Breast Neoplasm FemaleScreenTrustCADNCT04778670
Artificial Intelligence in Large-scale Breast Cancer Screening
A study testing AI CAD and Radiologist reading in people with Breast Neoplasm Female, run by Karolinska University Hospital at 1 site in Sweden.
Trial summary
This is a prospective clinical trial following a paired screen-positive design, with the aims to assess the performance of an artificial intelligence (AI) computer-aided detection (CAD) algorithm as an independent reader, in addition to two radiologists, of screening mammograms in a true screening population. Read more about this study
- Who is it for?
- Ages 40 to 74 years, women only
- Conditions the study is for
- Breast Neoplasm Female
- What is being tested?
- AI CAD and Radiologist reading
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 55,579 participants
- Where
- 1 siteSweden
- Study dates
- 1 Apr 2021to Dec 2024
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 40 to 74 years
- Women only
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Participants in regular population-based breast cancer screening at Capio St Göran Hospital
Who cannot join
- Incomplete exam (complete exam: mediolateral oblique and craniocaudal images of Left and Right breast)
- Breast implant
- Complete mastectomy (excluded from screening positive group)
- Participant in surveillance program for prior breast cancer
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Artificial Intelligence in a Population-based Breast Cancer Screening - the Prospective Clinical Trial ScreenTrust CAD
This is a prospective clinical trial following a paired screen-positive design, with the aims to assess the performance of an artificial intelligence (AI) computer-aided detection (CAD) algorithm as an independent reader, in addition to two radiologists, of screening mammograms in a true screening population. Since all decisions by individual readers will be recorded, it is possible to determine what the outcome would have been had one or two of the readers not been allowed to assess images, and to determine what the outcome would have been had the recall decision been performed by consensus decision (actual) compared to single reader arbitration of discordant cases.
- Conditions
- Breast Neoplasm Female
- Being tested
- AI CAD, Radiologist reading
- Keywords
- Artificial Intelligence, Breast Cancer, Mammography, Screening
- Medical subject headings
- Breast Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI CADDiagnostic test
Also called Lunit INSIGHT MMG
The Lunit INSIGHT MMG will be used as the AI CAD in our study. Initially, version 1.6.1.1 will be installed. The software version will be continuously updated with subsequent software releases, after confirming in a historic calibration dataset that the performance is improved. The operating point will be set based on a historic calibration dataset to attain a joint sensitivity of breast cancer detection of AI and first reader which is 2% higher than for first and second reader.
Radiologist readingDiagnostic test
Standard of care, each radiologist will assess the mammography examination, making a binary flagging decision (flag the examination to continue to consensus discussion, or not)
| Study group | Role | What they receive |
|---|---|---|
| Standard of Care | Active comparator | Standard of Care means all examinations will receive a flagging decision by: first reader and second reader radiologist as usual. However, in this paired design all participants will belong to both arms. Receives: Diagnostic Test: Radiologist reading |
| AI CAD combination | Experimental | AI CAD combination in the primary end-point means the combination of the flagging decision of the first reader and AI CAD; in the secondary end-points it means any combination of AI alone, or AI in combination with first, second and both readers. Receives: Diagnostic Test: AI CAD; Diagnostic Test: Radiologist reading |
What is being measured
The primary outcome is the main question the study is designed to answer.
Incident breast cancer
Breast cancer diagnosis by pathologist
Measured: At Screening
Incident breast cancer
Breast cancer diagnosis by pathologist
Measured: Within 12 months after screening
Incident breast cancer
Breast cancer diagnosis by pathologist
Measured: Within 23 months after screening
Secondary outcomes (4)
Reader flagging
Radiologist or AICAD assessing the mammograms as suspicious or not suspicious for malignancy
Measured: At screening
Consensus recall
A decision by the consensus discussion to recall the woman for further work-up
Measured: At screening
Tissue sampling
Biopsy or fine needle aspiration performed
Measured: At screening
Process failure
Failure of the AI CAD software to generate AI scores
Measured: At screening
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted3 Mar 2021
- Study start1 Apr 2021
- Primary completion9 Jun 2022
- Study completionDec 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (2)Papers the sponsor has linked to this study.
- Other trials for Breast Neoplasm FemaleStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Non-randomised
- Intervention model
- Single group
- Model description
- This is a prospective clinical trial following a paired screen-positive design (Pepe, Alonzo; 2001), with the aims to assess the performance of an AI algorithm combined with radiologists(s) compared to standard-of-care being two radiologists assessing screening mammograms in a true screening population. Since all decisions by individual readers will be recorded, it is possible to determine what the outcome would have been had one or two of the readers not been allowed to assess images, and to determine what the outcome would have been had the recall decision been performed by consensus decision (actual) compared to single reader arbitration of discordant cases.
- Masking
- Triple blind
- Who is masked
- Participant, Care provider, Investigator
- Enrolment
- 55,579 participants (actual)
Sponsor and investigators
- Lead sponsor
- Karolinska University Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- St Goran's Hospital, Lunit Inc., Karolinska Institutet
- Responsible party
- Principal investigator, Fredrik Strand, Karolinska University Hospital
- Investigators
- Fredrik Strand, MD PhD, Principal investigator, Karolinska University Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04778670
- Sponsor's protocol ID
- STGKS001
- Other IDs
- EPM 2020-00487, K 2020-0807
- Acronym
- ScreenTrustCAD
- Status last verified
- Mar 2023
- First submitted
- 22 Feb 2021
- Last updated
- 14 Mar 2023
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: To the extent allowed by source institution, legal agreements, and applicable laws and regulations
Publications
Dembrower KE, Crippa A, Eklund M, Strand F. Human-AI Interaction in the ScreenTrustCAD Trial: Recall Proportion and Positive Predictive Value Related to Screening Mammograms Flagged by AI CAD versus a Human Reader. Radiology. 2025 Mar;314(3):e242566. doi: 10.1148/radiol.242566.
PubMed 40100021Dembrower K, Crippa A, Colon E, Eklund M, Strand F; ScreenTrustCAD Trial Consortium. Artificial intelligence for breast cancer detection in screening mammography in Sweden: a prospective, population-based, paired-reader, non-inferiority study. Lancet Digit Health. 2023 Oct;5(10):e703-e711. doi: 10.1016/S2589-7500(23)00153-X. Epub 2023 Sep 8.
PubMed 37690911
Study at a glance
From the registry record, last updated by the sponsor on 14 Mar 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence in a Population-based Breast Cancer Screening - the Prospective Clinical Trial ScreenTrust CAD
- ClinicalTrials.gov ID
- NCT04778670
- Acronym / sponsor ID
- ScreenTrustCAD / STGKS001
- Conditions
- Breast Neoplasm Female
- Keywords
- Artificial Intelligence, Breast Cancer, Mammography, Screening
- Study type
- Interventional
- Recruitment status
- Unknown status
- Enrolment
- 55,579 participants (actual)
- Lead sponsor
- Karolinska University HospitalUniversity, hospital or other
- Collaborators
- St Goran's Hospital, Lunit Inc., Karolinska Institutet
- Study start
- 1 Apr 2021 (actual)
- Primary completion
- 9 Jun 2022 (actual)
- Study completion
- Dec 2024 (estimated)
- First posted
- 3 Mar 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
This is a prospective clinical trial following a paired screen-positive design, with the aims to assess the performance of an artificial intelligence (AI) computer-aided detection (CAD) algorithm as an independent reader, in addition to two radiologists, of screening mammograms in a true screening population. Since all decisions by individual readers will be recorded, it is possible to determine what the outcome would have been had one or two of the readers not been allowed to assess images, and to determine what the outcome would have been had the recall decision been performed by consensus decision (actual) compared to single reader arbitration of discordant cases.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Non-randomised
- Intervention model
- Single group
- Masking
- Triple blind (participant, care provider, investigator)
- Number of arms
- 2
- Model description
- This is a prospective clinical trial following a paired screen-positive design (Pepe, Alonzo; 2001), with the aims to assess the performance of an AI algorithm combined with radiologists(s) compared to standard-of-care being two radiologists assessing screening mammograms in a true screening population. Since all decisions by individual readers will be recorded, it is possible to determine what the outcome would have been had one or two of the readers not been allowed to assess images, and to determine what the outcome would have been had the recall decision been performed by consensus decision (actual) compared to single reader arbitration of discordant cases.
| Arm | Type | Receives |
|---|---|---|
| Standard of Care | Active comparator | Diagnostic Test: Radiologist readingStandard of Care means all examinations will receive a flagging decision by: first reader and second reader radiologist as usual. However, in this paired design all participants will belong to both arms. |
| AI CAD combination | Experimental | Diagnostic Test: AI CAD, Diagnostic Test: Radiologist readingAI CAD combination in the primary end-point means the combination of the flagging decision of the first reader and AI CAD; in the secondary end-points it means any combination of AI alone, or AI in combination with first, second and both readers. |
Who may qualify
- Ages
- 40 to 74 years
- Sex
- Female only
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (1 listed)
- Participants in regular population-based breast cancer screening at Capio St Göran Hospital
Key exclusion criteria (4 listed)
- Incomplete exam (complete exam: mediolateral oblique and craniocaudal images of Left and Right breast)
- Breast implant
- Complete mastectomy (excluded from screening positive group)
- Participant in surveillance program for prior breast cancer
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI CADAlso: Lunit INSIGHT MMGThe Lunit INSIGHT MMG will be used as the AI CAD in our study. Initially, version 1.6.1.1 will be installed. The software version will be continuously updated with subsequent software releases, after confirming in a historic calibration dataset that the performance is improved. The operating point will be set based on a historic calibration dataset to attain a joint sensitivity of breast cancer detection of AI and first reader which is 2% higher than for first and second reader. | Diagnostic test | AI CAD combination |
| Radiologist readingStandard of care, each radiologist will assess the mammography examination, making a binary flagging decision (flag the examination to continue to consensus discussion, or not) | Diagnostic test | AI CAD combination, Standard of Care |
Primary outcome
Incident breast cancer
Time frame: At Screening
Breast cancer diagnosis by pathologist
Incident breast cancer
Time frame: Within 12 months after screening
Breast cancer diagnosis by pathologist
Incident breast cancer
Time frame: Within 23 months after screening
Breast cancer diagnosis by pathologist
4 secondary outcomes
Reader flagging
Time frame: At screening
Consensus recall
Time frame: At screening
Tissue sampling
Time frame: At screening
Process failure
Time frame: At screening
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 14 Mar 2023.
- Derived from this study: PubMed 40100021Dembrower KE, Crippa A, Eklund M, Strand F. Human-AI Interaction in the ScreenTrustCAD Trial: Recall Proportion and Positive Predictive Value Related to Screening Mammograms Flagged by AI CAD versus a Human Reader. Radiology. 2025 Mar;314(3):e242566. doi: 10.1148/radiol.242566.
- Derived from this study: PubMed 37690911Dembrower K, Crippa A, Colon E, Eklund M, Strand F; ScreenTrustCAD Trial Consortium. Artificial intelligence for breast cancer detection in screening mammography in Sweden: a prospective, population-based, paired-reader, non-inferiority study. Lancet Digit Health. 2023 Oct;5(10):e703-e711. doi: 10.1016/S2589-7500(23)00153-X. Epub 2023 Sep 8.
- Individual participant dataSharing planned: Yes. To the extent allowed by source institution, legal agreements, and applicable laws and regulations
Related trials for Breast Neoplasm Female
Active, not recruitingPhase 1/2
A Beta-only IL-2 ImmunoTherapY Study
Advanced Solid Tumor, Unresectable Solid Tumor
RecruitingPhase 2
Targeted Therapy Directed by Genetic Testing in Treating Patients With Locally Advanced or Advanced Solid Tumors, The ComboMATCH Screening Trial
Advanced Malignant Solid Neoplasm, Anatomic Stage III Breast Cancer AJCC v8
Recruiting
Mainstreaming Genetics: Evaluation of a Digital Application to Scale and Spread Oncologist-initiated Genetic Testing
Cancer, Breast Cancer
RecruitingObservational study
Real World Treatment Experience of Patients With Breast, Lung, Ovarian, Multiple Myeloma, or Acute Myelogenous Leukemia Using Remote Symptom Monitoring
Breast Cancer, Lung Cancer
RecruitingPhase 2
Administration of Autologous T-Cells Genetically Engineered to Express T-Cell Receptors Reactive Against Neoantigens in People With Metastatic Cancer
Endocrine Tumors, Non-Small Cell Lung Cancer
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Fredrik Strand, MD PhD principal investigator
Karolinska University Hospital
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing AI CAD and Radiologist reading in people with Breast Neoplasm Female, run by Karolinska University Hospital at 1 site in Sweden.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 14 Mar 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Sweden. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Incident breast cancer (time frame: At Screening). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04778670, which also lists 2 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 14 Mar 2023.