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.

NCT04270032 | Unknown FDA Device

• 1 other identifier: AI-Breast-US
• Study Type: observational
• Target: 10,000
• Locations: 1 country
• Sites: 1

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.

Conditions

Breast Cancer

Outcome Measures

Primary Outcomes (5)

• 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

Study Arms (3)

malignant group

women with malignant lesions confirmed by pathology

Diagnostic Test: ABUS and HHUS

benign group

women with benign lesions confirmed by pathology or stable in follow-up \> 2 years

Diagnostic Test: ABUS and HHUS

normal group

women have normal images with follow up \> 2 years

Diagnostic Test: ABUS and HHUS

Interventions

ABUS and HHUS DIAGNOSTIC_TEST

Using deep learning method to analyze and extract the features of automated breast ultrasound and hand-held ultrasound images

benign group; malignant group; normal group

Eligibility Criteria

• Age: 18 Years+
• Sex: female
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

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

You may qualify if:

• Female patients over 18 years old who come to the two centers for physical examination or treatment;
• Complete basic information and image data

You may not qualify if:

• 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.

Sponsors & Collaborators

• The First Affiliated Hospital of the Fourth Military Medical University: lead
• Seoul National University Bundang Hospital: collaborator
• Xidian University: collaborator
• Shenzhen University: collaborator

Study Sites (1)

The First Affiliated Hospital of Fourth Military Medical University

Xi'an, Shaanxi, 710000, China

RECRUITING

MeSH Terms

Conditions

Breast Neoplasms

Condition Hierarchy (Ancestors)

Neoplasms by Site; Neoplasms; Breast Diseases; Skin Diseases; Skin and Connective Tissue Diseases

Study Officials

• Hongping Song, MD

  Xijing hospital of The fourth military medical university

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Hongping Song, MD

CONTACT

86 029 84771663; song.hp@foxmail.com

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: OTHER
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator

Study Record Dates

• First Submitted: February 12, 2020
• First Posted: February 17, 2020
• Study Start: February 1, 2020
• Primary Completion: September 1, 2024
• Study Completion: September 1, 2024
• Last Updated: January 27, 2022

Record last verified: 2022-01

Locations

CN (1)