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.
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.
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.
Single group
CT-AI-assisted breast cancer screening
DeviceA 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.
Incremental Breast Cancer Detection Rate of CT-AI
Sensitivity of CT-AI for Breast Cancer Detection in the Retrospective Cohort
Overall Breast Cancer Detection Rate of Combined CT-AI and Breast Ultrasound Screening
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 (%).
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 (%).
Proportion of Early-Stage Breast Cancers Detected
Physician Recall Rate
Proportion of participants for whom the reviewing physician recommends additional breast evaluation after CT-AI-assisted review. Unit of measure: percentage (%).
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