudication Committee: None 
Data and Safety Monitoring Board (DSMB): The Data and Safety Monitoring Board (DSMB) will be chaired 
by Dr. Ruth Carlos, who leads the ECOG-ACRIN Cancer Care Delivery Research Committee. It will include 
4 other individuals, including experts in cancer screening, RCT design, biostatistics, and one patient 
partner. The DSMB will evaluate interim data reports every 6 months for participant safety, study 
conduct, and progress, and to make recommendations concerning trial continuation, modiﬁcation, or 
termination. An interim data analysis will be conducted once half of exams are randomized. Results by 
study arm will be shared with the DSMB during closed session and will not be available to the executive 
or steering committees until enrollment is completed, and data are cleaned and locked. 
Patient Partner Advisory Board (PPAB): Patient partners (N = 8) chosen from the study data collection 
sites will meet approximately quarterly and help guide the study from the patient perspective. Patient 
partners will ensure that the study remains aligned with the needs and preferences of the populations 
we aim to serve. Furthermore, we will work closely with our patient partners and stakeholder groups to 
develop tailored dissemination materials to ensure that the ﬁndings are accessible and actionable for a 
wide audience. 
Stakeholder Partners: Stakeholder partners (N = ~15) include advocacy groups, clinicians, researchers, 
industry, health system leaders, and policy makers. Stakeholder partners will meet approximately 
annually with the research team and patient partners to discuss study progress. They will also provide 
input on the study and interpretation of study results. The goals of the stakeholder meetings are to 
garner input from a wide range of multiple perspectives as the study progresses, to promote conﬁdence 
in the utility of the AI tool if study results warrant an endorsement, and to create a wide network for 
disseminating results and implementing practice changes. 
Participating study sites for data collection: Data collection sites include UCLA, UC San Diego, University 
of Washington-Seattle, University of Wisconsin-Madison, Boston Medical Center, and University of 
Miami. The local team at each individual data collection site will vary, but in general will include the site 
PI(s), one or more clinical champions, an administrative IT staﬀ, a data analyst, a project manager, and a 
local patient representative.  The site PI(s) at each data collection site will be responsible for overseeing 
implementation of the study procedures and data collection. The local teams will address unique issues 
that may arise at an individual site.  The data managers from each data collection site will meet with the 
DCC approximately monthly in years 1 and 2 and as needed in years 3-5 to discuss data collection and 
submission protocols and issues.  
Open science 
4. Trial registration 
Name of trial registry, identifying number (with URL), and date of registration. If not yet registered, name 
of intended registry. 
Name of trial registry: 
clinicaltrials.gov 

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Identifying number and URL: 
NCT06934239; 
https://clinicaltrials.gov/study/NCT06934239 
Date of Registration: 
April 18, 2025 
 
5. Protocol and statistical analysis plan 
Where the trial protocol and statistical analysis plan can be accessed. 
The trial protocol and statistical analysis plan can be requested from the Dual PIs.   
6. Data sharing 
Where and how the individual deidentiﬁed participant data (including data dictionary), statistical code, 
and any other materials will be accessible. 
The DCC will ensure reproducibility through well-documented and maintained code and data ﬁles that 
will be accessible via deposit in a PCORI-designated data repository unless otherwise prohibited by law. 
Information related to the AI intervention, Transpara,  including any restrictions in access or re-use of 
the intervention or its code can be accessed from ScreenPoint, Medical BV, Transpara.   
7a, 7b. Funding and conﬂicts of interest 
(7a) Sources of funding and other support (e.g., supply of drugs). 
Patient-Centered Outcomes Research Institute (PCORI) 
BPS-2024C2-39667 
. 
California Breast Cancer Research Program of the University 
of California 
B30TR8496 
National Institutes of Health/National Cancer Institute 
pending 
 
 
(7b) Financial and other conﬂicts of interest for principal investigators and steering committee members. 
Financial and other conﬂicts of interest for Principal Investigators: 
Dr. Joann Elmore (UCLA) 
Serves as Editor-in-Chief for Adult General 
Medicine topics at UpToDate and receives 
textbook royalties from Elsevier unrelated to this 
study. 
Dr. Hannah Milch (UCLA) 
None 
Dr. Haydee Ojeda-Fournier (UCSD) 
None 
Dr. Janie Lee (University of Washington) 
None 
Dr. Christoph Lee (University of Wisconsin-
Madison) 
Reports disclosures unrelated to this study. He 
receives textbook royalties from UpToDate, Inc., 
Oxford University Press, and McGraw Hill, Inc.; 
personal fees from American College of Radiology 
for journal editorial board work; and clinical 
advisory fees from RadNet. 
Dr. Clare Poynton (Boston Medical Center) 
None 

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Dr. Jose Net (University of Miami) 
None 
Dr. Diana Miglioretti (UC Davis) 
Receives book royalties from Elsevier unrelated to 
this study and is faculty member in the 
Radiological Society of North America’s Clinical 
Trial Methodology Workshop and receives an 
honorarium for her participation 
Ms. Katherine Fultz Hollis (patient member of 
Steering Committee) 
None 
8. Dissemination policy 
Plans to communicate trial results to participants, health care professionals, the public, and other 
relevant groups (e.g., reporting in trial registry, plain-language summary, publication). 
Our Patient Partner Advisory Board and stakeholders will provide support and advice on the 
dissemination of aggregate study results to all the relevant stakeholders and communities. Multiple 
communication methods will be used to share study results from the clinical trial with patients and the 
methods may vary by health system site. Study sites’ healthcare systems will be encouraged to publish 
the PCORI lay language Results Summary as part of their larger communication strategy on any patient-
facing websites or newsletters. 
Radiologists will not receive their aggregate performance metrics with and without AI during the trial. 
Each participating site has agreed to not access or view their internal performance data until the last 
participant is randomized. After trial completion, individual radiologists will receive their interpretive 
performance metrics with and without AI, shared conﬁdentially. No data identifying individual 
radiologists or patients will be publicly posted. 
Trial results will be posted to ClinicalTrials.gov.  Manuscripts will be submitted to peer-reviewed journals 
to disseminate ﬁndings to the broader clinical and academic communities. We will also present ﬁndings 
at major national conferences (e.g., Radiological Society of North America Annual Meeting) to 
disseminate results to relevant professional audiences. 

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Introduction 
9a, 9b. Background and rationale 
9a. Scientiﬁc background and rationale, including summary of relevant studies (published and 
unpublished) examining beneﬁts and harms for each intervention. 
Computer Aided Detection (CAD) tools for mammography were FDA-cleared in 1998, based on small 
reader studies, and quickly adopted across the US, reaching 92% of U.S. mammography facilities by 
2016. However, our study team’s prior work1 demonstrated that CAD was associated with lower 
diagnostic accuracy, increased false positives, and higher costs, demonstrating limitations of early 
adoption without robust clinical validation.2 Our follow-up study conﬁrmed that historical CAD for 
mammography was associated with lower diagnostic accuracy, more patient harm, and higher costs in 
clinical settings.2 These risks were not evident in pre-market reader studies and indicate a gap in real-
world evaluation of emerging technologies. 
Today’s FDA-cleared AI tools for mammography oﬀer potential improvements in cancer detection and 
workﬂow eﬃciency. Potential harms observed with prior CAD systems warrant evaluation of current AI 
tools. This pragmatic randomized controlled trial adjudicates this gap in evidence for newer, FDA-cleared 
artiﬁcial intelligence (AI) technology that is already in use internationally and at many U.S. breast 
imaging facilities.  
This trial is intended to inform the interpretation of screening mammography and subsequent patient 
outcomes, which is pertinent to both radiologists interpreting screening mammograms and patients 
receiving screening mammograms, by comparing the eﬀectiveness of interpreting screening of digital 
breast tomosynthesis (DBT) with vs. without AI assistance in improving breast cancer detection with no 
more than a 1.5 percentage point increase in recall rate. The intended use of the AI intervention in the 
context of the clinical pathway is to provide radiologists who interpret screening mammograms with 
decision support at the time of interpreting these mammograms. More speciﬁcally, radiologists will be 
the ones using the AI intervention;  the screening mammography exams are randomized after image 
acquisition and will be interpreted either with or without the AI decision-support tool by the radiologists 
(see 15a.Intervention and comparator for details). We hypothesize the following: 
AI use will be associated with an increase in cancer detection and an initially higher recall rate as 
radiologists start using AI, followed by a recall rate comparable to that without AI (no more than 1.5 
percentage-points higher) after a learning curve period. AI use will be associated with lower rates of 
missed breast cancers and similar rates of false positives after a learning curve period. 
Improved patient outcomes with AI will be most pronounced for exams on women who are White, older, 
and have less dense breasts, and on baseline exams. AI will aid patient outcomes when the 
interpretation is by radiologists with less clinical experience, lower annual interpretive volume, and less 
tolerance of ambiguity, yet greater automation bias (the tendency for humans to defer to a computer 
algorithms’ results) will be noted among these radiologists. 
9b. Explanation for choice of comparator. 

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The comparator to interpretation of screening mammograms by radiologists alone (usual care of 
mammography interpretation without AI support) was selected for this trial as it is a leading AI tool 
designed to assist radiologists in interpreting screening mammography. We selected Transpara, 
ScreenPoint Medical’s FDA-cleared AI decision support tool for DBT, because it is one of the most widely 
adopted AI tools in clinical practice. In the U.S., Transpara is used by over 150 institutions, including large 
imaging center chains, community hospitals, and academic medical centers, and it is in use at more than 
100 international sites, including several national screening programs. Transpara combines traditional 
physics-based modeling with deep learning algorithms to detect suspicious ﬁndings on mammograms. 
Its goal is to assist radiologists in identifying cancers earlier and more accurately, thereby increasing the 
likelihood of successful treatment and reducing the need for more invasive procedures, ultimately 
lowering breast cancer–related morbidity.  
The Transpara 2.1 version will be used at participating sites early in this trial. ScreenPoint has received 
FDA clearance for a temporal comparison add on (i.e., Transpara version 2.1 + temporal comparisons). 
This version represents the most advanced AI tool for mammography currently available in the U.S. and 
internationally, as it incorporates prior mammograms into the AI assessment process. It allows the AI 
algorithm to compare the current mammogram with up to 6.5 years of prior images (aligning with how 
radiologists use priors in their interpretation). Both versions share the same foundational architecture, 
but the temporal comparison version additionally integrates prior images when generating AI scores. We 
anticipate that sites will transition to the updated version with temporal comparisons when it becomes 
commercially available in 2026.  
10. Objectives 
Speciﬁc objectives related to beneﬁts and harms. 
The primary objective is to compare the eﬀectiveness of interpreting digital breast tomosynthesis 
screening exams with vs. without AI assistance in improving breast cancer detection with no more than a 
1.5 percentage point increase in recall rate. 
Secondary objectives are to evaluate additional screening harms (false-negative rate, false positive recall 
rate, false positive short interval follow-up recommendation rate, false positive biopsy recommendation 
rate) and to assess heterogeneity of intervention eﬀects by patient, exam, and radiologist factors. 
Additionally, assessing trust and conﬁdence in AI via survey and focus group data is a secondary 
objective.

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Methods: patient and public involvement, trial design 
11. Patient and public involvement 
Details of, or plans for, patient or public involvement in the design, conduct, and reporting of the trial. 
Our patient partners will meet virtually with the research team quarterly, providing input on the study’s 
progress. They will be involved in interpreting results and co-authoring papers, to incorporate patient 
perspectives into dissemination. We will gather input during meetings and via email, using an informal 
consensus process to ensure all perspectives are considered in ﬁnal decisions. 
Our stakeholder partners will meet virtually with the research team yearly to review study progress, 
contribute to interpretation of results, and discuss potential for broader dissemination and 
implementation of the ﬁndings. These meetings will garner input from a wide range of perspectives, 
build conﬁdence in the AI tool (should results warrant endorsement), and establish a network for 
disseminating the study’s outcomes and implementing necessary practice changes. 
Patient and Stakeholder input will be sought at key decision points throughout, including design of any 
patient-facing materials, analysis and interpretation of results, and development of dissemination 
strategies. 
12. Trial design 
Description of trial design including type of trial (e.g., parallel group, crossover), allocation ratio, and 
framework (e.g., superiority, equivalence, noninferiority, exploratory). 
This is an interventional, phase IV, parallel-group randomized controlled trial comparing patient-centered 
outcomes when DBT exams are interpreted with vs. without an FDA-cleared AI tool. Screening 
mammography exams will be randomized with a 1:1 allocation ratio to be interpreted either with or 
without an AI decision-support tool.  The co-primary outcomes are cancer detection rate and recall rate. 
We will consider AI to be superior to usual care if the cancer detection rate is signiﬁcantly higher and the 
recall rate is non-inferior.

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Methods: participants, interventions, and outcomes 
13. Trial setting 
Settings (e.g., community, hospital) and locations (e.g., countries, sites) where the trial will be conducted. 
This trial will be conducted at individual breast imaging facilities that are aﬃliated with 6 US regional 
health systems: 
 
• 
University of California, Los Angeles (UCLA) 
• 
University of California, San Diego 
• 
University of Washington-Seattle 
• 
University of Wisconsin-Madison 
• 
Boston Medical Center 
• 
University of Miami 
Study Setting: The onsite requirements needed to integrate the AI intervention into the trial setting 
include educational training for radiologists who will use the tool, the health systems’ integration of the 
AI tool and Aidoc’s randomization platform into their clinical workﬂows, adequate staﬃng, the ability to 
track cancer outcomes, and the ability to conduct focus groups. 
We will require all radiologists, including breast imaging fellows, to complete a one-hour training session 
developed by the UCLA site.  This session will include a review of data from the external validation study 
with case examples of AI false alarms and cases of AI missing cancers. The goal of this training is to 
encourage appropriate use of the technology and protect against automation bias (the tendency for 
humans to defer to a computer algorithm result). The pre-RCT training sessions will be recorded and 
viewed as onboarding for any new faculty hired during the RCT. In addition, ScreenPoint will provide 
individualized training at each site, tailored to the clinical needs and requests of that site. 
All participating institutions must purchase and clinically implement the AI tool. Additionally, all 
participating institutions must pay for the AI tool with system-level technology use agreements with 
ScreenPoint and will be responsible for all necessary payments for clinical use of the AI algorithm. No 
patients will be charged for the use of the AI tool. Study sites will need to use Aidoc, a third-party AI 
delivery platform, as Aidoc developed the randomization tool that ensures AI information is overlaid on 
approximately 50% of exams within the existing Picture Archiving Communication Systems (PACS) at the 
sites.   Our study is not being done within a single national chain or health system and thus there are 
many diﬀerent internal IT and PACS systems (e.g., Fuji Synapse, Sectra), making our ﬁndings more 
representative of the US.   Aidoc can also export all relevant AI data for analysis. These automated 
capabilities are critical for successful trial execution as manual randomization and data collection is not 
feasible for 400,000 exams across multiple health systems and imaging facilities. 
Additionally, all sites have designated clinical directors that manage individual facilities or groups of 
imaging facilities.  These clinical directors will troubleshoot and oversee day-to-day clinical trial issues on 
the ground and be key to smooth operations and success.  

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Each of our health systems has strong medical record systems to track cancer outcomes and, we have 
budgeted for linkage to the local and state tumor registries. Funds are included to support two waves of 
focus groups at all sites. 
Taken together, this onsite support will allow for successful study implementation. 
14a, 14b. Eligibility criteria 
14a. Eligibility criteria for participants. 
This trial will include all interpreting radiologists and all adult patients undergoing screening 
mammography at any of the participating breast imaging facilities during the trial period. To be 
eligible to participate in this study, an individual must meet all the following criteria: 
 
1. Be at least 18 years of age or older 
2. Receive a screening mammogram at one of the 
participating breast imaging facilities OR be a 
radiologist who interprets screening mammograms at 
one of the participating breast imaging facilities. 
Exclusion criteria: Patients who have requested that 
their data not be used for any research purposes will 
be excluded and their data will not be submitted to the 
DCC.  
All patients who receive a screening mammogram at one of the participating breast imaging facilities— 
unless they have opted out of all research at the institution—and all radiologists who interpret 
screening mammograms at these facilities will be included. No screening exams will be excluded based 
on the nature or quality of their input data (i.e., screening mammogram images). Exams randomized to 
the AI arm will be included in the analysis even if the AI tool does not fully run for any reason. 
 
14b. If applicable, eligibility criteria for sites and for individuals who will deliver the interventions (e.g., 
surgeons, physiotherapists). 
All interpreting radiologists at any of the participating breast imaging facilities across 6 regional health 
systems during the trial period will participate in this trial: 
 
• 
University of California, Los Angeles (UCLA) 
• 
University of California, San Diego 
• 
University of Washington-Seattle 
• 
University of Wisconsin-Madison 
• 
Boston Medical Center 
• 
University of Miami 
 

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15a, 15b, 15c, 15d. Intervention and comparator 
15a. Intervention and comparator with suﬃcient details to allow replication including how, when, and by 
whom they will be administered. If relevant, where additional materials describing the intervention and 
comparator (e.g., intervention manual) can be accessed. 
For usual care arm of exams not randomized to AI, exams will be reviewed and interpreted as done in 
usual clinical practice (i.e., single read by radiologist alone).  
For the AI arm, radiologists will be assisted by Transpara AI (referred to as “AI” in this protocol).  The FDA-
cleared Transpara 2.1 version will be used at participating sites at study start. ScreenPoint has received 
FDA clearance for a temporal comparison add on (i.e., Transpara version 2.1 + temporal comparisons). 
This version allows the AI algorithm to compare the current mammogram with up to 6.5 years of prior 
images (aligning with how radiologists use priors in their interpretation). Both versions share the same 
foundational architecture, but the temporal comparison version additionally integrates prior images 
when generating AI scores. We anticipate that sites will transition to the updated version with temporal 
comparisons when it becomes commercially available in 2026. We will collect precise data on which 
Transpara version and functionality is in use at each site, including dates of implementation. 
The procedure for acquiring and selecting the screening mammograms (input data) for the AI 
intervention is as follows: patients who visit any of the participating imaging facilities for a screening 
mammogram will have their screening mammograms randomly assigned to be interpreted either with or 
without AI assistance. 
There is no human-AI interaction in the handling of the input data (i.e., screening mammogram images) 
at the time of acquiring the screening mammogram. Randomization occurs within minutes after the 
breast imaging acquisition (i.e., when the mammography technologist captures the images) by an 
automated system. Thus, the AI data (or lack thereof) is embedded within the mammogram before the 
radiologist opens the exam, preventing any option to “add AI” to an exam randomized to be interpreted 
without AI. Radiologists will be aware of AI availability only at the time of interpretation. The AI 
information will appear upon opening the exam (e.g., the AI information pops up with the exam images). 
The output of the AI intervention is AI information that appears upon opening the exam (for exams 
randomized to AI). More speciﬁcally, for exams randomized to AI, the ﬁrst image displayed to the 
radiologist upon opening an exam on the viewing station will be the AI report. This AI report provides the 
following concise, one-page, standardized information (Described below for Version 1.7): 
1) Overall exam risk category: Overall exam-level risk is divided into three categories: elevated, 
intermediate, or low. 
2) Image region scores: Image region markings and scores identify individual areas of interest 
demarcated with bounding boxes and state relevant slice locations for the 3D exams. The lesion score 
ranges from 1-100, with 100 denoting the highest level of suspicion. A maximum of 8 markings may be 
present for a given exam (2 markings per each of the 4 standard views).  
After viewing the report, the radiologist will interpret the exam; any speciﬁc lesion bounding boxes 
ﬂagged by AI will be visible while interpreting the exam. The radiologist can toggle these markings on 
and oﬀ as needed and will have complete autonomy regarding the ﬁnal interpretation of the exam as 

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positive or negative, meaning that they can choose to ignore the AI information. The AI intervention’s 
output can contribute to radiologists’ decision making, if desired, as they will have access to the AI tool’s 
interpretation of screening mammograms, for exams randomized to AI. 
15b. Criteria for discontinuing or modifying allocated intervention/comparator for a trial participant (e.g., 
drug dose change in response to harms, participant request, or improving/worsening disease). 
N/A 
15c.  Strategies to improve adherence to intervention/comparator protocols, if applicable, and any 
procedures for monitoring adherence (e.g., drug tablet return, sessions attended). 
Each data collection site has agreed to participating in the trial at the health system level. Each site will 
have their own dedicated site PI(s), IT and data support staﬀ to ensure that they are following this 
protocol. The site PI will meet regularly with the IT/data teams to ensure that the study protocol is being 
followed and that the infrastructure setup allows for seamless data integration. Data collection sites will 
upload monthly enrollment reports indicating how many screening exams were randomized to the AI 
and usual care arms and how many randomized patients opted out of having their data used for 
research. The DCC will monitor these reports to ensure the 1:1 randomization is being properly 
implemented. 
Procedures for assessing and handling poor-quality or unavailable input data:  The input data for this 
trial are the screening mammogram images and the AI report that is shown to radiologists. We do not 
anticipate issues with image quality or data availability.  There may be extremely rare exams deemed 
“poor quality” and the patients are asked to return and the images are placed under the same accession 
number. However, in rare cases, the AI scoring may fail to process a screening mammogram. If this 
occurs, the exam will still be included in the analysis. The frequency of AI scoring failures will be 
documented and reported, if feasible. 
 
15d. Concomitant care that is permitted or prohibited during the trial. 
N/A 
16. Outcomes 
Primary and secondary outcomes, including the speciﬁc measurement variable (e.g., systolic blood 
pressure), analysis metric (e.g., change from baseline, ﬁnal value, time to event), method of aggregation 
(e.g., median, proportion), and time point for each outcome. 
Primary Outcomes: 
1. 
Outcome name: 
Cancer detection rate 
 
Metric/method of measurement: 
Number of screening exams recommended for 
breast biopsy (ﬁnal BI-RADS assessment of 4 or 5) 
resulting in detected cancer, per 1,000 screening 
exams 

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Timepoint: 
Cancer diagnosed within 90 days of positive study 
entry screening mammogram 
 
2. 
Outcome name: 
Recall rate 
 
Metric/method of measurement: 
Number of screening exams recalled for 
diagnostic work-up (initial BI-RADS assessment of 
0, 3, 4, or 5), per 1,000 screening exams 
Timepoint: 
Day of interpretation 
 
 
 
Key Secondary Outcomes: 
1. 
Outcome name: 
Interval cancer rate (i.e., false-negative rate) 
Metric/method of measurement: 
Number of screening exams with a negative 
assessment (ﬁnal BI-RADS assessment of 1 or 2), 
and breast cancer diagnosed within 1 year, per 
1,000 screening exams 
Timepoint: 
Cancer diagnosed within 365 days of a negative 
study entry screening mammogram 
 
2. 
Outcome name: 
False positive recall rate 
Metric/method of measurement: 
Proportion of screening exams recalled for 
additional imaging (ﬁnal BI-RADS assessment of 1, 
2, or 3), with no breast cancer diagnosed within 1 
year 
Timepoint: 
No cancer diagnosed within 365 days of a positive 
study entry screening mammogram 
 
3. 
Outcome name: 
False positive short-interval follow-up 
recommendation rate 
Metric/method of measurement: 
Proportion of screening exams recalled for short-
interval follow-up (ﬁnal BI-RADS assessment of 3) 
with no breast cancer diagnosed within 1 year 
Timepoint: 
No cancer diagnosed within 365 days of a positive 
study entry screening mammogram 
 
4. 
Outcome name: 
False positive biopsy recommendation rate 
Metric/method of measurement: 
Proportion of screening exams recalled for breast 
biopsy (ﬁnal BI-RADS assessment of 4 or 5) with 
no breast cancer diagnosed within 1 year 
Timepoint: 
No cancer diagnosed within 365 days of a positive 
study entry screening mammogram 
 
5. 
Outcome name: 
Trust and conﬁdence in AI 

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Metric/method of measurement: 
Focus group and survey data 
 
Timepoint: 
Years 1,2 and Years 4,5 
 
6. 
Outcome name: 
Eﬃciency metrics (only for the UCLA site) 
 
Metric/method of measurement: 
Interpretation time required for radiologists to 
interpret each mammogram with versus without 
AI 
Delivery time, using time stamp data from exam 
acquisition to delivery of results to patients (aka 
turnaround time)  
 
 
Timepoint: 
Timepoints as described above 
 
17. Harms 
How harms are deﬁned and will be assessed (e.g., systematically, nonsystematically). 
Harms are deﬁned, consistent with UCLA’s Oﬃce of Human Research Protection Program, as an adverse 
event as “any untoward or unfavorable medical occurrence in a human subject (physical or psychological 
harm) temporally associated with the subject’s participation in the research (whether or not related to 
participation in the research”.  
In this study, the primary potential harm from screening mammography is being recalled for additional 
diagnostic work-up, which may lead to anxiety, unnecessary imaging, or invasive procedures. Therefore, 
recall rate will be tracked as the key adverse event. While screening mammograms will be obtained as 
part of routine clinical care, we will not systematically track other downstream outcomes such as biopsy 
complications or deaths, as doing so would require considerable additional eﬀort. 
Harms will be assessed systematically through ongoing monitoring of recall rates across trial arms. Recall 
rates will be reviewed by the DSMB through interim data reports every 6 months to evaluate participant 
safety.  An interim analysis will be conducted once half of the exams have been randomized. Based on 
this analysis and other available information, the DSMB may recommend trial continuation, 
modiﬁcation, or early termination. For example, if the recall rate in the AI arm is signiﬁcantly inferior 
without a corresponding improvement in the cancer detection rate, this may warrant DSMB action. 
We will also evaluate interval cancer rates and false positive rates as harms, but these outcomes cannot 
be estimated until after linkages are performed with state or regional cancer registries.   
18. Participant timeline 
Time schedule of enrollment, interventions (including any run-ins and washouts), assessments, and visits 
for participants. A schematic diagram is highly recommended (see Figure). 
The RCT will continue for approximately two years, longer at the UCLA site, during which the AI will be 
randomized to be turned on or oﬀ (1:1) at the exam level (See Figure).   

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Figure. Participant timeline. 
Timepoint 
Day 1 
Interim Days 
Day 90 
Day 365 
Visit to Imaging 
Facility for 
Screening 
Mammogram 
X 
 
 
 
Completion of 
Clinical Intake 
Survey (as part of 
routine clinical 
care) 
X 
 
 
 
Follow Patient to 
Collect Breast 
Cancer Outcome 
Data for Cancer 
Detection Rate 
 
 
 
 
Follow Patient to 
Collect Breast 
Cancer Outcome 
Data for Interval 
Cancer Rates, False-
Positive Rates, and 
Cancer 
Characteristics 
 
 
 
 
 
 
 

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Figure. Radiologist Timeline 
Timepoint 
Day 1 
Interim Days 
Day ~730 
Closeout 
Radiologist Baseline 
Survey 
X 
 
 
 
Interpretation of 
screening 
mammograms from 
both randomization 
arms: 
 
 
 
 
AI arm 
Usual Care arm 
 
 
 
 
Radiologist Follow-
Up Survey 
 
 
 
X 
 
 

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19. Sample size 
How sample size was determined, including all assumptions supporting the sample size calculation. 
Our target sample size of 400,000 screening exams was chosen to ensure suﬃcient power for evaluating 
our primary and secondary outcomes and for assessing heterogeneity of eﬀects in subgroups consisting 
of at least 25% of the study population. Power calculations for clinical screening outcomes (Table 1) are 
based on baseline outcome estimates from the participating sites and national benchmarks for 3D 
screening mammography.3 To account for the interim analysis, power calculations for cancer detection 
rate assume a two-sided alpha of 0.0492 and for the non-inferiority test for recall rate, a one-sided alpha 
of 0.0246. We did not adjust alpha for two primary outcomes, as we will require “success” on both 
primary outcomes to consider interpretation with AI to be superior to that without AI, i.e., we will 
conclude that interpretation with AI improves screening outcomes if both the cancer detection rate is 
signiﬁcantly higher and the recall rate increases by no more than 1.5 percentage-points with AI-
assistance. This non-inferiority margin was chosen based on expert opinion of the increase in recall rate 
that would be a reasonable tradeoﬀ for an increased cancer detection rate. Power calculations assume 
outcomes with and without AI are independent, because this intervention varies both within and 
between clusters, making it impossible to predict the eﬀect of clustering on variance estimates.4 
Detectable diﬀerences were calculated using PASS software for a Z-test of the diﬀerence between two 
proportions with un-pooled variance. 
 
For cancer detection rate, we expect 80% power to detect an increase from 5.8/1000 without AI to 
6.5/1000 with AI (RR=1.12). With a non-inferiority margin of 1.5% for recall rate, we expect 90% power 
to reject the null hypothesis that AI-assisted interpretation is inferior if the recall rate with AI is increases 
by ≤1.2 percentage points from 10.1% to ≤11.3%. If recall rate is found to be non-inferior with AI 
assistance, we will test for superiority. We will have >99% power to detect a decrease in recall rate of 
≥0.41 percentage-points (RR≤0.96) and 80% power to detect a decrease of 0.27 percentage-points 
(RR=0.97). 
For our interim analysis with 200,000 exams, we will have >99% power to detect an absolute diﬀerence 
in recall rate of 1.5 percentage points and 80% power to detect an absolute diﬀerence in cancer 
detection rate of 1.3/1000 (RR=1.22) with a two-sided alpha of 0.0492. 
For secondary outcomes, we expect 80% power to detect a decrease in interval cancer rate from 
0.83/1000 to 0.59/1000 (RR=0.72), an increase from 9.5% to 9.76% for false positive recalls, an increase 

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from 1.6% to 1.7% for false positive short-interval follow-up recommendations, and an increase from 
0.98% to 1.07% for false positive biopsy recommendations. 
For evaluating heterogeneity of treatment eﬀects (the), power calculations were conducted for 
subgroups comprising 11%, 25%, and 50% of the study sample, assuming a two-sided alpha of 0.05. We 
expect suﬃcient power to detect clinically meaningful diﬀerences for most outcomes, with detectable 
RRs ranging from 1.17-1.38. For recall rate, detectable RRs range from 0.92-0.96 with 80% power, and we 
have 99% to detect absolute diﬀerences of 0.57-1.19 percentage points. For secondary outcomes, we 
have 80% power to detect small increases in false positive recalls (absolute diﬀerences <1 percentage 
point; RRs 1.04-1.28).  Power is most limited for interval cancer rate, for which we have 80% power to 
detect a RR of 0.27-0.61. We may have small numbers for speciﬁc woman-level or radiologist-level 
characteristics (e.g., extremely dense breasts occur in <10% of the population). In these cases, we will 
group exams into larger binary categories for the purposes of secondary analyses (e.g., women with the 
2 highest vs. 2 lowest density categories). Analyses of smaller subgroups will be exploratory. 
20. Recruitment 
Strategies for achieving adequate participant enrollment to reach target sample size. 
Our recruitment strategy will ensure a representative sample from participating health systems. We are 
targeting 400,000 screening exams across regional health systems in ﬁve states (WA, CA, MA, FL, WI) 
that provide a range of clinical settings, from academic medical centers to rural community screening 
facilities. Recruitment estimates were generated based on each site's annual screening volumes and 
prior experience in similar studies. Feasibility of conducting the study at these sites was aﬃrmed via 
discussions with site leaders, administrators and clinicians. Participating radiologists expressed 
willingness to participate in implementation of the AI tool. Sites have agreed to participation and 
recruitment targets based on feasibility assessments. 
 

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Methods: assignment of interventions 
Randomization 
21a, 21b. Sequence generation 
21a. Who will generate the random allocation sequence and the method used. 
The Aidoc platform will randomize each exam at the time of image acquisition (i.e., when the 
mammography technologist captures the images), using a random allocation sequence.   
21b. Type of randomization (simple or restricted) and details of any factors for stratiﬁcation. To reduce 
predictability of a random sequence, other details of any planned restriction (e.g., blocking) should be 
provided in a separate document that is unavailable to those who enroll participants or assign 
interventions. 
Exams will be randomized 1:1 at each participating facility using simple randomization.  
22. Allocation concealment mechanism 
Mechanism used to implement the random allocation sequence (e.g., central computer/telephone; 
sequentially numbered, opaque, sealed containers), describing any steps to conceal the sequence until 
interventions are assigned. 
The Aidoc platform will be used to generate the random allocation sequence. Due to the automated 
nature of randomization that occurs immediately after imaging exam acquisition, no one will know in 
advance which patients will be randomized to the AI arm.  
23. Implementation 
Whether the personnel who will enroll and those who will assign participants to the interventions will 
have access to the random allocation sequence. 
Study personnel will not have access to the random allocation sequence. 
24a, 24b, 24c. Blinding 
24a. Who will be blinded after assignment to interventions (e.g., participants, care providers, outcome 
assessors, data analysts). 
Radiology department staﬀ, including schedulers and clinical support staﬀ, and patients will remain 
blinded throughout the trial. Patients will not be informed of the use of AI (per our UCLA IRB) because 
the AI program is an FDA-cleared support tool currently in use within the US. However, radiologists will 
not be blinded to study arm at the time of screening mammography interpretation because they will be 
shown the AI information, if present, as a support tool during their interpretation. Radiology department 
staﬀ, including schedulers and clinical support staﬀ, and patients will remain blinded throughout the 
trial. A strength of our randomization scheme is that we will avoid bias due to potential confounding 
factors (e.g., temporal trends and learning over time). Randomizing AI by exam level will best represent 
actual clinical practice patterns and not disrupt routine workﬂow. 

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24b. If blinded, how blinding will be achieved and description of the similarity of interventions. 
Only the interpreting radiologist will know about assignment to intervention upon interpreting the 
screening mammogram. Whether or not AI was used will not be provided in the mammography report; 
thus, patients and their clinicians will not know to which arm patients were randomized.  
24c. If blinded, circumstances under which unblinding is permissible, and procedure for revealing a 
participant’s allocated intervention during the trial. 
Unblinding is not expected but may occur if the DSMB, who will evaluate interim data reports every 6 
months for participant safety, study conduct and progress, make recommendations concerning trial 
modiﬁcation. 
 

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Methods: data collection, management, and analysis 
25a, 25b. Data collection methods 
25a. Plans for assessment and collection of trial data, including any related processes to promote data 
quality (e.g., duplicate measurements, training of assessors) and a description of trial instruments (e.g., 
questionnaires, laboratory tests), along with their reliability and validity, if known. Reference to where 
data collection forms can be accessed, if not in the protocol. 
We will collect data at the patient, exam, radiologist, and health system level. In addition to data 
collected during the trial enrollment period, a minimum of one year of baseline breast imaging data will 
also be obtained. A separate ﬁle will be created by site study staﬀ that contains the exam ID, randomized 
arm, and AI scores. These ﬁles will be submitted to and linked by the data coordinating center (DCC). 
For the collection of patient-level data, patients complete a questionnaire at most of the imaging 
facilities as part of routine clinical practice at each screening exam. They self-report their demographic 
information and complete an assessment to calculate their level of breast cancer risk. Participating sites 
will access the imaging facility data and EHR data to obtain clinical history and risk factors (e.g., breast 
procedures, breast cancer family history, menopausal status, use of hormone therapy, history of breast 
cancer). We will capture breast cancer diagnoses from multiple sources: local pathology labs, local health 
system cancer registries, and state/regional SEER and North American Association of Central Cancer 
Registries (NAACCR) cancer registry linkages.  
Exam-level data will be collected from the EHR including radiologists’ assessment, recommendations, 
and BI-RADS breast density recorded as part of routine clinical practice using the American College of 
Radiology (ACR) standardized deﬁnitions at each exam. We will also capture information on all diagnostic 
imaging work-up exams and biopsies following the study-entry screening mammogram. For each 
screening, we will collect whether it is a baseline exam (ﬁrst ever) versus subsequent exam and time 
since last mammogram based on self-report information in combination with examinations included in 
the EMR. Mammography manufacturer and model will be collected at the exam level for participating 
facilities where this information is available; otherwise, it will be collected at the facility level.   
Standardized information on radiologists will be collected via a self-report survey prior to interpreting 
mammograms with AI, and again at the end of the RCT. These surveys will gather information on breast 
imaging fellowship training, years of experience, fear of malpractice litigation, tolerance of ambiguity in 
clinical decision-making, and perceptions about AI and its impact on clinical workﬂow eﬃciency.6 We will 
also collect radiologist-level screening volume. 
Facility and health system level data will include academic vs community aﬃliation, imaging machine 
type(s), PACS system(s), payor mix and geographic location. We will also collect precise data on which 
Transpara version and functionality is in use at each site, including dates of implementation. 
Breast cancer outcomes and characteristics will be collected through linkages with Surveillance, 
Epidemiology, and End Results (SEER) registries, state cancer registries, local cancer registries, and 
pathology laboratories.  We will collect data on the following breast cancer characteristics for 
exploratory analyses: histology (e.g., ductal carcinoma in situ [DCIS], invasive ductal carcinoma, invasive 

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lobular carcinoma, inﬂammatory carcinoma); AJCC stage (8th edition, including prognostic and anatomic 
stages); SEER summary stage; lymph node status; tumor size; grade; and hormone receptor status (e.g., 
estrogen receptor, progesterone receptor, HER2/neu).  
25b. Plans to promote participant retention and complete follow-up, including list of any outcome data to 
be collected for participants who discontinue or deviate from intervention protocols. 
Screening mammography exams are one time data points; thus, we do not discuss retention or the 
possibility of patient participants discontinuing.  Follow-up data on all patients will be linked to local and 
regional or state tumor registries to capture breast cancer outcomes.  
26. Data management 
Plans for data entry, coding, security, and storage, including any related processes to promote data 
quality (e.g., double data entry; range checks for data values). Reference to where details of data 
management procedures can be accessed, if not in the protocol. 
The Data Coordinating Center (DCC) at UC Davis will serve as the central hub for data management and 
analysis. The DCC will collect data locally captured at each participating site and maintain it on secure 
computers and servers with safeguards to protect conﬁdentiality and prevent unauthorized access, in 
accordance with UC Davis Health System protocols. 
Data will be collected, managed, and overseen by a data scientist and a statistician from each site (e.g., 
via the CTSI group). Only the data scientist will have access to PHI. Data will be housed in a secure 
storage folder at each site (e.g., overseen by CTSI). All data will be locked until data collection is 
complete.  All non-image data will be de-identiﬁed and sent to UC Davis for analysis. Image data will stay 
at each site in this secure folder for future research and quality assurance purposes. 
Data collection sites will perform their own cancer registry linkages, with support from the DCC as 
needed.  De-identiﬁed exam-level data—including patient characteristics, radiologists’ interpretations, AI 
results, cancer outcomes, and radiologist survey data—will be submitted to the DCC for centralized 
processing, validation, and analysis. The DCC will provide a uniform database format, data dictionary, 
and study manual to ensure consistent standards and deﬁnitions across data collection sites. 
Participating sites will submit to the DCC aggregate enrollment data monthly and exam-level study data 
at least quarterly using REDCap (Research Electronic Data Capture), a secure, web-based software 
platform for managing research and clinical trial data. UC Davis hosts a REDCap instance on servers that 
meet UC Davis Health System security requirements. REDCap oﬀers an intuitive user interface, real-time 
validation rules (e.g., data type and range checks), audit trails, and automated exports to statistical 
software (e.g., SAS, R). The DCC will clean, verify, and pool the data and quality assurance. DCC staﬀ will 
review submissions for completeness, quality, and plausibility, and resolve any data issues. 
Although variables will be uniformly deﬁned, each site will develop its own approach to populating the 
REDCap database based on local information systems. The preferred method will be to export data in a 
standard format using the national mammography database (NMD) export feature available in all 
mammography systems. These data will be supplemented with other study data collected from the EHR 
and surveys. 

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27a, 27b, 27c, 27d. Statistical methods 
27a. Statistical methods used to compare groups for primary and secondary outcomes, including harms. 
All analyses will be pre-speciﬁed in the Statistical Analysis Plan. We will ﬁt linear probability models to 
estimate absolute diﬀerences and modiﬁed Poisson regression models to estimate relative risks 
comparing screening outcomes with vs. without AI.7-9 We have chosen these methods because some 
outcomes are not rare, such as recall rates, rendering odds ratios from logistic regression models diﬃcult 
to interpret. Models will be estimated using a multi-step generalized estimating equation (GEE) approach 
to account for non-nested clustering within patient, radiologist, and facility levels 4,10. We will report 
unadjusted comparisons and, as a sensitivity analysis, comparisons adjusting for temporal trends and 
important patient-, exam-, and radiologist-level factors; however, we do not expect adjustment to 
signiﬁcantly alter our results due to our randomization scheme controlling for these factors. For the non-
inferiority test of recall rate, the null hypothesis is that interpretation with AI is inferior to interpretation 
without AI. We will reject the null hypothesis of inferiority if the upper bound of the 95% confidence 
interval for the absolute difference in recall rates (recall rate with AI minus recall rate without AI) does 
not exceed the non-inferiority margin of 1.5 percentage points. 
Interim analysis: Given concerns that use of AI in mammography could decrease cancer detection rates 
(due to automation bias) and increase recall rates to unacceptable levels, we will conduct a formal 
interim analysis of our two primary outcomes once half of the screens (N=200,000) have been 
randomized using the O’Brian-Fleming approach with an alpha of 0.0054. This analysis will be presented 
to the DSMB in closed session. 
27b. Deﬁnition of who will be included in each analysis (eg, all randomized participants), and in which 
group. 
All randomized exams (i.e., exams interpreted with versus without AI assistance) fulﬁlling the eligibility 
criteria will be included in the analyses. 
27c. How missing data will be handled in the analysis. 
We will minimize the amount of missing data for core study variables through intensive quality 
assurance. The DCC will review data for potential data issues and work with sites to abstract missing 
data, correct data issues, and document when missing data cannot be retrieved. Despite these rigorous 
eﬀorts, we may encounter some missing data, though we expect this to be rare. If <5% of data are 
missing, we will conduct complete case analysis. Otherwise, we will use multiple imputation methods 
and compare these results to complete case analysis. We will report and compare the characteristics of 
screening exams with and without missing data. 
27d. Methods for any additional analyses (eg, subgroup and sensitivity analyses). 
Learning curve: We will evaluate whether there is a “learning curve” as radiologists gain experience 
using the AI tool. We will evaluate changes in screening outcomes as AI use increases over time, 
including an interaction between time and study arm. We will assess whether screening outcomes 
change as a function of volume of exams interpreted with AI using modiﬁed Poisson regression including 
interpreting radiologist as a ﬁxed eﬀect and an interaction between volume and study arm to estimate 
the within-radiologists changes in screening outcomes with increased AI experience over the trial period. 

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We will also assess whether the learning curve diﬀers for breast imaging specialists vs. general 
radiologists or for radiologists with high, average, or low baseline recall rates. If a learning curve is 
identiﬁed, we will adjust our comparative analyses by for volume. 
Heterogeneity of intervention eﬀects: We will evaluate whether the eﬀectiveness of AI depends on 
patient, exam, or radiologist characteristics or on AI version. To contrast diﬀerences in eﬀects by 
subgroups, we will include relevant interaction terms in the regression models and report magnitudes of 
eﬀects and conﬁdence intervals for each subgroup level along with p-values for tests of interaction. We 
will also evaluate whether the eﬀectiveness of AI diﬀers by AI version by including indicator variables for 
AI version in the regression models. We will compare each AI version to the control group and will 
compare the AI versions to each other, adjusting for temporal trends. 
Exploratory analyses: Cancer characteristics will be evaluated by study arm, overall and separately for 
screen-detected and interval cancers. We will compare the distribution of cancer characteristics among 
women diagnosed with cancer (across arms [with vs. without AI]) and calculate rates of cancer 
characteristics per 1,000 screening mammograms among all exams. Cancer characteristics will be 
classiﬁed as having favorable vs. unfavorable prognosis based on deﬁnitions commonly used by the 
Breast Cancer Surveillance Consortium and others.11 For example, stage will be categorized as advanced 
using a previously published deﬁnition based on AJCC prognostic stage II or higher.12 Rates of cancer 
diagnosis with favorable prognosis and rates of cancer diagnosis with unfavorable diagnosis (among all 
screening mammograms) will be separately modeled and compared across study arms using logistic 
regression estimated via a multi-step generalized estimating equation (GEE) approach to account for 
clustering at the patient, radiologist, and facility levels.13 
Eﬃciency Metrics:  We will also assess interpretation and reporting time with AI versus without using 
data from UCLA. We hypothesize that radiologists reading times will be slower during the early 
implementation of AI, until they adapt and optimize their workﬂow when using the AI tool to support 
their interpretation of mammography. We also hypothesize that in the later steady state, radiologists will 
interpret mammograms 20% faster and deliver results to patients 20% faster with AI compared to 
without AI. 
For analyses of eﬃciency metrics, we will analyze time required for radiologists to interpret each 
mammogram with versus without AI, a crucial metric in understanding any enhancement in radiologists' 
eﬃciency. We will also measure delivery time, using time stamp data from exam acquisition to delivery 
of results to patients (aka turnaround time), providing insights into eﬃciency at a health system level 
that are relevant for the longer-term use of AI. 
Eﬃciency metrics power calculations. We expect an average interpretation time of 5 minutes with a 
standard deviation of 10 minutes, as more diﬃcult mammograms can take longer to interpret. With 50% 
of >90,000 mammograms at just the UCLA site randomly assigned to receive AI assistance, we will have 
>99% power to detect a diﬀerence of 18 seconds in the average interpretation time (eﬃciency) of 
mammograms between those with and without AI. Larger observed diﬀerences in mean interpretation 
time will generally yield greater power and regression adjustment should reduce the variance associated 
with our estimates and yield even greater power. 
Our large sample size will allow for well-powered subgroup analyses whereby we can explore the 
efficiency of this technique for cases that are traditionally harder for radiologists to interpret (i.e., dense 
breast tissue). We would have >99% power to detect a difference of 33 seconds in the average 

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interpretation time (efficiency) of mammograms between those with and without AI. Assuming a 
prevalence of 7.4% extremely dense breast tissue, we would have >99% power to detect a difference of 
66 seconds in the average interpretation time (efficiency) between those with and without AI.  These 
power calculations apply to the UCLA site, but these analyses may expand to the other sites.

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Methods: monitoring 
28a, 28b. Data monitoring committee 
28a. Composition of data monitoring committee (DMC); summary of its role and reporting structure; 
statement of whether it is independent from the sponsor and funder; conﬂicts of interest and reference to 
where further details about its charter can be found, if not in the protocol. Alternatively, an explanation 
of why a DMC is not needed. 
Our Data and Safety Monitoring Board (DSMB) will be chaired by Dr. Ruth Carlos, who leads the ECOG-
ACRIN Cancer Care Delivery Research Committee and include 4 other individuals, including experts in 
cancer screening, RCT design, and biostatistics, and one patient partner. The DSMB will evaluate interim 
data reports every 6 months for participant safety, study conduct and progress, and make 
recommendations concerning trial continuation, modiﬁcation, or termination.  
The DSMB is independent from the sponsor and funder.  
Conﬂicts of interest for DSMB members are as follows: 
Dr. Ruth Carlos reports an honorarium from Canon Medical. She also reports board membership and 
leadership at Academy of Radiology and Biomedical Imaging Research, ECOG ACRIN, and Association of 
Academic Radiologists/CERRAF. She receives travel reimbursement from the aforementioned 
organizations due to leadership roles (reimbursed to self). Lastly, she reports salary support paid to 
institution for role as editor in chief of the Journal of the American College of Radiology (JACR). 
Dr. Solveig Hofvind reports no conﬂicts of interest.  
Dr. Robert Smith reports no conﬂicts of interest.  
Dr. Chaya Moskowitz reports that she is a faculty member in the Radiological Society of North America’s 
Clinical Trial Methodology Workshop and receives an honorarium for her participation. She has grants 
from the NIH, paid to her institution, unrelated to this study. 
Ms. Julie Nesbit, a patient representative, reports no conﬂicts of interest. 
The DSM charter will be available from the study PIs upon request. 
28b. Explanation of any interim analyses and stopping guidelines, including who will have access to these 
interim results and make the ﬁnal decision to terminate the trial. 
An interim data analysis will be conducted once half of exams are randomized. Based on these results 
and other available information, the DSMB may advise the PIs to stop the trial if the recall rate in the AI 
arm is signiﬁcantly inferior without evidence of an improvement in the cancer detection rate. The 
Executive Committee will make the ﬁnal decision to terminate the trial. 
29. Trial monitoring 
Frequency and procedures for monitoring trial conduct. If there is no monitoring, give explanation. 

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Data collection sites will upload monthly aggregate counts of exams randomized to the AI and usual care 
arm to a REDcap database developed and maintained by the DCC. Exam-level study data will be 
submitted to the DCC at least quarterly for data quality checks and monitoring.  The DSMB will evaluate 
interim data reports every 6 months for participant safety, study conduct and progress, and make 
recommendations concerning trial continuation, modiﬁcation, or termination. 

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Ethics 
30. Research ethics approval 
Plans for seeking research ethics committee/institutional review board approval. 
We have received Institutional Review Board (IRB) approval from UCLA for a waiver of consent to enroll 
women, link datasets, and perform data analyses. The participating sites will defer to UCLA’s single IRB. 
All procedures are Health Insurance Portability and Accountability Act (HIPAA) compliant, and we will 
protect identities of women, radiologists, and imaging facilities. Patients will not be informed of the use 
of AI, due to the lack of feasibility for this high volume of exams and because the study team will not 
have access to strong patient identiﬁers. Instead, study teams will work with their local informatics group 
to collect and deidentify data (at UCLA, for example, data will be collected and housed by the Clinical and 
Translational Science Institute/CTSI). In addition, the AI program is an FDA-cleared support tool currently 
in use within the US. In clinical settings, patients undergoing medical care and imaging are frequently 
unaware that computer-aided diagnostic tools are being utilized as part of their care, and use of these 
computer tools has become a standard aspect of many diagnostic imaging practices. The AI tool 
functions as an auxiliary support, and the ﬁnal interpretation of the mammogram remains under the 
purview of radiologists, who may choose to consider or disregard the AI’s input.  
31. Protocol amendments 
Plans for communicating important protocol modiﬁcations to relevant parties. 
We will communicate important protocol modiﬁcations to all relevant parties (e.g., sponsor, funder, IRB, 
participating sites) using multiple methods including written email, zoom meetings and telephone calls 
as needed. 
32a, 32b. Consent or assent 
(32a) Who will obtain informed consent or assent from potential trial participants or authorized proxies, 
and how. 
As noted in Section 30 above, we have received IRB approval from UCLA for a waiver of consent to enroll 
women, link datasets, and perform data analyses. 
All eligible screening mammography exams will be included in all analyses except for the sub-analyses 
using radiologists’ characteristics data (which are collected via radiologist surveys); radiologists will 
provide informed consent to have their survey data and professional characteristics used in these sub-
analyses.   This informed consent will be obtained from radiologists at the beginning of the study when 
radiologists receive the baseline survey.  
(32b) Additional consent provisions for collection and use of participant data and biological specimens in 
ancillary studies, if applicable. 
N/A 

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33. Conﬁdentiality 
How personal information about potential and enrolled participants will be collected, shared, and 
maintained in order to protect conﬁdentiality before, during, and after the trial. 
All research team members will abide by standards of conﬁdentiality. No data will be available in reports 
or publications that could lead to the identiﬁcation of individual patients, radiologists, or health systems. 
We will establish and maintain the appropriate administrative, technical, and physical safeguards to 
protect the conﬁdentiality of the data and to prevent unauthorized use or access to it. All data 
management activities will take place using established data management systems with appropriate 
safeguards to protect the conﬁdentiality of the data and to prevent unauthorized use or access to the 
data. All computers storing data will be encrypted. Secured access to computers, databases, and network 
domains is maintained using an isolated EtherNet Local Area Network and strong passwords. The 
Principal Investigators, collaborators, and research staﬀ will not use the data for purposes other than 
described in our proposal. The research team will not use the data to identify any individual patient, 
physician, or facility. 
After trial completion, individual radiologists will receive their interpretive performance metrics with and 
without AI, shared conﬁdentially. No data identifying individual radiologists or patients will be publicly 
posted. 
Multiple communication methods will be used to share study results from the clinical trial with patients 
and the methods may vary by health system site. This communication may be accomplished by patient 
portal e-messages, health system newsletters highlighting study ﬁndings, and dedicated pages on health 
system websites. Some breast imaging facilities and sites might want to have posters on the walls or 
paper handouts describing the ﬁndings. We will also use scientiﬁc dissemination approaches such as 
presentations/posters at national meetings and publications, including our patient partners as co-
authors and co-presenters. The results that are shared will be done in accordance with institution and/or 
state’s guidelines and laws. 
34. Ancillary and post-trial care 
Provisions, if any, for ancillary and posttrial care and for compensation to those who suﬀer harm from 
trial participation. 
Participants will receive screening mammography as part of routine clinical care. The study involves 
minimal risk, as it evaluates the use of Transpara—an FDA-cleared AI decision support tool for screening 
mammography—without altering clinical management pathways. Because the intervention occurs within 
standard care and no investigational procedures are involved, no speciﬁc ancillary or posttrial care 
provisions are necessary. As is standard, participants who experience harm attributable to routine clinical 
care will be managed according to institutional policies; no additional compensation is planned for trial-
related harm given the low-risk nature of the study. 
 
 

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