Overview
The purpose of this study is using a deep learning method to analyze the automated breast ultrasound (ABUS) imagings, establish and evaluate a diagnosis, therapy assessment and prognosis prediction model of breast cancer. The model would provide important references for further early prevention, early diagnosis and personalized treatment.
Description
- Establishing a database By collecting ABUS and comprehensive breast image data, essential information, clinical treatment information, prognosis, and curative effect information, a complete breast image database is constructed.
- Marking ABUS image Three doctors use a semi-automatic method to frame the lesions on the image.
- Building the model Using the deep learning method to preprocess, analyze and train the marked image, and finally get a model diagnosis, efficacy evaluation and prognosis prediction model of breast cancer.
- Evaluating the model 1Self-validation Analyze the sensitivity, AUC of the breast cancer diagnosis model and the false-positive number on each ABUS volume.
- Compared the sensitivity, AUC and the false-positive number with a commercial diagnosis model.
3)By analyzing the size and characteristics of the lesions after neoadjuvant chemotherapy, and predicting the OS and DFS time, the therapy assessment and prognosis prediction model were evaluated.
Eligibility
Inclusion Criteria:
- Female patients over 18 years old who come to the two centers for physical examination or treatment;
- Complete basic information and image data
Exclusion Criteria:
- There is no complete ABUS image data;
- The image quality of ABUS image is poor;
- In multifocal breast cancer, the correlation between the tumor in the image and the postoperative pathological examination is uncertain.