Breast NeoplasmsNCT05443672
Multi-center Study of Deep Learning AI in Breast Mass
An observational study of Breast Neoplasms, run by Cancer Institute and Hospital, Chinese Academy of Medical Sciences at 1 site in China.
Trial summary
This multi-center study intends to evaluate the value of the detection and differential diagnosis of breast mass using deep learning AI-based real-time ultrasound examination. Read more about this study
- Who is it for?
- Any age, women only
- Conditions the study is for
- Breast Neoplasms
- What is being tested?
- Yizhun BUSMS
- Type of treatment
- Medical device
- How many people
- 1,122 participants
- Where
- 1 siteChina
- Study dates
- 12 Aug 2021to 31 Aug 2023
- 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 Any age
- Women only
- No healthy volunteers
- Child, Adult, Older adult
Study population: Femal patients with breast neoplasm
Who may be able to join
- Females who undergo ultrasound examination for a complaint of breast lesion;
- The breast lesion that will obtain definite pathological diagnosis or follow-up at least two years.
Who cannot join
- The breast lesion that has received CNB or FNA;
- The breast cancer patient who has received neoadjuvant chemotherapy.
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College
Beijing, Beijing Municipality, 100021
Yong Wang, 13391817899, drwangyong77@163.com
About this study
Official title: A Multi-center Study of Breast Mass Screening and Diagnosis Using Deep Learning AI-based on Real-time Ultrasound Examination
This multi-center study intends to evaluate the value of the detection and differential diagnosis of breast mass using deep learning AI-based real-time ultrasound examination.
Read the detailed description
As the most common cancer expected to occur all over the world, extensive population screening plays a very important role in the early diagnosis and prognosis of the breast cancer. X-ray and ultrasound are the most commonly used screening methods, and ultrasound is especially important for Asian women with dense breasts. However, ultrasound is greatly affected by the operator's skill and experience, and the diagnostic accuracy varies greatly.
Artificial intelligence (AI) is a new method emerging in recent years, active in many medical fields and can effectively improve the diagnostic efficiency. However, previous researches on the application of AI in ultrasound are focused on single or multi-modality static ultrasound images. This multi-center study intends to evaluate the value of the detection and differential diagnosis of breast mass using deep learning AI-based real-time ultrasound examination.
- Conditions
- Breast Neoplasms
- Being tested
- Yizhun BUSMS
- Medical subject headings
- Breast Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Yizhun BUSMSDevice
During the breast scanning, Yizhun BUSMS uses different color box to identify the breast lesion, and the box color indicates the risk grade of the lesion.
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic performance of breast mass using deep learning AI-based real-time ultrasound examination
Pathology as a gold standard, to evaluate the diagnostic performance (sensitivity, specificity and accuracy)
Measured: 12 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted5 Jul 2022
- Study start12 Aug 2021
- Primary completion31 Aug 2022 (estimated)
- Study completion31 Aug 2023 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Breast NeoplasmsStudies of the same condition, open and under way.
- More trials run by Cancer Institute and Hospital, Chinese Academy of Medical SciencesThe sponsor's other registered studies.
- 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
- Prospective
- Enrolment
- 1,122 participants (estimated)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Collaborators
- Peking Union Medical College Hospital, Peking University Cancer Hospital & Institute, Peking University Third Hospital, Guangdong Provincial Hospital of Traditional Chinese Medicine, Third Affiliated Hospital of Zhengzhou University, Hebei Medical University Fourth Hospital, Henan Cancer Hospital, Shanxi Province Cancer Hospital, First Affiliated Hospital Xi'an Jiaotong University, Chongqing University Cancer Hospital, Anqing Municipal Hospital, Qinhuangdao Maternal and Child Health Care Hospital, The First Affiliated Hospital of Xiamen University, Anyang Tumor Hospital, The Third Affiliated Hospital of Jinzhou Medical University, General Hospital of Jincheng Coal Industry Group, The First Affiliated Hospital of Soochow University, Peking University
- Responsible party
- Sponsor
- Investigators
- Yong Wang, Principal investigator, Cancer Institute and Hospital, Chinese Academy of Medical Sciences
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05443672
- Sponsor's protocol ID
- NCC2962
- Status last verified
- Jun 2022
- First submitted
- 29 Jun 2022
- Last updated
- 5 Jul 2022
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 5 Jul 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Multi-center Study of Breast Mass Screening and Diagnosis Using Deep Learning AI-based on Real-time Ultrasound Examination
- ClinicalTrials.gov ID
- NCT05443672
- Acronym / sponsor ID
- NCC2962
- Conditions
- Breast Neoplasms
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 1,122 participants (estimated)
- Lead sponsor
- Cancer Institute and Hospital, Chinese Academy of Medical SciencesUniversity, hospital or other
- Collaborators
- Peking Union Medical College Hospital, Peking University Cancer Hospital & Institute, Peking University Third Hospital, Guangdong Provincial Hospital of Traditional Chinese Medicine, Third Affiliated Hospital of Zhengzhou University, Hebei Medical University Fourth Hospital, Henan Cancer Hospital, Shanxi Province Cancer Hospital, First Affiliated Hospital Xi'an Jiaotong University, Chongqing University Cancer Hospital, Anqing Municipal Hospital, Qinhuangdao Maternal and Child Health Care Hospital, The First Affiliated Hospital of Xiamen University, Anyang Tumor Hospital, The Third Affiliated Hospital of Jinzhou Medical University, General Hospital of Jincheng Coal Industry Group, The First Affiliated Hospital of Soochow University, Peking University
- Study start
- 12 Aug 2021 (actual)
- Primary completion
- 31 Aug 2022 (estimated)
- Study completion
- 31 Aug 2023 (estimated)
- First posted
- 5 Jul 2022 (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.
This multi-center study intends to evaluate the value of the detection and differential diagnosis of breast mass using deep learning AI-based real-time ultrasound examination.
Detailed description
As the most common cancer expected to occur all over the world, extensive population screening plays a very important role in the early diagnosis and prognosis of the breast cancer. X-ray and ultrasound are the most commonly used screening methods, and ultrasound is especially important for Asian women with dense breasts. However, ultrasound is greatly affected by the operator's skill and experience, and the diagnostic accuracy varies greatly.
Artificial intelligence (AI) is a new method emerging in recent years, active in many medical fields and can effectively improve the diagnostic efficiency. However, previous researches on the application of AI in ultrasound are focused on single or multi-modality static ultrasound images. This multi-center study intends to evaluate the value of the detection and differential diagnosis of breast mass using deep learning AI-based real-time ultrasound examination.
Study design
- Observational model
- Other
- Time perspective
- Prospective
Who may qualify
- Sex
- Female only
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Femal patients with breast neoplasm
Key inclusion criteria (2 listed)
- Females who undergo ultrasound examination for a complaint of breast lesion;
- The breast lesion that will obtain definite pathological diagnosis or follow-up at least two years.
Key exclusion criteria (2 listed)
- The breast lesion that has received CNB or FNA;
- The breast cancer patient who has received neoadjuvant chemotherapy.
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 |
|---|---|---|
| Yizhun BUSMSDuring the breast scanning, Yizhun BUSMS uses different color box to identify the breast lesion, and the box color indicates the risk grade of the lesion. | Device | – |
Primary outcome
Diagnostic performance of breast mass using deep learning AI-based real-time ultrasound examination
Time frame: 12 months
Pathology as a gold standard, to evaluate the diagnostic performance (sensitivity, specificity and accuracy)
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 5 Jul 2022.
- Individual participant dataSharing planned: No
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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.
Yong Wang principal investigator
Cancer Institute and Hospital, Chinese Academy of Medical Sciences
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 Neoplasms, run by Cancer Institute and Hospital, Chinese Academy of Medical Sciences 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 2 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 5 Jul 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 Yong Wang (13391817899, drwangyong77@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnostic performance of breast mass using deep learning AI-based real-time ultrasound examination (time frame: 12 months). 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 5 Jul 2022.