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Not yet recruitingNCT07809698Updated Sep 9, 2026

CT-AI Breast Cancer Opportunistic Screening Health Examination Study

An interventional study of CT-AI-assisted breast cancer screening in Breast Neoplasms, sponsored by The First Affiliated Hospital with Nanjing Medical University. Not yet recruiting. Open to female participants aged 18 Years to 80 Years, including healthy volunteers. Per ClinicalTrials.gov, last updated 2026-09-09.

Sponsored by The First Affiliated Hospital with Nanjing Medical University · Not applicable, Interventional, and Screening

Phase
Not applicable
Study type
Interventional
Enrollment
30,000
Allocation
Not applicable
Ages
18 Years to 80 Years
Sex
Female
01

Study 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. 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.

02

Conditions studied

  • Breast Neoplasms

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Keywords

  • Artificial Intelligence
  • Breast Cancer Screening
  • Opportunistic Screening
  • Non-contrast Chest CT
  • Breast Ultrasound
  • Health Examination
03

Who can participate

Ages eligible
18 Years to 80 Years
Sexes eligible
Female
Accepts healthy volunteers
Yes

Inclusion criteria

  • 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.

Exclusion criteria

Exclusion Criteria:

  • 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.
04

Study design

Phase
Not applicable
Primary purpose
Screening
Allocation
Not applicable
Intervention model
Single group
Masking
None (open label)
Enrollment
30,000 participants (estimated)

Study arms

  • Experimental
    AI-Assisted Screening Group

    Participants 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.

    Device: CT-AI-assisted breast cancer screening

Interventions

  • DeviceCT-AI-assisted breast cancer screening

    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.

05

What researchers measure

Primary outcomes

  1. Incremental Breast Cancer Detection Rate of CT-AI

    Time frame: Within 3 months after the index health examination

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

    Time frame: Up to 12 months of retrospective outcome ascertainment

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

    Time frame: Within 3 months after the index health examination

Secondary outcomes

  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 (%).

    Time frame: 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 (%).

    Time frame: Within 3 months after the index health examination

  3. Proportion of Early-Stage Breast Cancers Detected

    Time frame: 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 (%).

    Time frame: 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 (%).

    Time frame: 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.

    Time frame: Up to 24 months after the index health examination

06

Study locations

No study locations are listed for this record.

07

References and documents

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 ↗

Individual participant data

Plan to share: No

08

Registry details

Key details

Study ID
NCT07809698
Lead sponsor
The First Affiliated Hospital with Nanjing Medical University
Responsible party
Sponsor
First posted
Sep 9, 2026
Start date
Sep 1, 2026 (estimated)
Primary completion
Mar 31, 2028 (estimated)
Completion
Feb 28, 2030 (estimated)
Last update
Sep 9, 2026

Study contacts

Ge Ma, doctor
Contact
ntmage@sina.com
+8618262632870
Qiang Ding
principal investigator · The First Affiliated Hospital with Nanjing Medical University

Oversight

Data monitoring committee
Yes
FDA-regulated drug
No
FDA-regulated device
No
View the source record on ClinicalTrials.gov ↗

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This study is not yet recruiting, as verified in Aug 2026. You cannot join it, but the record below documents what was studied.

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