Breast CarcinomaNCT07605195
Research on the Whole-Process Intelligent Diagnosis and Treatment of Digital Breast Tomosynthesis Based on Deep Learning
A study testing To explore the value of digital breast tomosynthesis based on deep learning in the diagnosis of breast cancer in people with Breast Carcinoma, run by Yunnan Cancer Hospital.
Trial summary
This study aims to construct a multi-task deep learning model system to mine deep features in DBT images, so as to achieve accurate detection of breast lesions, differential diagnosis of benign and malignant (especially for the challenging BI-RADS 4A category), prediction of molecular subtypes, and evaluation of… Read more about this study
- Who is it for?
- Ages 18 to 80 years, women only
- Conditions the study is for
- Breast Carcinoma
- What is being tested?
- To explore the value of digital breast tomosynthesis based on deep learning in the diagnosis of breast cancer
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 5,000 participants
- Where
- No sites listed yet
- Study dates
- 20 May 2026to 30 Jun 2029
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
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 to 80 years
- Women only
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Female patients aged ≥ 18 years.
- Complete bilateral digital breast tomosynthesis (DBT) images available, including craniocaudal (CC) and mediolateral oblique (MLO) views.
- Confirmed pathological diagnosis (core needle biopsy or surgical resection) serving as the reference standard; or benign lesions with stable findings on follow-up for more than 2 years.
- (For the efficacy prediction subgroup) Patients who received complete neoadjuvant therapy and had postoperative pathological results.
- 2. Exclusion Criteria
- Poor image quality with severe artifacts that precluded reliable analysis.
- History of previous breast surgery or radiotherapy (except for the recurrence risk subgroup).
- Incomplete clinical or pathological data.
Who cannot join
None listed.
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
Official title: Research on the Whole-Process Intelligent Diagnosis and Treatment of Digital Breast Tomosynthesis Based on Deep Learning: Multicenter Retrospective and Prospective Validation
This study aims to construct a multi-task deep learning model system to mine deep features in DBT images, so as to achieve accurate detection of breast lesions, differential diagnosis of benign and malignant (especially for the challenging BI-RADS 4A category), prediction of molecular subtypes, and evaluation of neoadjuvant chemotherapy (NAC) efficacy, providing an imaging basis for precision medicine.
- Conditions
- Breast Carcinoma
- Being tested
- To explore the value of digital breast tomosynthesis based on deep learning in the diagnosis of breast cancer
- Medical subject headings
- Breast Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
To explore the value of digital breast tomosynthesis based on deep learning in the diagnosis of breast cancerDiagnostic test
The digital breast tomosynthesis is part of the standard treatment protocol.
| Study group | Role | What they receive |
|---|---|---|
| The application value of digital breast tomosynthesis in the accurate diagnosis of breast cancer | Experimental | This study aims to construct a multi-task deep learning model system to mine deep features in DBT images, so as to achieve accurate detection of breast lesions, differential diagnosis of benign and malignant (especially for the challenging BI-RADS 4A category), prediction of molecular subtypes, and evaluation of neoadjuvant chemotherapy (NAC) efficacy, providing an imaging basis for precision medicine. Receives: Diagnostic Test: To explore the value of digital breast tomosynthesis based on deep learning in the diagnosis of breast cancer |
What is being measured
The primary outcome is the main question the study is designed to answer.
The accuracy of the multi-task deep learning-based intelligent diagnostic model in differentiating benign and malignant breast lesions on digital breast tomosynthesis (DBT) images.
Taking surgical or puncture histopathological results as the gold standard, the accuracy of the multi-task deep learning-based intelligent diagnostic model in differentiating benign and malignant breast lesions on digital breast tomosynthesis (DBT) images was evaluated. It focuses on challenging BI-RADS 4A lesions, covering retrospective multi-center validation sets and prospective multi-center validation sets to ensure the representativeness and rigor of the indicator.
Measured: 1day
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted22 May 2026
- Study start20 May 2026 (estimated)
- Primary completion31 Dec 2026 (estimated)
- Study completion30 Jun 2029 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Breast CarcinomaStudies 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
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 5,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Yunnan Cancer Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Lianhua Ye, Study director, Ethics Committee of Yunnan Provincial Cancer Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07605195
- Sponsor's protocol ID
- KYLX2026-064
- Status last verified
- May 2026
- First submitted
- 27 Apr 2026
- Last updated
- 22 May 2026
- Results posted
- No
- Data monitoring committee
- 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 22 May 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Research on the Whole-Process Intelligent Diagnosis and Treatment of Digital Breast Tomosynthesis Based on Deep Learning: Multicenter Retrospective and Prospective Validation
- ClinicalTrials.gov ID
- NCT07605195
- Acronym / sponsor ID
- KYLX2026-064
- Conditions
- Breast Carcinoma
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 5,000 participants (estimated)
- Lead sponsor
- Yunnan Cancer HospitalUniversity, hospital or other
- Study start
- 20 May 2026 (estimated)
- Primary completion
- 31 Dec 2026 (estimated)
- Study completion
- 30 Jun 2029 (estimated)
- First posted
- 22 May 2026 (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 study aims to construct a multi-task deep learning model system to mine deep features in DBT images, so as to achieve accurate detection of breast lesions, differential diagnosis of benign and malignant (especially for the challenging BI-RADS 4A category), prediction of molecular subtypes, and evaluation of neoadjuvant chemotherapy (NAC) efficacy, providing an imaging basis for precision medicine.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| The application value of digital breast tomosynthesis in the accurate diagnosis of breast cancer | Experimental | Diagnostic Test: To explore the value of digital breast tomosynthesis based on deep learning in the diagnosis of breast cancerThis study aims to construct a multi-task deep learning model system to mine deep features in DBT images, so as to achieve accurate detection of breast lesions, differential diagnosis of benign and malignant (especially for the challenging BI-RADS 4A category), prediction of molecular subtypes, and evaluation of neoadjuvant chemotherapy (NAC) efficacy, providing an imaging basis for precision medicine. |
Who may qualify
- Ages
- 18 to 80 years
- Sex
- Female only
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (8 listed)
- Female patients aged ≥ 18 years.
- Complete bilateral digital breast tomosynthesis (DBT) images available, including craniocaudal (CC) and mediolateral oblique (MLO) views.
- Confirmed pathological diagnosis (core needle biopsy or surgical resection) serving as the reference standard; or benign lesions with stable findings on follow-up for more than 2 years.
- (For the efficacy prediction subgroup) Patients who received complete neoadjuvant therapy and had postoperative pathological results.
- 2. Exclusion Criteria
Key exclusion criteria (0 listed)
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 |
|---|---|---|
| To explore the value of digital breast tomosynthesis based on deep learning in the diagnosis of breast cancerThe digital breast tomosynthesis is part of the standard treatment protocol. | Diagnostic test | The application value of digital breast tomosynthesis in the accurate diagnosis of breast cancer |
Primary outcome
The accuracy of the multi-task deep learning-based intelligent diagnostic model in differentiating benign and malignant breast lesions on digital breast tomosynthesis (DBT) images.
Time frame: 1day
Taking surgical or puncture histopathological results as the gold standard, the accuracy of the multi-task deep learning-based intelligent diagnostic model in differentiating benign and malignant breast lesions on digital breast tomosynthesis (DBT) images was evaluated. It focuses on challenging BI-RADS 4A lesions, covering retrospective multi-center validation sets and prospective multi-center validation sets to ensure the representativeness and rigor of the indicator.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 22 May 2026.
- 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.
Lianhua Ye study director
Ethics Committee of Yunnan Provincial Cancer Hospital
Questions about this trial
What is this clinical trial studying?
A study testing To explore the value of digital breast tomosynthesis based on deep learning in the diagnosis of breast cancer in people with Breast Carcinoma, run by Yunnan Cancer Hospital.
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 8 inclusion and 0 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: opening soon, as of the sponsor's last update on 22 May 2026. Status can change between updates, so confirm with the study contact before making plans.
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?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Yu Xie (13708445492, xieyu@kmmu.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
The accuracy of the multi-task deep learning-based intelligent diagnostic model in differentiating benign and malignant breast lesions on digital breast tomosynthesis (DBT) images. (time frame: 1day). 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 22 May 2026.