Artifical Intelligence, Breast Cancer, Metastatic
Conditions
Brief summary
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.
Interventions
OTHERbulid primary AI model
For the collected patient data, deep learning is used to perform feature screening on the selected or collected images, and malignant risk factors are determined by combining clinical significance. A neural network classifier is trained on the training set data. Variable selection: independent variables (breast MRI images, breast ultrasound images, indicators such as CA199, CA153, CA125, AFP/CEA, etc.), dependent variables (whether suffering from breast cancer and breast cancer subtypes), and the verification accuracy standard is set as the pathological biopsy result.
OTHERverdict model and develop its function
The accuracy of a breast cancer prediction model is typically evaluated using multiple metrics that assess its performance in different aspects
Sponsors
Daping Hospital and the Research Institute of Surgery of the Third Military Medical University
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
19 Years to 85 Years
Healthy volunteers
No
Inclusion criteria
* Patients pathologically diagnosed with breast cancer or excluded from breast cancer * Available pathological results of breast masses * Involving diagnostic population onl
Exclusion criteria
* Suffering from mental disorders * Presence of non-breast diseases during examination * Presence of breast implants * Undergoing non-breast surgery or having received radiotherapy/chemotherapy * Lactating or pregnant women * Missing data
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| AUC (Area Under the ROC Curve) | Baseline-AUC1 Perioperative/Periprocedural-AUC2 |
Countries
China
Contacts
Primary ContactXu Yan
Outcome results
None listed