Retrospectively collect the clinical data, breast MRI images, breast ultrasound images and reports, laboratory indicators (such as CA199, CA153, CA125, CEA/AFP), pathological diagnosis results, HE staining images, and existing immunohistochemical results (including CD8A, KPT5, GFRA1, PFKP, ER/PR percentage, Her-2 expression, Ki-67 index, etc.) of patients pathologically confirmed with or excluded from breast cancer in our center between January 2019 and December 2024. For biopsy specimens from patients diagnosed with breast cancer and immunohistochemically confirmed as HR+/Her-2+ during the same period, additional immunohistochemical staining for CD8A, KPT5, GFRA1, and PFKP should be performed, with images and results collected. The collected basic clinical information, imaging data, pathological findings, and laboratory metrics of patients will serve as candidate inputs. Units of measurement will be standardized, and missing data will be imputed using the multiple imputation by chained equations algorithm. Data harmonization will employ the Box-Cox algorithm, while min-max scaling will be used for standardization. The adaptive synthetic sampling method with a balance ratio of 0.5 will address data imbalance. For the collected patient data, deep learning will be applied to screen features from the images, combined with clinical significance to identify malignant risk factors. A neural network classifier will be trained on the training set data, with independent variables including breast MRI/ultrasound images, CA199, CA153, CA125, AFP/CEA, etc., and dependent variables including breast cancer status and subtype. Pathological biopsy results will be set as the validation standard. Model tuning will be conducted on the validation set to construct a breast cancer prediction model. It should be noted that as a single-center study, the results have limited generalizability. The further optimization and evaluation plan for the model involves using breast disease screening data from external centers for validation and refinement, evaluating the model's practical impact on clinical decision-making, and continuously tracking and optimizing its performance.
Age range
19 Years – 85 Years
Sex
ALL
See this in plain English?
AI-rewrites the medical criteria so a patient or caregiver can understand them. Always confirm with the trial site.
Bring these to your next appointment. They're a starting point for a shared conversation — not a sign you qualify or a recommendation to enrol.
Generated to help you prepare — always confirm anything about your own eligibility and care with the study team and your doctor.
The trial coordinator is the person who runs the study day to day. These cover the practical side — logistics, costs, and what taking part would actually mean for your life. The study team confirms whether you meet the criteria; these are questions to ask, not a sign you qualify.
A starting point for the conversation — always confirm anything about your own eligibility, costs, and care with the study team and your doctor.
AUC (Area Under the ROC Curve)
Timeframe: Baseline-AUC1 Perioperative/Periprocedural-AUC2