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.
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
5,000
Fudan University Shanghai Cancer Center
Shanghai, China
RECRUITINGScreening 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
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
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