Ignite Creation Date:
2025-12-24 @ 9:56 PM
Ignite Modification Date:
2026-01-01 @ 8:48 AM
Study NCT ID:
NCT04270032
Status:
UNKNOWN
Last Update Posted:
2022-01-27
First Post:
2020-02-12
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
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.
Sponsor:
The First Affiliated Hospital of the Fourth Military Medical University
Organization:
Study Overview
Official Title:
To Build and Evaluate a Precise Diagnosis, Therapy Assessment and Prognosis Prediction Model of Breast Cancer Based on Artificial Intelligence
Status:
UNKNOWN
Status Verified Date:
2022-01
Last Known Status:
RECRUITING
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
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.
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.
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 Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
True
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
True
Is an FDA AA801 Violation?: