To Build and Evaluate a Precise Diagnosis, Therapy Assessment and Prognosis Prediction Model of Breast Cancer Based on Artificial Intelligence
Overview
- Phase
- Not Applicable
- Intervention
- Not specified
- Conditions
- Breast Cancer
- Sponsor
- The First Affiliated Hospital of the Fourth Military Medical University
- Enrollment
- 10000
- Locations
- 1
- Primary Endpoint
- sensitivity
- Status
- Recruiting
- Last Updated
- 4 years ago
Overview
Brief Summary
The purpose of this study is using a deep learning method to analyze the automated breast ultrasound (ABUS) and hand-held ultrasound(HHUS) images, 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.
Detailed Description
1. Establishing a database By collecting ABUS, HHUS and comprehensive breast images data, essential information, clinical treatment information, prognosis, and curative effect information, a complete breast image database is constructed. 2. Marking ABUS images Three doctors use a semi-automatic method to frame the lesions on the image. 3. Building the model Using the deep learning method to preprocess, analyze and train the marked images, and finally get a model diagnosis, efficacy evaluation and prognosis prediction model of breast cancer. 4. Evaluating the model 1）Self-validation： Analyze the sensitivity, AUC of the breast cancer diagnosis model and the false-positive number on each ABUS volume. 2) Compared the sensitivity, AUC and the false-positive number with a commercial diagnosis model. 3)To test the screening and diagnostic efficacy of computer-aided diagnosis systems through prospective or retrospective studies. 4)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.
Investigators
Song Hongping
Principal Investigator
The First Affiliated Hospital of the Fourth Military Medical University
Eligibility Criteria
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 and HHUS images data;
- •The image quality is poor;
- •In multifocal breast cancer, the correlation between the tumor in the image and the postoperative pathological examination is uncertain.
Outcomes
Primary Outcomes
sensitivity
Time Frame: 4 years
Proportion of corrected-marked malignant lesions by the model
area under curve
Time Frame: 4 years
area under receiver operating characteristic (ROC) curve in percentage (%)
false-positive per volume
Time Frame: 4 years
the number of uncorrected-marked malignant lesions by the model
overall survival(OS) time
Time Frame: up to 10 years
It measures the time from the date of cancer diagnosis to any cause of death.
Disease-free survival (DFS) time
Time Frame: up to 5 years
The time that the patient is free of the signs and symptoms of a disease after treatment.