Breast Cancer, MetastaticNCT07063667
Artificial Intelligence Model-Assisted Accurate Diagnosis of Early-Stage Breast Cancer
An observational study of Breast Cancer, Metastatic and Artifical Intelligence, run by Daping Hospital and the Research Institute of Surgery of the Third Military Medical University at 1 site in China.
Trial summary
Retrospectively collect the clinical data, breast MRI images, breast ultrasound images and reports, laboratory indicators (such as CA199, CA153, CA125, CEA/AFP), pathological diagnosis results, HE staining images, and existing immunohistochemical results (including CD8A, KPT5, GFRA1, PFKP, ER/PR percentage, Her-2… Read more about this study
- Who is it for?
- Ages 19 to 85 years
- Conditions the study is for
- Breast Cancer, Metastatic, Artifical Intelligence
- What is being tested?
- bulid primary AI model and verdict model and develop its function
- How many people
- 900 participants
- Where
- 1 siteChina
- Study dates
- 1 Aug 2025to 31 Oct 2026
- Phase
- Observational
- 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 19 to 85 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Retrospectively collect the clinical data, breast MRI images, breast ultrasound images and reports, laboratory indicators (such as CA199, CA153, CA125, CEA/AEP), pathological diagnosis results, HE staining images, and existing immunohistochemical results (including CD8A, KPT5, GFRA1, PFKP, ER/PR percentage, Her-2 expression, Ki-67 index, etc.) of patients who were pathologically confirmed with breast cancer or excluded from breast cancer in our center between January 2019 and December 2024. For biopsy specimens from patients diagnosed with breast cancer and immunohistochemically confirmed as HR+/Her-2+ between January 2019 and December 2024, additional immunohistochemical staining for CD8A, KPT5, GFRA1, and PFKP should be performed, and the immunohistochemical images and results should be collected.
Who may be able to join
- Patients pathologically diagnosed with breast cancer or excluded from breast cancer
- Available pathological results of breast masses
- Involving diagnostic population onl
Who cannot join
- Suffering from mental disorders
- Presence of non-breast diseases during examination
- Presence of breast implants
- Undergoing non-breast surgery or having received radiotherapy/chemotherapy
- Lactating or pregnant women
- Missing data
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site
Army medical Cnter
Chongqing, Chongqing Municipality
Yan Xu, 8615923100038, xy931@163.com
About this study
Retrospectively collect the clinical data, breast MRI images, breast ultrasound images and reports, laboratory indicators (such as CA199, CA153, CA125, CEA/AFP), pathological diagnosis results, HE staining images, and existing immunohistochemical results (including CD8A, KPT5, GFRA1, PFKP, ER/PR percentage, Her-2 expression, Ki-67 index, etc.) of patients pathologically confirmed with or excluded from breast cancer in our center between January 2019 and December 2024. For biopsy specimens from patients diagnosed with breast cancer and immunohistochemically confirmed as HR+/Her-2+ during the same period, additional immunohistochemical staining for CD8A, KPT5, GFRA1, and PFKP should be performed, with images and results collected.
The collected basic clinical information, imaging data, pathological findings, and laboratory metrics of patients will serve as candidate inputs. Units of measurement will be standardized, and missing data will be imputed using the multiple imputation by chained equations algorithm. Data harmonization will employ the Box-Cox algorithm, while min-max scaling will be used for standardization. The adaptive synthetic sampling method with a balance ratio of 0.5 will address data imbalance. For the collected patient data, deep learning will be applied to screen features from the images, combined with clinical significance to identify malignant risk factors. A neural network classifier will be trained on the training set data, with independent variables including breast MRI/ultrasound images, CA199, CA153, CA125, AFP/CEA, etc., and dependent variables including breast cancer status and subtype. Pathological biopsy results will be set as the validation standard.
Model tuning will be conducted on the validation set to construct a breast cancer prediction model. It should be noted that as a single-center study, the results have limited generalizability. The further optimization and evaluation plan for the model involves using breast disease screening data from external centers for validation and refinement, evaluating the model's practical impact on clinical decision-making, and continuously tracking and optimizing its performance.
- Conditions
- Breast Cancer, Metastatic, Artifical Intelligence
- Being tested
- bulid primary AI model, verdict model and develop its function
- Medical subject headings
- Breast Neoplasms, Neoplasm Metastasis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
bulid primary AI modelOther
For the collected patient data, deep learning is used to perform feature screening on the selected or collected images, and malignant risk factors are determined by combining clinical significance. A neural network classifier is trained on the training set data. Variable selection: independent variables (breast MRI images, breast ultrasound images, indicators such as CA199, CA153, CA125, AFP/CEA, etc.), dependent variables (whether suffering from breast cancer and breast cancer subtypes), and the verification accuracy standard is set as the pathological biopsy result.
verdict model and develop its functionOther
The accuracy of a breast cancer prediction model is typically evaluated using multiple metrics that assess its performance in different aspects
| Study group | Role | What they receive |
|---|---|---|
| training group | Not described Receives: Other: bulid primary AI model | |
| verdict group | Not described Receives: Other: verdict model and develop its function |
What is being measured
The primary outcome is the main question the study is designed to answer.
AUC (Area Under the ROC Curve)
Measured: Baseline-AUC1 Perioperative/Periprocedural-AUC2
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted14 Jul 2025
- Study start1 Aug 2025 (estimated)
- Primary completion31 Dec 2025 (estimated)
- Study completion31 Oct 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Breast Cancer, MetastaticStudies 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
- 900 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Daping Hospital and the Research Institute of Surgery of the Third Military Medical University
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07063667
- Sponsor's protocol ID
- Ratification NO: 2025（188）
- Status last verified
- May 2025
- First submitted
- 25 May 2025
- Last updated
- 14 Jul 2025
- 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 14 Jul 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence Model-Assisted Accurate Diagnosis of Early-Stage Breast Cancer
- ClinicalTrials.gov ID
- NCT07063667
- Acronym / sponsor ID
- Ratification NO: 2025（188）
- Conditions
- Breast Cancer, Metastatic, Artifical Intelligence
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 900 participants (estimated)
- Lead sponsor
- Daping Hospital and the Research Institute of Surgery of the Third Military Medical UniversityUniversity, hospital or other
- Study start
- 1 Aug 2025 (estimated)
- Primary completion
- 31 Dec 2025 (estimated)
- Study completion
- 31 Oct 2026 (estimated)
- First posted
- 14 Jul 2025 (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.
Retrospectively collect the clinical data, breast MRI images, breast ultrasound images and reports, laboratory indicators (such as CA199, CA153, CA125, CEA/AFP), pathological diagnosis results, HE staining images, and existing immunohistochemical results (including CD8A, KPT5, GFRA1, PFKP, ER/PR percentage, Her-2 expression, Ki-67 index, etc.) of patients pathologically confirmed with or excluded from breast cancer in our center between January 2019 and December 2024. For biopsy specimens from patients diagnosed with breast cancer and immunohistochemically confirmed as HR+/Her-2+ during the same period, additional immunohistochemical staining for CD8A, KPT5, GFRA1, and PFKP should be performed, with images and results collected.
The collected basic clinical information, imaging data, pathological findings, and laboratory metrics of patients will serve as candidate inputs. Units of measurement will be standardized, and missing data will be imputed using the multiple imputation by chained equations algorithm. Data harmonization will employ the Box-Cox algorithm, while min-max scaling will be used for standardization. The adaptive synthetic sampling method with a balance ratio of 0.5 will address data imbalance. For the collected patient data, deep learning will be applied to screen features from the images, combined with clinical significance to identify malignant risk factors. A neural network classifier will be trained on the training set data, with independent variables including breast MRI/ultrasound images, CA199, CA153, CA125, AFP/CEA, etc., and dependent variables including breast cancer status and subtype. Pathological biopsy results will be set as the validation standard.
Model tuning will be conducted on the validation set to construct a breast cancer prediction model. It should be noted that as a single-center study, the results have limited generalizability. The further optimization and evaluation plan for the model involves using breast disease screening data from external centers for validation and refinement, evaluating the model's practical impact on clinical decision-making, and continuously tracking and optimizing its performance.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| training group | – | Other: bulid primary AI model |
| verdict group | – | Other: verdict model and develop its function |
Who may qualify
- Ages
- 19 to 85 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Retrospectively collect the clinical data, breast MRI images, breast ultrasound images and reports, laboratory indicators (such as CA199, CA153, CA125, CEA/AEP), pathological diagnosis results, HE staining images, and existing immunohistochemical results (including CD8A, KPT5, GFRA1, PFKP, ER/PR percentage, Her-2 expression, Ki-67 index, etc.) of patients who were pathologically confirmed with breast cancer or excluded from breast cancer in our center between January 2019 and December 2024. For biopsy specimens from patients diagnosed with breast cancer and immunohistochemically confirmed as HR+/Her-2+ between January 2019 and December 2024, additional immunohistochemical staining for CD8A, KPT5, GFRA1, and PFKP should be performed, and the immunohistochemical images and results should be collected.
Key inclusion criteria (3 listed)
- Patients pathologically diagnosed with breast cancer or excluded from breast cancer
- Available pathological results of breast masses
- Involving diagnostic population onl
Key exclusion criteria (6 listed)
- Suffering from mental disorders
- Presence of non-breast diseases during examination
- Presence of breast implants
- Undergoing non-breast surgery or having received radiotherapy/chemotherapy
- Lactating or pregnant women
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 |
|---|---|---|
| bulid primary AI modelFor the collected patient data, deep learning is used to perform feature screening on the selected or collected images, and malignant risk factors are determined by combining clinical significance. A neural network classifier is trained on the training set data. Variable selection: independent variables (breast MRI images, breast ultrasound images, indicators such as CA199, CA153, CA125, AFP/CEA, etc.), dependent variables (whether suffering from breast cancer and breast cancer subtypes), and the verification accuracy standard is set as the pathological biopsy result. | Other | training group |
| verdict model and develop its functionThe accuracy of a breast cancer prediction model is typically evaluated using multiple metrics that assess its performance in different aspects | Other | verdict group |
Primary outcome
AUC (Area Under the ROC Curve)
Time frame: Baseline-AUC1 Perioperative/Periprocedural-AUC2
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 14 Jul 2025.
- 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.
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, Metastatic and Artifical Intelligence, run by Daping Hospital and the Research Institute of Surgery of the Third Military Medical University 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 3 inclusion and 6 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 14 Jul 2025. 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?
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 Xu Yan (8615923100038, xy931@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
AUC (Area Under the ROC Curve) (time frame: Baseline-AUC1 Perioperative/Periprocedural-AUC2). 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 14 Jul 2025.