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Breast NeoplasmsNCT07809698

CT-AI Breast Cancer Opportunistic Screening Health Examination Study

A study testing CT-AI-assisted breast cancer screening in people with Breast Neoplasms, run by The First Affiliated Hospital with Nanjing Medical University.

Not yet recruiting

Opening soon. The study is approved but enrolment has not started yet. Sites may already take enquiries.

Sites and contacts
Official record
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Trial summary

This study evaluates the clinical utility of a locked chest CT artificial intelligence model for opportunistic breast cancer screening among women undergoing health examinations. The study includes a retrospective validation phase and a prospective single-arm implementation phase. Read more about this study

Who is it for?
Ages 18 to 80 years, women onlyHealthy volunteers can take part
Conditions the study is for
Breast Neoplasms
What is being tested?
CT-AI-assisted breast cancer screening
Type of treatment
Medical device
Could I receive a placebo?
No, everyone receives an active treatment
How many people
30,000 participants
Where
No sites listed yet
Study dates
1 Sep 2026to 28 Feb 2030
Phase
Interventional
Are there results?
Not yet: the study is under way

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 18 to 80 years
  • Women only
  • Healthy volunteers accepted
  • Adult, Older adult

Who may be able to join

  • Female participants aged 18 to 80 years.
  • Undergoing routine health examination.
  • For the prospective phase, no clear ongoing breast-related symptoms at baseline.
  • Availability of complete non-contrast chest CT images. In the prospective phase, chest CT must have been scheduled as part of the routine health examination or for another established clinical purpose and must not be performed solely for this study.
  • Availability of a contemporaneous routine breast ultrasound report and relevant clinical information.
  • Chest CT image quality adequate for AI analysis.
  • Availability of an appropriate follow-up pathway through hospital records, pathology systems, cancer registry data, or approved follow-up methods.
  • For the prospective phase, study information has been provided through an ethics-approved process and the participant has not actively opted out.

Who cannot join

  • Previous diagnosis of breast cancer, prior treatment for breast malignancy, or breast malignancy already confirmed before baseline.
  • Pregnancy or breastfeeding.
  • Incomplete breast coverage on chest CT, severe image artifacts, missing images, or other conditions that prevent valid AI analysis.
  • Critical baseline or outcome data are substantially incomplete and cannot reasonably be recovered.
  • No effective follow-up pathway can be established.
  • For the prospective phase, the participant actively opts out before AI analysis or explicitly declines use of imaging and clinical data for this study.

Trial locations and contacts

No sites are listed yet. Contact the study team or a site near you to ask about taking part.

Ge Ma, doctor contact

+8618262632870

ntmage@sina.com

About this study

Official title: AI-Assisted Opportunistic Breast Cancer Screening Using Non-contrast Chest CT in Women Undergoing Health Examination: A Retrospective Validation and Prospective Single-Arm Interventional Study

This study evaluates the clinical utility of a locked chest CT artificial intelligence model for opportunistic breast cancer screening among women undergoing health examinations. The study includes a retrospective validation phase and a prospective single-arm implementation phase. AI analyzes existing non-contrast chest CT images without additional CT examinations. Clinical physicians make further evaluation decisions based on AI outputs, imaging findings, ultrasound results and clinical information.

Read the detailed description

Retrospective phase: Historical health examination data will be used for offline validation of the locked CT-AI model.

Prospective phase: Eligible women undergoing routine health examination will be consecutively enrolled. Participants receive routine chest CT and breast ultrasound. After routine reports are completed and locked, AI analysis is performed. Cases exceeding predefined thresholds are reviewed by clinicians who determine recall decisions. The study evaluates incremental detection value, recall workflow, safety and feasibility.

Being tested
CT-AI-assisted breast cancer screening
Keywords
Artificial Intelligence, Breast Cancer Screening, Opportunistic Screening, Non-contrast Chest CT, Breast Ultrasound, Health Examination
Medical subject headings
Breast Neoplasms

What participants receive

What participants receive. In a randomised study each person is assigned to one group by chance.

  • CT-AI-assisted breast cancer screeningDevice

    A fixed artificial intelligence model is applied to existing non-contrast chest CT images obtained during routine health examinations to identify and localize suspicious breast lesions and generate a breast cancer risk score and risk category. No additional CT examination is performed for study purposes. Participants meeting predefined AI review criteria are evaluated by trained physicians, who review the original CT images together with the AI output and make the final decision regarding whether additional breast evaluation is recommended. The AI system does not independently diagnose breast cancer or automatically recall participants. Subsequent imaging, biopsy, or treatment is determined according to routine clinical practice and participant preference.

Study groups in NCT07809698
Study groupRoleWhat they receive
AI-Assisted Screening GroupExperimentalParticipants undergo routine health examination, including breast ultrasound and non-contrast chest CT when clinically scheduled. No additional chest CT is performed for study purposes. After routine breast ultrasound and chest CT reports are completed and locked, a fixed CT-AI model analyzes the existing chest CT images. Participants meeting predefined AI criteria are reviewed by trained physicians, who make the final decision regarding whether additional breast evaluation is recommended. Further imaging, biopsy, or treatment is determined according to routine clinical practice and participant preference.
Receives: Device: CT-AI-assisted breast cancer screening

What is being measured

The primary outcome is the main question the study is designed to answer.

  1. Incremental Breast Cancer Detection Rate of CT-AI

    Measured: Within 3 months after the index health examination

  2. Sensitivity of CT-AI for Breast Cancer Detection in the Retrospective Cohort

    Measured: Up to 12 months of retrospective outcome ascertainment

  3. Overall Breast Cancer Detection Rate of Combined CT-AI and Breast Ultrasound Screening

    Measured: Within 3 months after the index health examination

Secondary outcomes (6)

  1. Specificity of CT-AI

    Specificity of the fixed CT-AI model for breast cancer detection, using final breast cancer status based on pathology and/or clinical follow-up as the reference standard. Unit of measure: percentage (%).

    Measured: Up to 24 months after the index health examination

  2. Positive Predictive Value of CT-AI-Assisted Recall

    Proportion of participants recalled following CT-AI-assisted physician review who are subsequently confirmed to have breast cancer. Unit of measure: percentage (%).

    Measured: Within 3 months after the index health examination

  3. Proportion of Early-Stage Breast Cancers Detected

    Measured: Within 3 months after the index health examination

  4. Physician Recall Rate

    Proportion of participants for whom the reviewing physician recommends additional breast evaluation after CT-AI-assisted review. Unit of measure: percentage (%).

    Measured: Within 3 months after the index health examination

  5. Breast Biopsy Rate

    Proportion of participants who undergo breast biopsy following the index screening episode. Unit of measure: percentage (%).

    Measured: Within 3 months after the index health examination

  6. 24-Month Interval Breast Cancer Rate

    Number of breast cancers diagnosed during follow-up after the index screening episode among participants without breast cancer detected at the initial screening assessment, reported per 1,000 participants.

    Measured: Up to 24 months after the index health examination

Timeline

From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.

  1. First posted9 Sep 2026
  2. Study start1 Sep 2026 (estimated)
  3. Primary completion31 Mar 2028 (estimated)
  4. Study completion28 Feb 2030 (estimated)

Resources

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
Screening
Allocation
Not applicable
Intervention model
Single group
Masking
None (open label)
Enrolment
30,000 participants (estimated)
IDs, dates and publications
ClinicalTrials.gov ID
NCT07809698
Sponsor's protocol ID
CTAI-BC-IIT-002
Status last verified
Aug 2026
First submitted
1 Sep 2026
Last updated
9 Sep 2026
Results posted
No
Data monitoring committee
Yes
FDA-regulated drug
No
FDA-regulated device
No
Individual data sharing
No

Publications

  • Lang K, Josefsson V, Larsson AM, Larsson S, Hogberg C, Sartor H, Hofvind S, Andersson I, Rosso A. Artificial intelligence-supported screen reading versus standard double reading in the Mammography Screening with Artificial Intelligence trial (MASAI): a clinical safety analysis of a randomised, controlled, non-inferiority, single-blinded, screening accuracy study. Lancet Oncol. 2023 Aug;24(8):936-944. doi: 10.1016/S1470-2045(23)00298-X.

    PubMed 37541274

  • Yasaka K, Sato C, Hirakawa H, Fujita N, Kurokawa M, Watanabe Y, Kubo T, Abe O. Impact of deep learning on radiologists and radiology residents in detecting breast cancer on CT: a cross-vendor test study. Clin Radiol. 2024 Jan;79(1):e41-e47. doi: 10.1016/j.crad.2023.09.022. Epub 2023 Oct 13.

    PubMed 37872026

  • Koh J, Yoon Y, Kim S, Han K, Kim EK. Deep Learning for the Detection of Breast Cancers on Chest Computed Tomography. Clin Breast Cancer. 2022 Jan;22(1):26-31. doi: 10.1016/j.clbc.2021.04.015. Epub 2021 May 5.

    PubMed 34078566

  • Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024 May-Jun;74(3):229-263. doi: 10.3322/caac.21834. Epub 2024 Apr 4.

    PubMed 38572751

Questions about this trial

What is this clinical trial studying?

A study testing CT-AI-assisted breast cancer screening in people with Breast Neoplasms, run by The First Affiliated Hospital with Nanjing Medical University.

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 8 inclusion and 6 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: opening soon, as of the sponsor's last update on 9 Sep 2026. Status can change between updates, so confirm with the study contact before making plans.

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?

This study is still under way. Results are not available in the source record yet.

How can a doctor refer a patient or contact the research team?

Contact Ge Ma, doctor (+8618262632870, ntmage@sina.com), or a site near the patient. AzTrials does not enrol participants or take referrals.

What does the primary outcome measure?

Incremental Breast Cancer Detection Rate of CT-AI (time frame: Within 3 months after the index health examination). 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 NCT07809698, which also lists 4 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 9 Sep 2026.

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