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.

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

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.

Study Overview

• Status: Recruiting
• Conditions: Breast Cancer
• Intervention / Treatment: Diagnostic test: ABUS and HHUS

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.

• Study Type: Observational
• Enrollment (Anticipated): 10000

Contacts and Locations

Study Locations

• China
  • Shaanxi
    • Xi'an, Shaanxi, China, 710000
      • Recruiting
      • The First Affiliated Hospital of Fourth Military Medical University

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (ADULT, OLDER_ADULT)
• Accepts Healthy Volunteers: Yes
• Genders Eligible for Study: Female

• Sampling Method: Probability Sample

Study Population

Female patients over 18 years old from two countries (China and Korea).

Description

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.

Study Plan

How is the study designed?

Number of groups / cohorts

3

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
malignant group; women with malignant lesions confirmed by pathology	Diagnostic test: ABUS and HHUS; Using deep learning method to analyze and extract the features of automated breast ultrasound and hand-held ultrasound images
benign group; women with benign lesions confirmed by pathology or stable in follow-up > 2 years	Diagnostic test: ABUS and HHUS; Using deep learning method to analyze and extract the features of automated breast ultrasound and hand-held ultrasound images
normal group; women have normal images with follow up > 2 years	Diagnostic test: ABUS and HHUS; Using deep learning method to analyze and extract the features of automated breast ultrasound and hand-held ultrasound images

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
sensitivity	Proportion of corrected-marked malignant lesions by the model	4 years
false-positive per volume	the number of uncorrected-marked malignant lesions by the model	4 years
area under curve	area under receiver operating characteristic (ROC) curve in percentage (%)	4 years
overall survival(OS) time	It measures the time from the date of cancer diagnosis to any cause of death.	up to 10 years
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

Collaborators and Investigators

• Sponsor: The First Affiliated Hospital of the Fourth Military Medical University
• Collaborators: Seoul National University Bundang Hospital; Xidian University; Shenzhen University

Study record dates

Study Major Dates

• Study Start (ACTUAL): February 1, 2020
• Primary Completion (ANTICIPATED): September 1, 2024
• Study Completion (ANTICIPATED): September 1, 2024

Study Registration Dates

• First Submitted: February 12, 2020
• First Submitted That Met QC Criteria: February 13, 2020
• First Posted (ACTUAL): February 17, 2020

Study Record Updates

• Last Update Posted (ACTUAL): January 27, 2022
• Last Update Submitted That Met QC Criteria: January 12, 2022
• Last Verified: January 1, 2022

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Skin Diseases; Neoplasms; Neoplasms by Site; Breast Diseases; Breast Neoplasms
• Other Study ID Numbers: AI-Breast-US

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: Yes
• product manufactured in and exported from the U.S.: Yes