Breast cancer is one of the most common malignancies in women worldwide, and distant metastasis is the main cause of poor prognosis and death. This project aims to construct a precision prediction system based on multi-omics (digital pathology, immunohistochemistry, proteomics, gene sequencing) and artificial intelligence to predict distant and organ-specific metastasis (bone, lung, liver, brain) in breast cancer, analyze its mechanisms, and provide new solutions for precision medicine.
Female patients pathologically diagnosed with breast cancer at our hospital or participating centers between January 1, 2000, and December 30, 2028.
Must meet one of the following: 1) Initially diagnosed with breast cancer at our hospital, has developed distant metastasis (liver, bone, lung, or brain), and has a biopsy or surgical specimen from the metastatic site stored at our hospital, with complete clinical information available. 2) Primary tumor specimen is not at our hospital, but a biopsy or surgical specimen from a distant metastatic site (liver, bone, lung, brain) is stored at our hospital.
Concomitant metastatic malignancy from another primary cancer.
Incomplete clinical information.
The study population comprises female patients diagnosed with breast cancer between 2000 and 2028, identified through participating hospital centers. This includes patients who have developed distant (organ-specific) metastasis (liver, lung, bone, or brain) and for whom primary and/or metastatic tissue samples are available.
Cohort
Retrospective
Not listed
Metastasis-Free Survival
Time from initial breast cancer diagnosis to the occurrence of distant metastasis.
Performance of Prediction Models
Evaluate the performance of (1) the metastatic risk stratification model, (2) the organ-specific metastasis prediction model, and (3) the metastasis time-window prediction model. Measure: Accuracy and Area Under the Curve (AUC). (Target accuracy ≥80%, AUC ≥0.80)
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