Breast CancerNCT04270032
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.
An observational study of Breast Cancer, run by The First Affiliated Hospital of the Fourth Military Medical University at 1 site in China.
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. Read more about this study
- Who is it for?
- Ages 18 years and older, women onlyHealthy volunteers can take part
- Conditions the study is for
- Breast Cancer
- What is being tested?
- ABUS and HHUS
- How many people
- 10,000 participants
- Where
- 1 siteChina
- Study dates
- 1 Feb 2020to 1 Sep 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- Women only
- Healthy volunteers accepted
- Adult, Older adult
Study population: Female patients over 18 years old from two countries (China and Korea).
Who may be able to join
- Female patients over 18 years old who come to the two centers for physical examination or treatment;
- Complete basic information and image data
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
The First Affiliated Hospital of Fourth Military Medical University
Xi'an, Shaanxi, 710000
hongping song, Ph.D, +86-29-84771663, Songhp@fmmu.edu.cn
hongping song
About this study
Official title: 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.
Read the detailed description
- 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.
- Marking ABUS images Three doctors use a semi-automatic method to frame the lesions on the image.
- 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.
- 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.
- 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
- Breast Cancer
- Being tested
- ABUS and HHUS
- Medical subject headings
- Breast Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
ABUS and HHUSDiagnostic test
Using deep learning method to analyze and extract the features of automated breast ultrasound and hand-held ultrasound images
| Study group | Role | What they receive |
|---|---|---|
| malignant group | women with malignant lesions confirmed by pathology Receives: Diagnostic Test: ABUS and HHUS | |
| benign group | women with benign lesions confirmed by pathology or stable in follow-up > 2 years Receives: Diagnostic Test: ABUS and HHUS | |
| normal group | women have normal images with follow up > 2 years Receives: Diagnostic Test: ABUS and HHUS |
What is being measured
The primary outcome is the main question the study is designed to answer.
sensitivity
Proportion of corrected-marked malignant lesions by the model
Measured: 4 years
false-positive per volume
the number of uncorrected-marked malignant lesions by the model
Measured: 4 years
area under curve
area under receiver operating characteristic (ROC) curve in percentage (%)
Measured: 4 years
overall survival(OS) time
It measures the time from the date of cancer diagnosis to any cause of death.
Measured: 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.
Measured: up to 5 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted17 Feb 2020
- Study start1 Feb 2020
- Primary completion1 Sep 2024 (estimated)
- Study completion1 Sep 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Breast CancerStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Other
- Enrolment
- 10,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- The First Affiliated Hospital of the Fourth Military Medical University
- Sponsor type
- University, hospital or other
- Collaborators
- Seoul National University Bundang Hospital, Xidian University, Shenzhen University
- Responsible party
- Principal investigator, Song Hongping, The First Affiliated Hospital of the Fourth Military Medical University
- Investigators
- Hongping Song, MD, Principal investigator, Xijing hospital of The fourth military medical university
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04270032
- Sponsor's protocol ID
- AI-Breast-US
- Status last verified
- Jan 2022
- First submitted
- 12 Feb 2020
- Last updated
- 27 Jan 2022
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- Yes
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 27 Jan 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- To Build and Evaluate a Precise Diagnosis, Therapy Assessment and Prognosis Prediction Model of Breast Cancer Based on Artificial Intelligence
- ClinicalTrials.gov ID
- NCT04270032
- Acronym / sponsor ID
- AI-Breast-US
- Conditions
- Breast Cancer
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 10,000 participants (estimated)
- Lead sponsor
- The First Affiliated Hospital of the Fourth Military Medical UniversityUniversity, hospital or other
- Collaborators
- Seoul National University Bundang Hospital, Xidian University, Shenzhen University
- Study start
- 1 Feb 2020 (actual)
- Primary completion
- 1 Sep 2024 (estimated)
- Study completion
- 1 Sep 2024 (estimated)
- First posted
- 17 Feb 2020 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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
- 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.
- Marking ABUS images Three doctors use a semi-automatic method to frame the lesions on the image.
- 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.
- 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.
- 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 design
- Observational model
- Other
- Time perspective
- Other
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| malignant group | – | Diagnostic Test: ABUS and HHUSwomen with malignant lesions confirmed by pathology |
| benign group | – | Diagnostic Test: ABUS and HHUSwomen with benign lesions confirmed by pathology or stable in follow-up > 2 years |
| normal group | – | Diagnostic Test: ABUS and HHUSwomen have normal images with follow up > 2 years |
Who may qualify
- Ages
- 18 years and older
- Sex
- Female only
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Female patients over 18 years old from two countries (China and Korea).
Key inclusion criteria (2 listed)
- Female patients over 18 years old who come to the two centers for physical examination or treatment;
- Complete basic information and image data
Key exclusion criteria (3 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| ABUS and HHUSUsing deep learning method to analyze and extract the features of automated breast ultrasound and hand-held ultrasound images | Diagnostic test | benign group, malignant group, normal group |
Primary outcome
sensitivity
Time frame: 4 years
Proportion of corrected-marked malignant lesions by the model
false-positive per volume
Time frame: 4 years
the number of uncorrected-marked malignant lesions by the model
area under curve
Time frame: 4 years
area under receiver operating characteristic (ROC) curve in percentage (%)
overall survival(OS) time
Time frame: up to 10 years
It measures the time from the date of cancer diagnosis to any cause of death.
Disease-free survival (DFS) time
Time frame: up to 5 years
The time that the patient is free of the signs and symptoms of a disease after treatment.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 27 Jan 2022.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Hongping Song, MD principal investigator
Xijing hospital of The fourth military medical university
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Breast Cancer, run by The First Affiliated Hospital of the Fourth Military Medical University at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 27 Jan 2022. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Hongping Song, MD (86 029 84771663, song.hp@foxmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
sensitivity (time frame: 4 years). The study lists 5 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 27 Jan 2022.