Breast NeoplasmsBUST-AI BenchNCT07500428
Construction of a Benchmark for Breast Ultrasound AI Interpretation and Performance Evaluation of Multimodal AI Models
An observational study of Breast Neoplasms and Breast Diseases, run by Peking Union Medical College Hospital at 1 site in China.
Trial summary
This single-center, retrospective, observational study aims to construct a standardized benchmark evaluation system for intelligent breast ultrasound image interpretation and to systematically assess the diagnostic performance of current mainstream multimodal artificial intelligence (AI) models. Read more about this study
- Who is it for?
- Ages 18 to 75 years, women onlyHealthy volunteers can take part
- Conditions the study is for
- Breast Neoplasms, Breast Diseases, Ultrasonography
- What is being tested?
- Multimodal AI Model Diagnostic Evaluation
- How many people
- 1,380 participants
- Where
- 1 siteChina
- Study dates
- 12 Mar 2026to 1 Mar 2027
- 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 18 to 75 years
- Women only
- Healthy volunteers accepted
- Adult, Older adult
Study population: De-identified breast ultrasound images from adult patients who underwent breast ultrasound examination at Peking Union Medical College Hospital between 2018 and 2025 with subsequent pathological confirmation, supplemented by images from published, ethics-approved, open-access breast ultrasound datasets (e.g., BUSI, BrEaST).
Who may be able to join
- B-mode breast ultrasound grayscale images from the institutional PACS database or from published open-access breast ultrasound datasets with documented original institutional ethics approval
- Image quality adequate for clinical diagnosis with clear visualization of the region of interest
- Pathological diagnosis confirmed (for benign and malignant lesion groups), or normal breast status confirmed by a senior radiologist with >15 years of breast ultrasound experience (for the normal group)
- Complete de-identification with removal of all personally identifiable information
Who cannot join
- Severely degraded image quality precluding meaningful BI-RADS assessment
- Duplicate images from the same patient (only the most representative image retained per lesion)
- Images with residual personally identifiable information after de-identification processing
- Cases with ambiguous, disputed, or unavailable pathological results
- Non-B-mode ultrasound images, including elastography, contrast-enhanced ultrasound, and Doppler imaging
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, 1 recruiting
- Recruiting
Peking Union Medical College Hospital
Beijing, 100730
Qingli Zhu, MD, +86 13621376699, zqlpumch@126.com
About this study
Official title: Construction of a Standardized Benchmark Evaluation System for Intelligent Breast Ultrasound Image Interpretation and Systematic Performance Assessment of Multimodal Artificial Intelligence Models Based on ACR BI-RADS v2025 Criteria
This single-center, retrospective, observational study aims to construct a standardized benchmark evaluation system for intelligent breast ultrasound image interpretation and to systematically assess the diagnostic performance of current mainstream multimodal artificial intelligence (AI) models.
De-identified B-mode breast ultrasound images with confirmed pathological diagnoses will be retrospectively collected from the institutional archive (2018-2025) and supplemented with images from published open-access datasets. Expert radiologists with varying experience levels will independently annotate all images according to the American College of Radiology (ACR) Breast Imaging Reporting and Data System (BI-RADS) v2025 criteria, including glandular tissue composition, lesion characterization (mass vs. non-mass lesion), morphological descriptors, and final BI-RADS classification.
Read more
Baseline deep learning models (CNN-based ResNet-50 and Transformer-based USFM) will be trained to establish performance baselines and to stratify cases by diagnostic difficulty through cross-architecture consensus. Multiple multimodal large language models (MLLMs), including both general-purpose and medical-domain models, will then be evaluated via standardized API calls using BI-RADS-guided chain-of-thought prompts at temperature 0 for reproducibility.
Primary endpoints include BI-RADS classification accuracy and diagnostic AUC for benign-malignant differentiation. Model robustness and safety will be assessed through out-of-distribution rejection testing, temperature-stability experiments, and thinking-mode ablation studies. This study adheres to the FLAIR and TRIPOD-LLM reporting guidelines.
Read the detailed description
Background: Breast cancer is the most prevalent malignancy among women worldwide. Ultrasound is a first-line screening modality, particularly in Asian populations with dense breast tissue where mammographic sensitivity is limited. However, ultrasound interpretation is highly operator-dependent, with substantial inter-observer variability in BI-RADS classification, especially for category 4A-4B lesions. Multimodal large language models (MLLMs) have emerged as a promising tool for medical image analysis due to their zero-shot diagnostic capability, interpretable chain-of-thought reasoning, and structured report generation. Nevertheless, there is currently no standardized benchmark for evaluating AI performance in breast ultrasound interpretation.
Study Design: Approximately 1,380 breast ultrasound images will be curated (1,200 evaluation set + 150 out-of-distribution safety test set + 30 prompt development set), encompassing three diagnostic categories: normal breast, benign lesions (BI-RADS 2-4B), and malignant lesions (BI-RADS 3-5). Two junior radiologists (<5 years of experience) and two senior radiologists (>15 years) will independently annotate images per ACR BI-RADS v2025 with arbitration by a fifth expert for discordant cases.
Diagnostic difficulty will be stratified into three tiers using cross-architecture deep learning consensus: Tier 1 (straightforward, both models correct), Tier 2 (equivocal, one correct/one incorrect), and Tier 3 (difficult, both incorrect, with senior expert validation). MLLMs will be evaluated across multiple dimensions: classification accuracy, sensitivity, specificity, F1 score, AUC, Cohen's kappa agreement with expert consensus, expected calibration error (ECE), morphological feature description accuracy, and chain-of-thought reasoning quality.
Safety Assessment: (1) Out-of-distribution rejection test using 150 non-diagnostic images (degraded images, non-breast ultrasound, other imaging modalities); (2) Temperature-stability pre-experiment across parameter settings; (3) Thinking-mode ablation comparing standard vs. chain-of-thought reasoning modes. All experiments use fixed model snapshots, system fingerprint monitoring, and complete logging for reproducibility.
- Conditions
- Breast Neoplasms, Breast Diseases, Ultrasonography
- Being tested
- Multimodal AI Model Diagnostic Evaluation
- Keywords
- Breast Ultrasound, BI-RADS, Artificial Intelligence, Multimodal Large Language Model, Benchmark, Computer-Aided Diagnosis
- Medical subject headings
- Breast Neoplasms, Breast Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Multimodal AI Model Diagnostic EvaluationDiagnostic test
Retrospective evaluation of de-identified breast ultrasound images by multiple AI systems, including baseline deep learning models (ResNet-50, USFM) and multimodal large language models, using standardized BI-RADS-guided chain-of-thought prompts via API. No patient contact or clinical decision-making is involved.
| Study group | Role | What they receive |
|---|---|---|
| Normal Breast | Breast ultrasound images showing normal glandular tissue across different tissue composition types, with no focal lesions identified. Confirmed by senior radiologist review. Receives: Diagnostic Test: Multimodal AI Model Diagnostic Evaluation | |
| Benign Lesion | Breast ultrasound images containing pathologically confirmed benign lesions (BI-RADS 2-4B), including fibroadenoma, cyst, lipoma, sclerosing adenosis, intraductal papilloma, and selected non-mass lesions (NML). Receives: Diagnostic Test: Multimodal AI Model Diagnostic Evaluation | |
| Malignant Lesion | Breast ultrasound images containing pathologically confirmed malignant lesions (BI-RADS 3-5), including invasive ductal carcinoma, invasive lobular carcinoma, mucinous carcinoma, and selected non-mass lesions (NML). Receives: Diagnostic Test: Multimodal AI Model Diagnostic Evaluation |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic Accuracy for Pathological Diagnosis
Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1 score of AI models for benign-malignant classification, with histopathological diagnosis as the gold standard.
Measured: At study completion, approximately 12 months
BI-RADS Classification Accuracy
Overall accuracy of AI models in assigning BI-RADS categories (2, 3, 4A, 4B, 4C, 5) to breast ultrasound images, compared with expert consensus annotation as the reference standard.
Measured: At study completion, approximately 12 months
Secondary outcomes (3)
Agreement with Expert Consensus (Cohen's Kappa)
Cohen's kappa coefficient measuring agreement between each AI model's BI-RADS classification and the expert consensus annotation, reported with 95% confidence intervals.
Measured: At study completion, approximately 12 months
Out-of-Distribution Rejection Rate
Proportion of non-diagnostic images (degraded quality, non-breast ultrasound, other imaging modalities) correctly identified and refused by AI models, evaluating domain safety.
Measured: At study completion, approximately 12 months
Sensitivity, Specificity, PPV, NPV, and F1 Score
Standard diagnostic performance metrics for benign-malignant classification, reported for each AI model individually.
Measured: At study completion, approximately 12 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted30 Mar 2026
- Study start12 Mar 2026
- Primary completion1 Dec 2026 (estimated)
- Study completion1 Mar 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (12)Papers the sponsor has linked to this study.
- Other trials for Breast NeoplasmsStudies 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
- Retrospective
- Enrolment
- 1,380 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Peking Union Medical College Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- Chinese Academy of Medical Sciences
- Responsible party
- Principal investigator, Qingli Zhu, Peking Union Medical College Hospital
- Investigators
- Qingli Zhu, MD, Principal investigator, Peking Union Medical College Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07500428
- Sponsor's protocol ID
- K10349
- Other IDs
- 2024-I2M-CT-B-035, I-26PJ0568
- Acronym
- BUST-AI Bench
- Status last verified
- Mar 2026
- First submitted
- 24 Mar 2026
- Last updated
- 30 Mar 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: The de-identified benchmark evaluation dataset, including expert-annotated breast ultrasound images with paired BI-RADS reading reports, is planned for public release to promote academic reproducibility and collaborative research.
Publications
Bi WL, Hosny A, Schabath MB, Giger ML, Birkbak NJ, Mehrtash A, Allison T, Arnaout O, Abbosh C, Dunn IF, Mak RH, Tamimi RM, Tempany CM, Swanton C, Hoffmann U, Schwartz LH, Gillies RJ, Huang RY, Aerts HJWL. Artificial intelligence in cancer imaging: Clinical challenges and applications. CA Cancer J Clin. 2019 Mar;69(2):127-157. doi: 10.3322/caac.21552. Epub 2019 Feb 5.
PubMed 30720861Bhayana R, Krishna S, Bleakney RR. Performance of ChatGPT on a Radiology Board-style Examination: Insights into Current Strengths and Limitations. Radiology. 2023 Jun;307(5):e230582. doi: 10.1148/radiol.230582. Epub 2023 May 16.
PubMed 37191485Clusmann J, Kolbinger FR, Muti HS, Carrero ZI, Eckardt JN, Laleh NG, Loffler CML, Schwarzkopf SC, Unger M, Veldhuizen GP, Wagner SJ, Kather JN. The future landscape of large language models in medicine. Commun Med (Lond). 2023 Oct 10;3(1):141. doi: 10.1038/s43856-023-00370-1.
PubMed 37816837Seyyed-Kalantari L, Zhang H, McDermott MBA, Chen IY, Ghassemi M. Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations. Nat Med. 2021 Dec;27(12):2176-2182. doi: 10.1038/s41591-021-01595-0. Epub 2021 Dec 10.
PubMed 34893776Moor M, Banerjee O, Abad ZSH, Krumholz HM, Leskovec J, Topol EJ, Rajpurkar P. Foundation models for generalist medical artificial intelligence. Nature. 2023 Apr;616(7956):259-265. doi: 10.1038/s41586-023-05881-4. Epub 2023 Apr 12.
PubMed 37045921Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021 May;71(3):209-249. doi: 10.3322/caac.21660. Epub 2021 Feb 4.
PubMed 33538338Benary M, Wang XD, Schmidt M, Soll D, Hilfenhaus G, Nassir M, Sigler C, Knodler M, Keller U, Beule D, Keilholz U, Leser U, Rieke DT. Leveraging Large Language Models for Decision Support in Personalized Oncology. JAMA Netw Open. 2023 Nov 1;6(11):e2343689. doi: 10.1001/jamanetworkopen.2023.43689.
PubMed 37976064Miaojiao S, Xia L, Xian Tao Z, Zhi Liang H, Sheng C, Songsong W. Using a Large Language Model for Breast Imaging Reporting and Data System Classification and Malignancy Prediction to Enhance Breast Ultrasound Diagnosis: Retrospective Study. JMIR Med Inform. 2025 Jun 11;13:e70924. doi: 10.2196/70924.
PubMed 40498674Jiao J, Zhou J, Li X, Xia M, Huang Y, Huang L, Wang N, Zhang X, Zhou S, Wang Y, Guo Y. USFM: A universal ultrasound foundation model generalized to tasks and organs towards label efficient image analysis. Med Image Anal. 2024 Aug;96:103202. doi: 10.1016/j.media.2024.103202. Epub 2024 May 15.
PubMed 38788326Xiang H, Wang X, Xu M, Zhang Y, Zeng S, Li C, Liu L, Deng T, Tang G, Yan C, Ou J, Lin Q, He J, Sun P, Li A, Chen H, Heng PA, Lin X. Deep Learning-assisted Diagnosis of Breast Lesions on US Images: A Multivendor, Multicenter Study. Radiol Artif Intell. 2023 Jul 12;5(5):e220185. doi: 10.1148/ryai.220185. eCollection 2023 Sep.
PubMed 37795135Collins GS, Moons KGM, Dhiman P, Riley RD, Beam AL, Van Calster B, Ghassemi M, Liu X, Reitsma JB, van Smeden M, Boulesteix AL, Camaradou JC, Celi LA, Denaxas S, Denniston AK, Glocker B, Golub RM, Harvey H, Heinze G, Hoffman MM, Kengne AP, Lam E, Lee N, Loder EW, Maier-Hein L, Mateen BA, McCradden MD, Oakden-Rayner L, Ordish J, Parnell R, Rose S, Singh K, Wynants L, Logullo P. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ. 2024 Apr 16;385:e078378. doi: 10.1136/bmj-2023-078378.
PubMed 38626948Kottlors J, Iuga AI, Bluethgen C, Bressem K, Kather JN, Moy L, Wald C, Wang W, Liu T, Ranschaert E, Dratsch T, Kleesiek J, Gertz RJ, Rajpurkar P, Bedayat A, Fink MA, Zeeck A, Chaudhari A, Alkasab T, Wu H, Nensa F, Wang B, Grosse Hokamp N, Laukamp KR, Persigehl T, Maintz D, Truhn D, Lennartz S. Guidelines for Reporting Studies on Large Language Models in Radiology: An International Delphi Expert Survey. Radiology. 2026 Feb;318(2):e250913. doi: 10.1148/radiol.250913.
PubMed 41631991
Study at a glance
From the registry record, last updated by the sponsor on 30 Mar 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Construction of a Standardized Benchmark Evaluation System for Intelligent Breast Ultrasound Image Interpretation and Systematic Performance Assessment of Multimodal Artificial Intelligence Models Based on ACR BI-RADS v2025 Criteria
- ClinicalTrials.gov ID
- NCT07500428
- Acronym / sponsor ID
- BUST-AI Bench / K10349
- Conditions
- Breast Neoplasms, Breast Diseases, Ultrasonography
- Keywords
- Breast Ultrasound, BI-RADS, Artificial Intelligence, Multimodal Large Language Model, Benchmark, Computer-Aided Diagnosis
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 1,380 participants (estimated)
- Lead sponsor
- Peking Union Medical College HospitalUniversity, hospital or other
- Collaborators
- Chinese Academy of Medical Sciences
- Study start
- 12 Mar 2026 (actual)
- Primary completion
- 1 Dec 2026 (estimated)
- Study completion
- 1 Mar 2027 (estimated)
- First posted
- 30 Mar 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 single-center, retrospective, observational study aims to construct a standardized benchmark evaluation system for intelligent breast ultrasound image interpretation and to systematically assess the diagnostic performance of current mainstream multimodal artificial intelligence (AI) models.
De-identified B-mode breast ultrasound images with confirmed pathological diagnoses will be retrospectively collected from the institutional archive (2018-2025) and supplemented with images from published open-access datasets. Expert radiologists with varying experience levels will independently annotate all images according to the American College of Radiology (ACR) Breast Imaging Reporting and Data System (BI-RADS) v2025 criteria, including glandular tissue composition, lesion characterization (mass vs. non-mass lesion), morphological descriptors, and final BI-RADS classification.
Baseline deep learning models (CNN-based ResNet-50 and Transformer-based USFM) will be trained to establish performance baselines and to stratify cases by diagnostic difficulty through cross-architecture consensus. Multiple multimodal large language models (MLLMs), including both general-purpose and medical-domain models, will then be evaluated via standardized API calls using BI-RADS-guided chain-of-thought prompts at temperature 0 for reproducibility.
Primary endpoints include BI-RADS classification accuracy and diagnostic AUC for benign-malignant differentiation. Model robustness and safety will be assessed through out-of-distribution rejection testing, temperature-stability experiments, and thinking-mode ablation studies. This study adheres to the FLAIR and TRIPOD-LLM reporting guidelines.
Detailed description
Background: Breast cancer is the most prevalent malignancy among women worldwide. Ultrasound is a first-line screening modality, particularly in Asian populations with dense breast tissue where mammographic sensitivity is limited. However, ultrasound interpretation is highly operator-dependent, with substantial inter-observer variability in BI-RADS classification, especially for category 4A-4B lesions. Multimodal large language models (MLLMs) have emerged as a promising tool for medical image analysis due to their zero-shot diagnostic capability, interpretable chain-of-thought reasoning, and structured report generation. Nevertheless, there is currently no standardized benchmark for evaluating AI performance in breast ultrasound interpretation.
Study Design: Approximately 1,380 breast ultrasound images will be curated (1,200 evaluation set + 150 out-of-distribution safety test set + 30 prompt development set), encompassing three diagnostic categories: normal breast, benign lesions (BI-RADS 2-4B), and malignant lesions (BI-RADS 3-5). Two junior radiologists (<5 years of experience) and two senior radiologists (>15 years) will independently annotate images per ACR BI-RADS v2025 with arbitration by a fifth expert for discordant cases.
Diagnostic difficulty will be stratified into three tiers using cross-architecture deep learning consensus: Tier 1 (straightforward, both models correct), Tier 2 (equivocal, one correct/one incorrect), and Tier 3 (difficult, both incorrect, with senior expert validation). MLLMs will be evaluated across multiple dimensions: classification accuracy, sensitivity, specificity, F1 score, AUC, Cohen's kappa agreement with expert consensus, expected calibration error (ECE), morphological feature description accuracy, and chain-of-thought reasoning quality.
Safety Assessment: (1) Out-of-distribution rejection test using 150 non-diagnostic images (degraded images, non-breast ultrasound, other imaging modalities); (2) Temperature-stability pre-experiment across parameter settings; (3) Thinking-mode ablation comparing standard vs. chain-of-thought reasoning modes. All experiments use fixed model snapshots, system fingerprint monitoring, and complete logging for reproducibility.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Normal Breast | – | Diagnostic Test: Multimodal AI Model Diagnostic EvaluationBreast ultrasound images showing normal glandular tissue across different tissue composition types, with no focal lesions identified. Confirmed by senior radiologist review. |
| Benign Lesion | – | Diagnostic Test: Multimodal AI Model Diagnostic EvaluationBreast ultrasound images containing pathologically confirmed benign lesions (BI-RADS 2-4B), including fibroadenoma, cyst, lipoma, sclerosing adenosis, intraductal papilloma, and selected non-mass lesions (NML). |
| Malignant Lesion | – | Diagnostic Test: Multimodal AI Model Diagnostic EvaluationBreast ultrasound images containing pathologically confirmed malignant lesions (BI-RADS 3-5), including invasive ductal carcinoma, invasive lobular carcinoma, mucinous carcinoma, and selected non-mass lesions (NML). |
Who may qualify
- Ages
- 18 to 75 years
- Sex
- Female only
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- De-identified breast ultrasound images from adult patients who underwent breast ultrasound examination at Peking Union Medical College Hospital between 2018 and 2025 with subsequent pathological confirmation, supplemented by images from published, ethics-approved, open-access breast ultrasound datasets (e.g., BUSI, BrEaST).
Key inclusion criteria (4 listed)
- B-mode breast ultrasound grayscale images from the institutional PACS database or from published open-access breast ultrasound datasets with documented original institutional ethics approval
- Image quality adequate for clinical diagnosis with clear visualization of the region of interest
- Pathological diagnosis confirmed (for benign and malignant lesion groups), or normal breast status confirmed by a senior radiologist with >15 years of breast ultrasound experience (for the normal group)
- Complete de-identification with removal of all personally identifiable information
Key exclusion criteria (5 listed)
- Severely degraded image quality precluding meaningful BI-RADS assessment
- Duplicate images from the same patient (only the most representative image retained per lesion)
- Images with residual personally identifiable information after de-identification processing
- Cases with ambiguous, disputed, or unavailable pathological results
- Non-B-mode ultrasound images, including elastography, contrast-enhanced ultrasound, and Doppler imaging
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 |
|---|---|---|
| Multimodal AI Model Diagnostic EvaluationRetrospective evaluation of de-identified breast ultrasound images by multiple AI systems, including baseline deep learning models (ResNet-50, USFM) and multimodal large language models, using standardized BI-RADS-guided chain-of-thought prompts via API. No patient contact or clinical decision-making is involved. | Diagnostic test | Benign Lesion, Malignant Lesion, Normal Breast |
Primary outcome
Diagnostic Accuracy for Pathological Diagnosis
Time frame: At study completion, approximately 12 months
Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1 score of AI models for benign-malignant classification, with histopathological diagnosis as the gold standard.
BI-RADS Classification Accuracy
Time frame: At study completion, approximately 12 months
Overall accuracy of AI models in assigning BI-RADS categories (2, 3, 4A, 4B, 4C, 5) to breast ultrasound images, compared with expert consensus annotation as the reference standard.
3 secondary outcomes
Agreement with Expert Consensus (Cohen's Kappa)
Time frame: At study completion, approximately 12 months
Out-of-Distribution Rejection Rate
Time frame: At study completion, approximately 12 months
Sensitivity, Specificity, PPV, NPV, and F1 Score
Time frame: At study completion, approximately 12 months
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 30 Mar 2026.
- Background: PubMed 30720861Bi WL, Hosny A, Schabath MB, Giger ML, Birkbak NJ, Mehrtash A, Allison T, Arnaout O, Abbosh C, Dunn IF, Mak RH, Tamimi RM, Tempany CM, Swanton C, Hoffmann U, Schwartz LH, Gillies RJ, Huang RY, Aerts HJWL. Artificial intelligence in cancer imaging: Clinical challenges and applications. CA Cancer J Clin. 2019 Mar;69(2):127-157. doi: 10.3322/caac.21552. Epub 2019 Feb 5.
- Background: PubMed 37191485Bhayana R, Krishna S, Bleakney RR. Performance of ChatGPT on a Radiology Board-style Examination: Insights into Current Strengths and Limitations. Radiology. 2023 Jun;307(5):e230582. doi: 10.1148/radiol.230582. Epub 2023 May 16.
- Background: PubMed 37816837Clusmann J, Kolbinger FR, Muti HS, Carrero ZI, Eckardt JN, Laleh NG, Loffler CML, Schwarzkopf SC, Unger M, Veldhuizen GP, Wagner SJ, Kather JN. The future landscape of large language models in medicine. Commun Med (Lond). 2023 Oct 10;3(1):141. doi: 10.1038/s43856-023-00370-1.
- Background: PubMed 34893776Seyyed-Kalantari L, Zhang H, McDermott MBA, Chen IY, Ghassemi M. Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations. Nat Med. 2021 Dec;27(12):2176-2182. doi: 10.1038/s41591-021-01595-0. Epub 2021 Dec 10.
- Background: PubMed 37045921Moor M, Banerjee O, Abad ZSH, Krumholz HM, Leskovec J, Topol EJ, Rajpurkar P. Foundation models for generalist medical artificial intelligence. Nature. 2023 Apr;616(7956):259-265. doi: 10.1038/s41586-023-05881-4. Epub 2023 Apr 12.
- Background: PubMed 33538338Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021 May;71(3):209-249. doi: 10.3322/caac.21660. Epub 2021 Feb 4.
- Background: PubMed 37976064Benary M, Wang XD, Schmidt M, Soll D, Hilfenhaus G, Nassir M, Sigler C, Knodler M, Keller U, Beule D, Keilholz U, Leser U, Rieke DT. Leveraging Large Language Models for Decision Support in Personalized Oncology. JAMA Netw Open. 2023 Nov 1;6(11):e2343689. doi: 10.1001/jamanetworkopen.2023.43689.
- Background: PubMed 40498674Miaojiao S, Xia L, Xian Tao Z, Zhi Liang H, Sheng C, Songsong W. Using a Large Language Model for Breast Imaging Reporting and Data System Classification and Malignancy Prediction to Enhance Breast Ultrasound Diagnosis: Retrospective Study. JMIR Med Inform. 2025 Jun 11;13:e70924. doi: 10.2196/70924.
- Background: PubMed 38788326Jiao J, Zhou J, Li X, Xia M, Huang Y, Huang L, Wang N, Zhang X, Zhou S, Wang Y, Guo Y. USFM: A universal ultrasound foundation model generalized to tasks and organs towards label efficient image analysis. Med Image Anal. 2024 Aug;96:103202. doi: 10.1016/j.media.2024.103202. Epub 2024 May 15.
- Background: PubMed 37795135Xiang H, Wang X, Xu M, Zhang Y, Zeng S, Li C, Liu L, Deng T, Tang G, Yan C, Ou J, Lin Q, He J, Sun P, Li A, Chen H, Heng PA, Lin X. Deep Learning-assisted Diagnosis of Breast Lesions on US Images: A Multivendor, Multicenter Study. Radiol Artif Intell. 2023 Jul 12;5(5):e220185. doi: 10.1148/ryai.220185. eCollection 2023 Sep.
- Background: PubMed 38626948Collins GS, Moons KGM, Dhiman P, Riley RD, Beam AL, Van Calster B, Ghassemi M, Liu X, Reitsma JB, van Smeden M, Boulesteix AL, Camaradou JC, Celi LA, Denaxas S, Denniston AK, Glocker B, Golub RM, Harvey H, Heinze G, Hoffman MM, Kengne AP, Lam E, Lee N, Loder EW, Maier-Hein L, Mateen BA, McCradden MD, Oakden-Rayner L, Ordish J, Parnell R, Rose S, Singh K, Wynants L, Logullo P. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ. 2024 Apr 16;385:e078378. doi: 10.1136/bmj-2023-078378.
- Background: PubMed 41631991Kottlors J, Iuga AI, Bluethgen C, Bressem K, Kather JN, Moy L, Wald C, Wang W, Liu T, Ranschaert E, Dratsch T, Kleesiek J, Gertz RJ, Rajpurkar P, Bedayat A, Fink MA, Zeeck A, Chaudhari A, Alkasab T, Wu H, Nensa F, Wang B, Grosse Hokamp N, Laukamp KR, Persigehl T, Maintz D, Truhn D, Lennartz S. Guidelines for Reporting Studies on Large Language Models in Radiology: An International Delphi Expert Survey. Radiology. 2026 Feb;318(2):e250913. doi: 10.1148/radiol.250913.
- Individual participant dataSharing planned: Yes. The de-identified benchmark evaluation dataset, including expert-annotated breast ultrasound images with paired BI-RADS reading reports, is planned for public release to promote academic reproducibility and collaborative research.
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Anatomic Stage II Breast Cancer AJCC v8, Anatomic Stage IIIA Breast Cancer AJCC v8
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.
Qingli Zhu, MD principal investigator
Peking Union Medical College 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 Neoplasms and Breast Diseases, 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 4 inclusion and 5 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: looking for participants now, as of the sponsor's last update on 30 Mar 2026. 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 Qingli Zhu, MD (+86 13621376699, zqlpumch@126.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnostic Accuracy for Pathological Diagnosis (time frame: At study completion, approximately 12 months). 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 NCT07500428, which also lists 12 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 30 Mar 2026.