Artificial Intelligence Model-Assisted Accurate Diagnosis of Early-Stage Breast Cancer

Army Medical University of People's Liberation Army

Status: Not yet enrolling

Conditions: Artifical Intelligence; Breast Cancer, Metastatic

Treatments: Other: bulid primary AI model; Other: verdict model and develop its function

Study type: Observational

Funder types: Other

Identifiers

NCT07063667

Ratification NO: 2025（188）

Details and patient eligibility

About

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.

Enrollment: 900 estimated patients

Sex: All

Ages: 19 to 85 years old

Volunteers: No Healthy Volunteers

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

Trial design

900 participants in 2 patient groups

training group

Treatment:

Other: bulid primary AI model

verdict group

Treatment:

Other: verdict model and develop its function