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.

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.

Recruitment Status

UNKNOWN

Total Enrollment

10000 participants

Study Classification

OBSERVATIONAL

Study Start Date

2020-02-01

Study Completion Date

2024-09-01

Brief Summary

Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.

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.

Related Clinical Trials

Explore similar clinical trials based on study characteristics and research focus.

Multi-center Study of Deep Learning AI in Breast Mass

NCT05443672

Breast Neoplasms

UNKNOWN

Using Deep Learning and Radiomics to Diagnose Benign and Malignant Breast Lesions Based on Ultrasound

NCT06069921

Breast Diseases

COMPLETED

Application of Deep-learning and Ultrasound Elastography in Opportunistic Screening of Breast Cancer

NCT03851497

Breast Cancer

COMPLETED

Deep Learning With MRI-based Multimodal-data Fusion Enhanced Postoperative Risk Stratification of Breast Cancer

NCT06546072

Breast Cancer

COMPLETED

Remote Breast Cancer Screening Study

NCT04527510

Breast Cancer Mass Screening Cancer Screening +2 more

ACTIVE_NOT_RECRUITING

Detailed Description

Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.

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.

Conditions

See the medical conditions and disease areas that this research is targeting or investigating.

Breast Cancer

Study Design

Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.

Observational Model Type

OTHER

Study Time Perspective

OTHER

Study Groups

Review each arm or cohort in the study, along with the interventions and objectives associated with them.

malignant group

women with malignant lesions confirmed by pathology

ABUS and HHUS

Intervention Type DIAGNOSTIC_TEST

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

ABUS and HHUS

Intervention Type DIAGNOSTIC_TEST

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

ABUS and HHUS

Intervention Type DIAGNOSTIC_TEST

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

Interventions

Learn about the drugs, procedures, or behavioral strategies being tested and how they are applied within this trial.

ABUS and HHUS

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

Intervention Type DIAGNOSTIC_TEST

Eligibility Criteria

Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.

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.

Minimum Eligible Age

18 Years

Eligible Sex

FEMALE

Accepts Healthy Volunteers

Yes