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

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT04270032
• Other study ID #: AI-Breast-US
• Status: Recruiting
• Start date: February 1, 2020
• Est. completion date: September 1, 2024

Study information

• Verified date: January 2022
• Source: The First Affiliated Hospital of the Fourth Military Medical University
• Contact: Hongping Song, MD
• Phone: 86 029 84771663
• Email: song.hp[@]foxmail.com
• Is FDA regulated: No
• Study type: Observational

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.

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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 10000
• Est. completion date: September 1, 2024
• Est. primary completion date: September 1, 2024
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: Female
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

1. Female patients over 18 years old who come to the two centers for physical examination or treatment;

2. Complete basic information and image data

Exclusion Criteria:

1. There is no complete ABUS and HHUS images data;

2. The image quality is poor;

3. In multifocal breast cancer, the correlation between the tumor in the image and the postoperative pathological examination is uncertain.

Related Conditions & MeSH terms

• Breast Cancer
• Breast Neoplasms

Intervention

Diagnostic Test:
• ABUS and HHUS
Using deep learning method to analyze and extract the features of automated breast ultrasound and hand-held ultrasound images

Locations

Country	Name	City	State
China	The First Affiliated Hospital of Fourth Military Medical University	Xi'an	Shaanxi

Sponsors (4)

Lead Sponsor	Collaborator
The First Affiliated Hospital of the Fourth Military Medical University	Seoul National University Bundang Hospital, Shenzhen University, Xidian University

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	sensitivity	Proportion of corrected-marked malignant lesions by the model	4 years
Primary	false-positive per volume	the number of uncorrected-marked malignant lesions by the model	4 years
Primary	area under curve	area under receiver operating characteristic (ROC) curve in percentage (%)	4 years
Primary	overall survival(OS) time	It measures the time from the date of cancer diagnosis to any cause of death.	up to 10 years
Primary	Disease-free survival (DFS) time	The time that the patient is free of the signs and symptoms of a disease after treatment.	up to 5 years