CT-AI Breast Cancer Opportunistic Screening Health Examination Study

NCT07809698 | Not Yet Recruiting DMC

• 1 other identifier: CTAI-BC-IIT-002
• Study Type: interventional
• Target: 30,000
• Locations: 0 countries
• Sites: N/A

Brief 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.

Conditions

Breast Neoplasms

Keywords

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

Outcome Measures

Primary Outcomes (3)

• Incremental Breast Cancer Detection Rate of CT-AI

  Within 3 months after the index health examination

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

  Up to 12 months of retrospective outcome ascertainment

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

  Within 3 months after the index health examination

Secondary Outcomes (6)

• Specificity of CT-AI

  Up to 24 months after the index health examination

• Positive Predictive Value of CT-AI-Assisted Recall

  Within 3 months after the index health examination

• Proportion of Early-Stage Breast Cancers Detected

  Within 3 months after the index health examination

• Physician Recall Rate

  Within 3 months after the index health examination

• Breast Biopsy Rate

  Within 3 months after the index health examination

Study Arms (1)

AI-Assisted Screening Group

EXPERIMENTAL

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

CT-AI-assisted breast cancer screening DEVICE

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.

AI-Assisted Screening Group

Eligibility Criteria

• Age: 18 Years - 80 Years
• Sex: female
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• The First Affiliated Hospital with Nanjing Medical University: lead

Related Publications (4)

• 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.

  PMID: 37541274 RESULT

• 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.

  PMID: 37872026 RESULT

• 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.

  PMID: 34078566 RESULT

• 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.

  PMID: 38572751 RESULT

MeSH Terms

Conditions

Breast Neoplasms

Condition Hierarchy (Ancestors)

Neoplasms by Site; Neoplasms; Breast Diseases; Skin Diseases; Skin and Connective Tissue Diseases

Study Officials

• Qiang Ding

  The First Affiliated Hospital with Nanjing Medical University

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Ge Ma, doctor

CONTACT

+8618262632870; ntmage@sina.com

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: SCREENING
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: September 1, 2026
• First Posted: September 9, 2026
• Study Start: September 1, 2026
• Primary Completion (Estimated): March 31, 2028
• Study Completion (Estimated): February 28, 2030
• Last Updated: September 9, 2026

Record last verified: 2026-08

Data Sharing

• IPD Sharing: Will not share