Breast CancerNCT03851497
Application of Deep-learning and Ultrasound Elastography in Opportunistic Screening of Breast Cancer
An observational study of Breast Cancer, run by Peking Union Medical College Hospital at 1 site in China.
Trial summary
As the most common cancer expected to occur all over the world, breast cancer still faces with the unsatisfied diagnostic accuracy in US imaging. S-detect is a sophisticated CAD system for breast US imaging based on deep learning algorithms. Read more about this study
- Who is it for?
- Ages 18 years and older, women only
- Conditions the study is for
- Breast Cancer
- How many people
- 1,200 participants
- Where
- 1 siteChina
- Study dates
- 1 Jan 2019to 1 Jan 2021
- 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
- No healthy volunteers
- Adult, Older adult
Study population: Asymptomatic female patients voluntarily asked for breast US examination in comprehensive hospitals for breast cancer screening.
Who may be able to join
- Female over 18 years of age;
- Had breast lesions detected by ultrasound.
- No clinical symptoms such as nipple discharge, while breast lesions were not palpable.
- Received breast surgery within one week of ultrasound examination.
- Agreed to participant in this study and signed informed consent.
Who cannot join
- Patients who had received a biopsy of breast lesion before the ultrasound examination.
- Patients who were pregnant or lactating.
- Patients who were undergoing neoadjuvant treatment.
Trial locations and contacts
1 site in 1 country.
China1 site
Peking Union Medical College Hospital
Beijing, Beijing Municipality, 100730
About this study
Official title: A Multi-center Study of Deep Learning Diagnosis and Ultrasound Elastography in Opportunistic Screening of Breast Cancer
As the most common cancer expected to occur all over the world, breast cancer still faces with the unsatisfied diagnostic accuracy in US imaging. S-detect is a sophisticated CAD system for breast US imaging based on deep learning algorithms. E-breast is a software installed in US machines which automatically reveals tumor elastographic features. This multi-center study intends to further validate the diagnostic efficiency of S-detect and E-breast in opportunistic breast cancer screening populations in China. Our hypothesis is that S-detect and E-breast can increase the diagnostic accuracy and specificity as compared to routinely US examinations by doctors.
- Conditions
- Breast Cancer
- Medical subject headings
- Breast Neoplasms
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: From 2019.1.1 to 2020.1.1
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted22 Feb 2019
- Study start1 Jan 2019
- Primary completion1 Jan 2021
- Study completion1 Jan 2021
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.
- More trials run by Peking Union Medical College HospitalThe 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
- Cohort
- Time perspective
- Prospective
- Enrolment
- 1,200 participants (actual)
Sponsor and investigators
- Lead sponsor
- Peking Union Medical College Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- Peking University Third Hospital, Beijing Hospital, Beijing Chao Yang Hospital, Beijing Zhongguancun Hospital, Peking University Aerospace Center Hospital, Beijing Anzhen Community Health Service Center, First Hospital of Tsinghua University, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, The First Affiliated Hospital of Zhengzhou University, Henan Provincial People's Hospital, Third Affiliated Hospital of Zhengzhou University, Xinxiang Central Hospital, Henan Cancer Hospital, First Hospital of China Medical University, Shengjing Hospital, Liaoning Cancer Hospital & Institute, West China Hospital, Sichuan Provincial People's Hospital, Yan'an Affiliated Hospital of Kunming Medical University, Xi'an Central Hospital, Ningxia Medical University, First Hospital of Shijiazhuang City, Chengde Central Hospital, Qinghai Province Fifth People's Hospital, Gansu Cancer Hospital, Shanghai Zhongshan Hospital, Ruijin Hospital, The Affiliated Hospital of Qingdao University, Qingdao Central Hospital, Jining First People's Hospital, Linyi Tumour Hospital, The First Affiliated Hospital of Shanxi Medical University, The Second Affiliated Hospital of Harbin Medical University, Second Hospital of Jilin University, The Second Hospital of the West Coast New Area of Qingdao, Fudan University, Tongji Hospital, Jiangsu Province People's Hospital, Peking University Shougang Hospital, Gansu Jiugang Hospital
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT03851497
- Sponsor's protocol ID
- S-detect 2019
- Status last verified
- Mar 2021
- First submitted
- 21 Feb 2019
- Last updated
- 26 Mar 2021
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 26 Mar 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Multi-center Study of Deep Learning Diagnosis and Ultrasound Elastography in Opportunistic Screening of Breast Cancer
- ClinicalTrials.gov ID
- NCT03851497
- Acronym / sponsor ID
- S-detect 2019
- Conditions
- Breast Cancer
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 1,200 participants (actual)
- Lead sponsor
- Peking Union Medical College HospitalUniversity, hospital or other
- Collaborators
- Peking University Third Hospital, Beijing Hospital, Beijing Chao Yang Hospital, Beijing Zhongguancun Hospital, Peking University Aerospace Center Hospital, Beijing Anzhen Community Health Service Center, First Hospital of Tsinghua University, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, The First Affiliated Hospital of Zhengzhou University, Henan Provincial People's Hospital, Third Affiliated Hospital of Zhengzhou University, Xinxiang Central Hospital, Henan Cancer Hospital, First Hospital of China Medical University, Shengjing Hospital, Liaoning Cancer Hospital & Institute, West China Hospital, Sichuan Provincial People's Hospital, Yan'an Affiliated Hospital of Kunming Medical University, Xi'an Central Hospital, Ningxia Medical University, First Hospital of Shijiazhuang City, Chengde Central Hospital, Qinghai Province Fifth People's Hospital, Gansu Cancer Hospital, Shanghai Zhongshan Hospital, Ruijin Hospital, The Affiliated Hospital of Qingdao University, Qingdao Central Hospital, Jining First People's Hospital, Linyi Tumour Hospital, The First Affiliated Hospital of Shanxi Medical University, The Second Affiliated Hospital of Harbin Medical University, Second Hospital of Jilin University, The Second Hospital of the West Coast New Area of Qingdao, Fudan University, Tongji Hospital, Jiangsu Province People's Hospital, Peking University Shougang Hospital, Gansu Jiugang Hospital
- Study start
- 1 Jan 2019 (actual)
- Primary completion
- 1 Jan 2021 (actual)
- Study completion
- 1 Jan 2021 (actual)
- First posted
- 22 Feb 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.
As the most common cancer expected to occur all over the world, breast cancer still faces with the unsatisfied diagnostic accuracy in US imaging. S-detect is a sophisticated CAD system for breast US imaging based on deep learning algorithms. E-breast is a software installed in US machines which automatically reveals tumor elastographic features. This multi-center study intends to further validate the diagnostic efficiency of S-detect and E-breast in opportunistic breast cancer screening populations in China. Our hypothesis is that S-detect and E-breast can increase the diagnostic accuracy and specificity as compared to routinely US examinations by doctors.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 years and older
- Sex
- Female only
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Asymptomatic female patients voluntarily asked for breast US examination in comprehensive hospitals for breast cancer screening.
Key inclusion criteria (5 listed)
- Female over 18 years of age;
- Had breast lesions detected by ultrasound.
- No clinical symptoms such as nipple discharge, while breast lesions were not palpable.
- Received breast surgery within one week of ultrasound examination.
- Agreed to participant in this study and signed informed consent.
Key exclusion criteria (3 listed)
- Patients who had received a biopsy of breast lesion before the ultrasound examination.
- 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.
Primary outcome
Benign or malignant lesions as determined by pathology
Time frame: From 2019.1.1 to 2020.1.1
The pathological diagnosis of benign or malignant lesions from surgery samples
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 26 Mar 2021.
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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.
The record lists no contact. Use the official record to reach the sponsor.
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 Peking Union Medical College Hospital 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 5 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: finished, as of the sponsor's last update on 26 Mar 2021. 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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Benign or malignant lesions as determined by pathology (time frame: From 2019.1.1 to 2020.1.1). 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 26 Mar 2021; AzTrials last refreshed its copy on 7 Nov 2026.