Opportunistic Breast Cancer Screening Using Non-Contrast Chest CT (OPBCS-CT)

Artificial Intelligence-Assisted Opportunistic Screening for Breast Cancer Using Non-Contrast Chest CT: A Comparative Study With Mammography and/or Breast MRI

The goal of this observational study is to evaluate the feasibility and effectiveness of using non-contrast chest computed tomography scans for opportunistic breast cancer screening, and to further compare its diagnostic performance with conventional imaging modalities, including mammography and/or breast magnetic resonance imaging.

Study Overview

• Status: Not yet recruiting
• Conditions: Breast Cancer

Detailed Description

Breast cancer is one of the most common malignancies in women, and early detection is essential for improving clinical outcomes. While dedicated breast imaging modalities, including mammography and breast MRI, are widely used for screening. Many women undergo non-contrast chest CT scans for other clinical indications, providing a potential opportunity for breast evaluation. This observational study aims to investigate the clinical value of non-contrast chest CT scans as an opportunistic screening tool for breast cancer. Breast tissue visible on routine CT scans will be assessed using artificial intelligence-based methods to identify suspicious lesions. The primary objective is to evaluate the diagnostic performance of non-contrast chest CT in detecting breast cancer, including sensitivity, specificity, and accuracy, and to further compare its diagnostic performance with mammography and breast MRI. The findings are expected to determine whether non-contrast chest CT can serve as an opportunistic tool for early breast cancer detection without additional imaging burden, and to clarify its relative clinical value compared with established breast imaging techniques.

• Study Type: Observational
• Enrollment (Estimated): 5000

Contacts and Locations

• Study Contact: Name: Chao You, MD; Phone Number: +86-021-15800780035; Email: youchao@fudan.edu.cn
• Study Contact Backup: Name: Yajia Gu, MD; Phone Number: +86-021-18017312040; Email: guyajia@fudan.edu.cn

Study Locations

• China
  • Shanghai, China, 200032
    • Fudan University Shanghai Cancer Center

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: Yes

• Sampling Method: Non-Probability Sample

Study Population

The study population consists of female patients undergoing non-contrast chest CT scans for any clinical indication at participating institutions, with or without breast lesions detected on CT. For participants included in the comparative analysis, at least one additional breast imaging modality (mammography and/or breast MRI) must have been performed within three months of the non-contrast chest CT examination and without any intervening clinical events.

Description

Inclusion Criteria:

1. Female patients who have undergone non-contrast chest CT examination;
2. Participants included in the comparative analysis must have undergone at least one comparator imaging modality (mammography and/or breast MRI);
3. If a breast lesion is detected, it must be confirmed by pathology or clinical follow-up at least 12 months;
4. No prior systemic or local therapy before imaging examinations;
5. Imaging data are complete and of sufficient quality for analysis.

Exclusion Criteria:

1. History of other malignancies with potential impact on breast imaging interpretation;
2. Participants with suspected malignant breast lesions but no confirmation;
3. Prior radiotherapy, chemotherapy, or immunotherapy before imaging examinations;
4. Imaging data that are incomplete, of poor quality, or contain significant artifacts preventing reliable analysis;
5. Time interval between non-contrast chest CT and comparator imaging modalities exceeding three months, or with clinical events occurring between examinations that may alter lesion status.

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Screening performance of non-contrast chest CT for detection of breast cancer, with comparison to mammography and/or breast MRI	The primary outcome is the screening performance of AI-assisted analysis for the detection of breast cancer on non-contrast chest CT. The detection process is conducted in a stepwise approach, involving lesion identification followed by classification into benign or malignant categories for breast cancer detection. Performance metrics include sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and area under the receiver operating characteristic curve. Participants included in the comparative analysis must have undergone at least one comparator imaging modality (mammography and/or breast MRI) within three months of the chest CT examination, with no intervening clinical events. Performance metrics will be further compared with those obtained from mammography and/or breast MRI within the same participants to evaluate the relative screening performance.	Up to 12 months

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Performance of non-contrast chest CT for histological classification of breast cancer, with comparison to mammography and/or breast MRI	The secondary outcome is the performance of AI-assisted analysis for classifying the histological types of breast cancer on non-contrast chest CT. Histological types are defined according to the World Health Organization classification system based on histopathological examination. Performance metrics include sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and area under the receiver operating characteristic curve. Participants included in the comparative analysis must have undergone at least one comparator imaging modality (mammography and/or breast MRI) within three months of the chest CT examination, with no intervening clinical events. Performance metrics will be further compared with those obtained from mammography and/or breast MRI within the same participants to evaluate the relative classification performance.	Up to 12 months

Collaborators and Investigators

• Sponsor: Fudan University
• Investigators: Principal Investigator: Yajia Gu, MD, Fudan University; Principal Investigator: Chao You, MD, Fudan University

Study record dates

Study Major Dates

• Study Start (Estimated): May 1, 2026
• Primary Completion (Estimated): May 1, 2028
• Study Completion (Estimated): May 1, 2029

Study Registration Dates

• First Submitted: April 15, 2026
• First Submitted That Met QC Criteria: April 25, 2026
• First Posted (Actual): April 29, 2026

Study Record Updates

• Last Update Posted (Actual): April 29, 2026
• Last Update Submitted That Met QC Criteria: April 25, 2026
• Last Verified: April 1, 2026

More Information

Terms related to this study

• Keywords: Breast Cancer; Artificial Intelligence; Mammography; Opportunistic Screening; Chest Computed Tomography; Breast Magnetic Resonance Imaging
• Additional Relevant MeSH Terms: Neoplasms by Site; Neoplasms; Skin Diseases; Breast Diseases; Skin and Connective Tissue Diseases; Breast Neoplasms
• Other Study ID Numbers: FDBC-2026

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No