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RecruitingNCT07557654OPBCS-CTUpdated Jun 15, 2026

Opportunistic Breast Cancer Screening Using Non-Contrast Chest CT

An observational study in Breast Cancer, sponsored by Fudan University. Recruiting at 1 site in China. Open to female participants aged 18 Years and older, including healthy volunteers. Per ClinicalTrials.gov, last updated 2026-06-15.

Sponsored by Fudan University · Observational

From the registry’s dates

  • Started May 2026; still recruiting 5 months later.
Study type
Observational
Model
Cohort
Time perspective
Prospective
Enrollment
5,000
Ages
18 Years and older
Sex
Female
01

Study summary

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.

Read the 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.

02

Conditions studied

  • Breast Cancer

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Keywords

  • Breast Cancer
  • Opportunistic Screening
  • Chest Computed Tomography
  • Mammography
  • Breast Magnetic Resonance Imaging
  • Artificial Intelligence
03

In context

Breast Neoplasms

12,544 studies on the registry are indexed under Breast Neoplasms; 2,892 are open to participants now.

This study's planned enrollment of 5,000 is above the median of 184 across 2,642 observational studies indexed under Breast Neoplasms.

Browse Breast Neoplasms studies →

Lead sponsor

Fudan University is the lead sponsor of 1,270 studies on the registry; 623 are open to participants now.

Counted across the registry records on this site, refreshed daily.

04

Who can participate

Ages eligible
18 Years and older
Sexes eligible
Female
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.

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

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.
05

Study design

Observational model
Cohort
Time perspective
Prospective
Enrollment
5,000 participants (estimated)
Target follow-up
2 Years
Patient registry
Yes
06

What researchers measure

Primary outcomes

  1. 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.

    Time frame: Up to 12 months

Secondary outcomes

  1. 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.

    Time frame: Up to 12 months

07

Study locations

1 of 1 sites recruiting
  • Fudan University Shanghai Cancer Center
    Shanghai, 200032, China
    Recruiting
08

References and documents

Individual participant data

Plan to share: No

No publications or documents are linked to this record.

09

Updates

Tracking since Sep 25, 2026
No changes since tracking began. The registry record was last updated on Jun 15, 2026, before this site started recording changes on Sep 25, 2026. Its history is on ClinicalTrials.gov ↗
10

Registry details

Key details

Study ID
NCT07557654
Lead sponsor
Fudan University
Responsible party
Yajia Gu, MD (Professor, Department of Radiology, Fudan University Shanghai Cancer Center, Fudan University) — Principal investigator
First posted
Apr 29, 2026
Start date
May 6, 2026
Primary completion
May 1, 2028 (estimated)
Completion
May 1, 2029 (estimated)
Last update
Jun 15, 2026

Study contacts

Chao You, MD
Contact
youchao@fudan.edu.cn
+86-021-15800780035
Yajia Gu, MD
Contact
guyajia@fudan.edu.cn
+86-021-18017312040
Yajia Gu, MD
principal investigator · Fudan University
Chao You, MD
principal investigator · Fudan University

Oversight

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

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