Using Deep Learning Methods to Analyze Automated Breast Ultrasound and Hand-held Ultrasound Images, to Establish a Diagnosis, Therapy Assessment and Prognosis Prediction Model of Breast Cancer.

Breast Cancer Clinical Trial

Official title: To Build and Evaluate a Precise Diagnosis, Therapy Assessment and Prognosis Prediction Model of Breast Cancer Based on Artificial Intelligence

Status Recruiting

Clinical Trial 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.

Clinical Trial 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. ;

Related Conditions & MeSH terms

• Breast Cancer
• Breast Neoplasms

• NCT number: NCT04270032
• Study type: Observational
• Source: The First Affiliated Hospital of the Fourth Military Medical University
• Contact: Hongping Song, MD
• Phone: 86 029 84771663
• Email: song.hp@foxmail.com
• Status: Recruiting
• Start date: February 1, 2020
• Completion date: September 1, 2024