Breast DiseasesNCT06069921
Using Deep Learning and Radiomics to Diagnose Benign and Malignant Breast Lesions Based on Ultrasound
An observational study of Breast Diseases, run by Ma Zhe at 1 site in China.
Trial summary
This retrospective study aimed to create a prediction model using deep learning and radiomics features extracted from intratumoral and peritumoral regions of breast lesions in ultrasound images, to diagnose benign and malignant breast lesions with BI-RADS 4 classification. Read more about this study
- Who is it for?
- Ages 15 to 80 years, women only
- Conditions the study is for
- Breast Diseases
- How many people
- 400 participants
- Where
- 1 siteChina
- Study dates
- 1 Jan 2015to 30 Dec 2022
- 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 15 to 80 years
- Women only
- No healthy volunteers
- Child, Adult, Older adult
Study population: Patients with breast lesions attending the First Affiliated Hospital of Shandong First Medical University were selected.
Who may be able to join
- female patients with US-visible solid breast masses who underwent biopsy and/or surgical resection, and were classified as having BI-RADS 4 lesions in medical US reports.
Who cannot join
- preoperative endocrine therapy, chemotherapy, or radiotherapy, preoperative invasive breast operation, insufficient image quality, and no pathological results.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Ultrasound-based Deep Learning Signature and Radiomics Signature Nomogram for Diagnosis of Benign and Malignant Breast Lesions of BI-RADS Category 4 Using Intratumoral and Peritumoral Regions
This retrospective study aimed to create a prediction model using deep learning and radiomics features extracted from intratumoral and peritumoral regions of breast lesions in ultrasound images, to diagnose benign and malignant breast lesions with BI-RADS 4 classification.
Materials and methods: Patients who visited in The First Affiliated Hospital of Shandong First Medical University ＆ Shandong Provincial Qianfoshan Hospital were collected. Their general clinical features, information on preoperative ultrasound diagnosis, and postoperative pathologic data were reviewed.
- Conditions
- Breast Diseases
- Medical subject headings
- Breast Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| maligant | female patients with US-visible solid maligant breast masses who underwent biopsy and/or surgical resection. | |
| benign | female patients with US-visible solid benign breast masses who underwent biopsy and/or surgical resection. |
What is being measured
The primary outcome is the main question the study is designed to answer.
radiomcis prediction model and the model evaluation
three radiomics models were established using the support vector machines algorithm based on features extracted from the intratumoral, peritumoral, and combined regions of the breast lesions.The models were evaluated using various metrics, including AUC, accuracy, sensitivity, specificity, PPV, and NPV
Measured: Immediately evaluated after the radiomcis prediction model was built
Secondary outcomes (1)
deep learning prediction model and the model evaluation
three deep learning models were established using the support vector machines algorithm based on features extracted from the intratumoral, peritumoral, and combined regions of the breast lesions.The models were evaluated using various metrics, including AUC, accuracy, sensitivity, specificity, PPV, and NPV
Measured: Immediately evaluated after the deep learning prediction model was built
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted6 Oct 2023
- Study start1 Jan 2015
- Primary completion30 Dec 2022
- Study completion30 Dec 2022
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Breast DiseasesStudies 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
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 400 participants (actual)
Sponsor and investigators
- Lead sponsor
- Ma Zhe
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Ma Zhe, Qianfoshan Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06069921
- Sponsor's protocol ID
- YXLL-KY-2023(045)
- Status last verified
- Jun 2024
- First submitted
- 29 Sep 2023
- Last updated
- 25 Jun 2024
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Lack of resources or infrastructure: Sharing IPDs requires resources and infrastructure to ensure data security, manage access requests and provide necessary documentation. Currently these resources are not well developed, so it may be difficult to share IPDs.
Study at a glance
From the registry record, last updated by the sponsor on 25 Jun 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Ultrasound-based Deep Learning Signature and Radiomics Signature Nomogram for Diagnosis of Benign and Malignant Breast Lesions of BI-RADS Category 4 Using Intratumoral and Peritumoral Regions
- ClinicalTrials.gov ID
- NCT06069921
- Acronym / sponsor ID
- YXLL-KY-2023(045)
- Conditions
- Breast Diseases
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 400 participants (actual)
- Lead sponsor
- Ma ZheUniversity, hospital or other
- Study start
- 1 Jan 2015 (actual)
- Primary completion
- 30 Dec 2022 (actual)
- Study completion
- 30 Dec 2022 (actual)
- First posted
- 6 Oct 2023 (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 retrospective study aimed to create a prediction model using deep learning and radiomics features extracted from intratumoral and peritumoral regions of breast lesions in ultrasound images, to diagnose benign and malignant breast lesions with BI-RADS 4 classification.
Materials and methods: Patients who visited in The First Affiliated Hospital of Shandong First Medical University ＆ Shandong Provincial Qianfoshan Hospital were collected. Their general clinical features, information on preoperative ultrasound diagnosis, and postoperative pathologic data were reviewed.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| maligant | – | –female patients with US-visible solid maligant breast masses who underwent biopsy and/or surgical resection. |
| benign | – | –female patients with US-visible solid benign breast masses who underwent biopsy and/or surgical resection. |
Who may qualify
- Ages
- 15 to 80 years
- Sex
- Female only
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Patients with breast lesions attending the First Affiliated Hospital of Shandong First Medical University were selected.
Key inclusion criteria (1 listed)
- female patients with US-visible solid breast masses who underwent biopsy and/or surgical resection, and were classified as having BI-RADS 4 lesions in medical US reports.
Key exclusion criteria (1 listed)
- preoperative endocrine therapy, chemotherapy, or radiotherapy, preoperative invasive breast operation, insufficient image quality, and no pathological results.
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
radiomcis prediction model and the model evaluation
Time frame: Immediately evaluated after the radiomcis prediction model was built
three radiomics models were established using the support vector machines algorithm based on features extracted from the intratumoral, peritumoral, and combined regions of the breast lesions.The models were evaluated using various metrics, including AUC, accuracy, sensitivity, specificity, PPV, and NPV
1 secondary outcome
deep learning prediction model and the model evaluation
Time frame: Immediately evaluated after the deep learning prediction model was built
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 25 Jun 2024.
- Individual participant dataSharing planned: No. Lack of resources or infrastructure: Sharing IPDs requires resources and infrastructure to ensure data security, manage access requests and provide necessary documentation. Currently these resources are not well developed, so it may be difficult to share IPDs.
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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 Diseases, run by Ma Zhe 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 1 inclusion and 1 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 25 Jun 2024. 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?
radiomcis prediction model and the model evaluation (time frame: Immediately evaluated after the radiomcis prediction model was built). 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 25 Jun 2024.