Breast CancerNCT03887598
Application of Ultrasound Artificial Intelligence and Elastography in Differential Diagnosis of Breast Nodules
An observational study of Breast Cancer, run by Xin-Wu Cui at 1 site in China.
Trial summary
The application of computer-aided diagnosis (CAD) technology "S-Detect" enables qualitative and quantitative automated analysis of ultrasound images to obtain objective, repeatable and more accurate diagnostic results. 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?
- Ultrasound diagnosis
- Type of treatment
- Medical device
- How many people
- 2,000 participants
- Where
- 1 siteChina
- Study dates
- 18 Jan 2019to 18 Feb 2020
- 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: Patients with breast nodules in large tertiary hospitals
Who may be able to join
- Had breast lesions detected by ultrasound
- Age 18 or older
- Upcoming FNAB or surgery
- Signing informed consent
Who cannot join
- Patients who had received a biopsy of breast lesion before the ultrasound examination
- Can not cooperate with the test operation
- Patients who were pregnant or lactating
- Patients who were undergoing neoadjuvant treatment.
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
Xin-Wu Cui
Wuhan, Hubei, 430030
Xin-Wu Cui, PhD,MD, 15927103161, cuixinwu@live.cn
You-Bin Deng, PhD,MD, 13871197838, ybdeng2007@hotmail.com
About this study
Official title: A Multi-center Study of Differential Diagnosis Breast Nodules by Ultrasound Artificial Intelligence and Ultrasound Elastography
The application of computer-aided diagnosis (CAD) technology "S-Detect" enables qualitative and quantitative automated analysis of ultrasound images to obtain objective, repeatable and more accurate diagnostic results. The Elastic Contrast Index (ECI) technique, unlike conventional strain-elastic imaging techniques, can evaluate the elastic distribution in the region of interest. The purpose of the study was to evaluate the differential diagnosis value of ultrasound S-Detect technology for benign and malignant breast nodules and evaluate the differential diagnosis consistency of the ultrasound S-Detect technique and the examiner for benign and malignant breast nodules and explore the differential diagnosis value of Samsung ultrasound elastic contrast Index (ECI) technique for benign and malignant breast nodules.
Read the detailed description
Breast cancer is the most common malignancy in women and the second leading cause of cancer deaths worldwide. Therefore, early detection of breast cancer and timely treatment are of great significance for controlling and reducing breast cancer mortality. Breast ultrasound is an adjunct to extensive use in the detection of breast cancer, but ultrasound is highly technically dependent on the examiner, and the results are greatly influenced by the subjective nature of the examiner, adding unnecessary surgery and puncture, which causes great problems for clinicians and patients.Moreover, the value of conventional ultrasound in the differential diagnosis of breast mass is still limited, and the emergence of new technologies such as artificial intelligence and elastography has improved the accuracy of ultrasound diagnosis to varying degrees.
S-Detect technology is a computer-aided (CAD) system recently developed by Samsung Medical Center for breast ultrasound to assist in morphological analysis based on the Breast Imaging Reporting and Data System (BI-RADS) description and final assessment.This provides a new way to identify the benign and malignant breast nodules.
The E-Breast technique, unlike conventional strain-elastic imaging technology, performs an elastic analysis of the entire two-dimensional image.Moreover, when measuring the elastic ratio, it is only necessary to place a region of interest (ROI) at the nodule.Compared with the average elasticity of the surrounding area, it is more reflective of the elastic ratio of the mass to the surrounding tissue.
- Conditions
- Breast Cancer
- Being tested
- Ultrasound diagnosis
- Keywords
- Artificial Intelligence; Ultrasound; Breast nodule; ECI
- Medical subject headings
- Breast Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Ultrasound diagnosisDevice
Ultrasound diagnosis of lesions with Samsung S-Detect and ECI technology
| Study group | Role | What they receive |
|---|---|---|
| breast nodule | Those with one or more breast nodules, age 18 or older, upcoming FNAB or surgery and signed informed consent.Those without adverse effects on the test or threatening other candidates, such as mental illness, pregnancy, poor ultrasound image quality, history of breast surgery or breast biopsy, simple cystic nodules, calcification, excessive mass or too small, the S-DetectTM system can not identify the boundary of the tumor, the basic information is incomplete. Receives: Device: Ultrasound diagnosis |
What is being measured
The primary outcome is the main question the study is designed to answer.
Benign or malignant lesions as determined by pathology
The pathological diagnosis of benign or malignant lesions from surgery samples
Measured: Before surgery or biopsy
Elastic ratio
Clear ECI value
Measured: Before surgery or biopsy
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted25 Mar 2019
- Study start18 Jan 2019
- Primary completion18 Jan 2020 (estimated)
- Study completion18 Feb 2020 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (5)Papers the sponsor has linked to this study.
- 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
- Case only
- Time perspective
- Prospective
- Enrolment
- 2,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Xin-Wu Cui
- Sponsor type
- University, hospital or other
- Collaborators
- Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Taizhou Hospital, Wuhan Hospital of Traditional Chinese Medicine, Macheng People's Hospital, Huangshi Central Hospital, Affiliated Hospital of Jiangsu University, The First People's Hospital of Yichang, Yichang Second People's Hospital, Xiangyang Central Hospital, The Second Hospital of Anhui Medical University, Anqing Municipal Hospital, Huainan People's Hospital, Wenzhou Central Hospital, Xuzhou First People's Hospital, The Central Hospital of Lishui City, Huai'an First People's Hospital, WISCO General Hospital, First People's Hospital of Jiangxia District, Wuhan City, Enshi State Central Hospital, Lianyungang Third People's Hospital, First People's Hospital of Xianyang
- Responsible party
- Sponsor-investigator, Xin-Wu Cui, Tongji Hospital
- Investigators
- Xin-Wu Cui, PhD,MD, Study chair, Tongji HospitalYou-Bin Deng, PhD,MD, Study chair, Tongji Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT03887598
- Sponsor's protocol ID
- 2019(S073)
- Status last verified
- Mar 2019
- First submitted
- 21 Mar 2019
- Last updated
- 26 Mar 2019
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- Yes
- Individual data sharing
- No: Not public because of the personal information of the participants
Publications
Choi JH, Kang BJ, Baek JE, Lee HS, Kim SH. Application of computer-aided diagnosis in breast ultrasound interpretation: improvements in diagnostic performance according to reader experience. Ultrasonography. 2018 Jul;37(3):217-225. doi: 10.14366/usg.17046. Epub 2017 Aug 14.
PubMed 28992680Kowal M, Filipczuk P, Obuchowicz A, Korbicz J, Monczak R. Computer-aided diagnosis of breast cancer based on fine needle biopsy microscopic images. Comput Biol Med. 2013 Oct;43(10):1563-72. doi: 10.1016/j.compbiomed.2013.08.003. Epub 2013 Aug 19.
PubMed 24034748Di Segni M, de Soccio V, Cantisani V, Bonito G, Rubini A, Di Segni G, Lamorte S, Magri V, De Vito C, Migliara G, Bartolotta TV, Metere A, Giacomelli L, de Felice C, D'Ambrosio F. Automated classification of focal breast lesions according to S-detect: validation and role as a clinical and teaching tool. J Ultrasound. 2018 Jun;21(2):105-118. doi: 10.1007/s40477-018-0297-2. Epub 2018 Apr 21.
PubMed 29681007Kim K, Song MK, Kim EK, Yoon JH. Clinical application of S-Detect to breast masses on ultrasonography: a study evaluating the diagnostic performance and agreement with a dedicated breast radiologist. Ultrasonography. 2017 Jan;36(1):3-9. doi: 10.14366/usg.16012. Epub 2016 Apr 14.
PubMed 27184656Wei Q, Yan YJ, Wu GG, Ye XR, Jiang F, Liu J, Wang G, Wang Y, Song J, Pan ZP, Hu JH, Jin CY, Wang X, Dietrich CF, Cui XW. The diagnostic performance of ultrasound computer-aided diagnosis system for distinguishing breast masses: a prospective multicenter study. Eur Radiol. 2022 Jun;32(6):4046-4055. doi: 10.1007/s00330-021-08452-1. Epub 2022 Jan 23.
PubMed 35066633
Study at a glance
From the registry record, last updated by the sponsor on 26 Mar 2019. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Multi-center Study of Differential Diagnosis Breast Nodules by Ultrasound Artificial Intelligence and Ultrasound Elastography
- ClinicalTrials.gov ID
- NCT03887598
- Acronym / sponsor ID
- 2019(S073)
- Conditions
- Breast Cancer
- Keywords
- Artificial Intelligence; Ultrasound; Breast nodule; ECI
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 2,000 participants (estimated)
- Lead sponsor
- Xin-Wu CuiUniversity, hospital or other
- Collaborators
- Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Taizhou Hospital, Wuhan Hospital of Traditional Chinese Medicine, Macheng People's Hospital, Huangshi Central Hospital, Affiliated Hospital of Jiangsu University, The First People's Hospital of Yichang, Yichang Second People's Hospital, Xiangyang Central Hospital, The Second Hospital of Anhui Medical University, Anqing Municipal Hospital, Huainan People's Hospital, Wenzhou Central Hospital, Xuzhou First People's Hospital, The Central Hospital of Lishui City, Huai'an First People's Hospital, WISCO General Hospital, First People's Hospital of Jiangxia District, Wuhan City, Enshi State Central Hospital, Lianyungang Third People's Hospital, First People's Hospital of Xianyang
- Study start
- 18 Jan 2019 (actual)
- Primary completion
- 18 Jan 2020 (estimated)
- Study completion
- 18 Feb 2020 (estimated)
- First posted
- 25 Mar 2019 (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 application of computer-aided diagnosis (CAD) technology "S-Detect" enables qualitative and quantitative automated analysis of ultrasound images to obtain objective, repeatable and more accurate diagnostic results. The Elastic Contrast Index (ECI) technique, unlike conventional strain-elastic imaging techniques, can evaluate the elastic distribution in the region of interest. The purpose of the study was to evaluate the differential diagnosis value of ultrasound S-Detect technology for benign and malignant breast nodules and evaluate the differential diagnosis consistency of the ultrasound S-Detect technique and the examiner for benign and malignant breast nodules and explore the differential diagnosis value of Samsung ultrasound elastic contrast Index (ECI) technique for benign and malignant breast nodules.
Detailed description
Breast cancer is the most common malignancy in women and the second leading cause of cancer deaths worldwide. Therefore, early detection of breast cancer and timely treatment are of great significance for controlling and reducing breast cancer mortality. Breast ultrasound is an adjunct to extensive use in the detection of breast cancer, but ultrasound is highly technically dependent on the examiner, and the results are greatly influenced by the subjective nature of the examiner, adding unnecessary surgery and puncture, which causes great problems for clinicians and patients.Moreover, the value of conventional ultrasound in the differential diagnosis of breast mass is still limited, and the emergence of new technologies such as artificial intelligence and elastography has improved the accuracy of ultrasound diagnosis to varying degrees.
S-Detect technology is a computer-aided (CAD) system recently developed by Samsung Medical Center for breast ultrasound to assist in morphological analysis based on the Breast Imaging Reporting and Data System (BI-RADS) description and final assessment.This provides a new way to identify the benign and malignant breast nodules.
The E-Breast technique, unlike conventional strain-elastic imaging technology, performs an elastic analysis of the entire two-dimensional image.Moreover, when measuring the elastic ratio, it is only necessary to place a region of interest (ROI) at the nodule.Compared with the average elasticity of the surrounding area, it is more reflective of the elastic ratio of the mass to the surrounding tissue.
Study design
- Observational model
- Case only
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| breast nodule | – | Device: Ultrasound diagnosisThose with one or more breast nodules, age 18 or older, upcoming FNAB or surgery and signed informed consent.Those without adverse effects on the test or threatening other candidates, such as mental illness, pregnancy, poor ultrasound image quality, history of breast surgery or breast biopsy, simple cystic nodules, calcification, excessive mass or too small, the S-DetectTM system can not identify the boundary of the tumor, the basic information is incomplete. |
Who may qualify
- Ages
- 18 years and older
- Sex
- Female only
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Patients with breast nodules in large tertiary hospitals
Key inclusion criteria (4 listed)
- Had breast lesions detected by ultrasound
- Age 18 or older
- Upcoming FNAB or surgery
- Signing informed consent
Key exclusion criteria (4 listed)
- Patients who had received a biopsy of breast lesion before the ultrasound examination
- Can not cooperate with the test operation
- Patients who were pregnant or lactating
- Patients who were undergoing neoadjuvant treatment.
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 |
|---|---|---|
| Ultrasound diagnosisUltrasound diagnosis of lesions with Samsung S-Detect and ECI technology | Device | breast nodule |
Primary outcome
Benign or malignant lesions as determined by pathology
Time frame: Before surgery or biopsy
The pathological diagnosis of benign or malignant lesions from surgery samples
Elastic ratio
Time frame: Before surgery or biopsy
Clear ECI value
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 26 Mar 2019.
- Results publication: PubMed 28992680Choi JH, Kang BJ, Baek JE, Lee HS, Kim SH. Application of computer-aided diagnosis in breast ultrasound interpretation: improvements in diagnostic performance according to reader experience. Ultrasonography. 2018 Jul;37(3):217-225. doi: 10.14366/usg.17046. Epub 2017 Aug 14.
- Results publication: PubMed 24034748Kowal M, Filipczuk P, Obuchowicz A, Korbicz J, Monczak R. Computer-aided diagnosis of breast cancer based on fine needle biopsy microscopic images. Comput Biol Med. 2013 Oct;43(10):1563-72. doi: 10.1016/j.compbiomed.2013.08.003. Epub 2013 Aug 19.
- Results publication: PubMed 29681007Di Segni M, de Soccio V, Cantisani V, Bonito G, Rubini A, Di Segni G, Lamorte S, Magri V, De Vito C, Migliara G, Bartolotta TV, Metere A, Giacomelli L, de Felice C, D'Ambrosio F. Automated classification of focal breast lesions according to S-detect: validation and role as a clinical and teaching tool. J Ultrasound. 2018 Jun;21(2):105-118. doi: 10.1007/s40477-018-0297-2. Epub 2018 Apr 21.
- Results publication: PubMed 27184656Kim K, Song MK, Kim EK, Yoon JH. Clinical application of S-Detect to breast masses on ultrasonography: a study evaluating the diagnostic performance and agreement with a dedicated breast radiologist. Ultrasonography. 2017 Jan;36(1):3-9. doi: 10.14366/usg.16012. Epub 2016 Apr 14.
- Derived from this study: PubMed 35066633Wei Q, Yan YJ, Wu GG, Ye XR, Jiang F, Liu J, Wang G, Wang Y, Song J, Pan ZP, Hu JH, Jin CY, Wang X, Dietrich CF, Cui XW. The diagnostic performance of ultrasound computer-aided diagnosis system for distinguishing breast masses: a prospective multicenter study. Eur Radiol. 2022 Jun;32(6):4046-4055. doi: 10.1007/s00330-021-08452-1. Epub 2022 Jan 23.
- Individual participant dataSharing planned: No. Not public because of the personal information of the participants
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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.
Xin-Wu Cui, PhD,MD study chair
Tongji Hospital
You-Bin Deng, PhD,MD study chair
Tongji Hospital
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 Xin-Wu Cui 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 4 inclusion and 4 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 26 Mar 2019. 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 Li-Qiang Zhou, MD (15387076275, zlq_1118@hust.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Benign or malignant lesions as determined by pathology (time frame: Before surgery or biopsy). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT03887598, which also lists 5 linked publications.
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 26 Mar 2019.