Breast Cancer
Conditions
Keywords
Breast Cancer, Opportunistic Screening, Chest Computed Tomography, Mammography, Breast Magnetic Resonance Imaging, Artificial Intelligence
Brief 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.
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.
Interventions
None listed
Sponsors
Fudan University
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
FEMALE
Age
18 Years to No maximum
Healthy volunteers
Yes
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Screening performance of non-contrast chest CT for detection of breast cancer, with comparison to mammography and/or breast MRI | Up to 12 months | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Performance of non-contrast chest CT for histological classification of breast cancer, with comparison to mammography and/or breast MRI | Up to 12 months | 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. |
Countries
China
Contacts
CONTACTChao You, MD
CONTACTYajia Gu, MD
PRINCIPAL_INVESTIGATORYajia Gu, MD
Fudan University
PRINCIPAL_INVESTIGATORChao You, MD
Fudan University
Outcome results
None listed