Clinical trial · Interventional

CT-AI Breast Cancer Opportunistic Screening Health Examination Study

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

NCT07809698CI-TRIAL-00126181not yet recruitingN/AClinicalTrials.gov clinicaltrialsPublishedExplore in graph →

Summary

Brief summary (as posted)

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

Conditions (1)

Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.

Condition (as posted)	Mapped entity	Match	Confidence
Breast Neoplasms	Breast Neoplasm	ONTOLOGY_EXACT	0.98

Interventions

Interventions (1)

Intervention	Type	Mapped drug	Match
CT-AI-assisted breast cancer screening	Device	—	UNRESOLVED

Design

Arms and outcomes

Arms (1)

type
EXPERIMENTAL
label
AI-Assisted Screening Group
description
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.
interventionNames
1. Device: CT-AI-assisted breast cancer screening

Primary outcomes (3)

measure
Incremental Breast Cancer Detection Rate of CT-AI
timeFrame
Within 3 months after the index health examination
measure
Sensitivity of CT-AI for Breast Cancer Detection in the Retrospective Cohort
timeFrame

Eligibility

Eligibility (as posted)

Sex: Female
Minimum age: 18 Years
Maximum age: 80 Years

Show eligibility criteria text

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:

* 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.

References

Publications (4)

RESULTLang 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
RESULTYasaka 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
RESULTKoh 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
RESULTBray 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