Breast CancerATARAXIS NEOPNCT07327970
Prospective Validation of Ataraxis AI Test for Predicting Treatment Response in Neoadjuvant Breast Cancer
An observational study of Breast Cancer, run by Young-Joon Kang at 1 site in South Korea.
Trial summary
This study evaluates the real-world clinical workflow integration of a previously developed artificial intelligence (AI) prognostic test in breast cancer patients receiving neoadjuvant chemotherapy, and validates its accuracy in predicting treatment response. Read more about this study
- Who is it for?
- Ages 18 years and older, women only
- Conditions the study is for
- Breast Cancer
- What is being tested?
- multi-modal foundation AI test
- How many people
- 150 participants
- Where
- 1 siteSouth Korea
- Study dates
- 20 Jan 2026to 31 Dec 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 years and older
- Women only
- No healthy volunteers
- Adult, Older adult
Study population: Adult female patients with Stage I-III invasive breast cancer who are planned for neoadjuvant chemotherapy. Patients must have available H\&E-stained slides from diagnostic biopsy for AI analysis.
Who may be able to join
- Histologically confirmed Stage I-III invasive breast cancer
- Planned for neoadjuvant chemotherapy
- H\&E-stained slides available from core needle biopsy
- Age 18 years or older
- Able to provide written informed consent
Who cannot join
- Metastatic breast cancer (Stage IV)
- Not a candidate for neoadjuvant chemotherapy
- H\&E slides not obtainable from core needle biopsy
- Unable to provide informed consent
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
South Korea1 site, 1 recruiting
- Recruiting
Incheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea
Incheon, 21431
Young Joon Kang, 01026383847, yjkang.md@gmail.com
About this study
Official title: A Prospective Non-Interventional Study Using a Multi-Modal Prognostic Test (Ataraxis) for Evaluating the Clinical Integration in Early-Stage Invasive Breast Cancer
This study evaluates the real-world clinical workflow integration of a previously developed artificial intelligence (AI) prognostic test in breast cancer patients receiving neoadjuvant chemotherapy, and validates its accuracy in predicting treatment response.
The Ataraxis AI test analyzes digitized images of tumor biopsy slides combined with basic clinical information (age, tumor stage, hormone receptor status) to generate a risk score. Prior studies showed the AI test can predict cancer recurrence with accuracy comparable to or better than existing genomic tests.
Read more
The study has two stages:
- Stage 1 (30 patients): Assess whether the AI test can be practically integrated into routine clinical workflow, including ease of use, report clarity, and time requirements.
- Stage 2 (70-120 additional patients): Validate the accuracy of AI-predicted pathological complete response (pCR) rates against actual surgical outcomes.
This study uses a blinded design where treating physicians remain blinded to AI results until post-surgical pCR assessment. AI analysis is performed by the research coordinator in collaboration with Ataraxis. After pCR evaluation, AI results are disclosed and physicians complete surveys assessing hypothetical treatment changes. This design eliminates AI influence on treatment decisions and ensures independent validation.
Participants are adults with Stage I-III breast cancer planned for neoadjuvant chemotherapy. The study involves no additional procedures beyond standard care except for completing surveys about the AI test experience.
- Conditions
- Breast Cancer
- Being tested
- multi-modal foundation AI test
- Keywords
- Artificial Intelligence, Digital Pathology, Neoadjuvant Chemotherapy, Pathological Complete Response, Foundation Model, Treatment Response Prediction, Breast Neoplasms
- Medical subject headings
- Breast Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
multi-modal foundation AI testDiagnostic test
Multi-modal AI test combining digital pathology features from H\&E-stained core needle biopsy slides with clinical information (age, molecular biomarkers, TNM stage) to generate a continuous risk score (0-1) predicting pathological complete response. Results provided as reference information only; does not influence treatment decisions.
| Study group | Role | What they receive |
|---|---|---|
| NAC Patients with AI Assessment | Stage I-III invasive breast cancer patients undergoing neoadjuvant chemotherapy. All participants receive standard-of-care treatment. AI analysis is performed but results remain blinded from treating physicians during NAC. AI results are disclosed only after surgery and pCR assessment for retrospective evaluation. Treatment decisions are made independently of AI results. Receives: Diagnostic Test: multi-modal foundation AI test |
What is being measured
The primary outcome is the main question the study is designed to answer.
Stage 1 - Feasibility: Clinical Workflow Compatibility Score
Mean score on 5-point Likert scale assessing AI system integration into existing clinical workflow, including ease of use, report comprehensibility, credibility, and time burden. Higher scores indicate better compatibility.
Measured: Within 4 weeks after surgery following NAC completion (approximately 5-7 months per participant)
pCR Prediction: pCR Prediction Accuracy (AUC-ROC)
Area under the receiver operating characteristic curve for AI-predicted pCR probability versus actual pathological complete response status (defined as ypT0/is ypN0).
Measured: Within 4 weeks after surgery following NAC completion (approximately 5-7 months per participant)
Secondary outcomes (5)
Subtype-specific pCR Prediction Accuracy
AUC-ROC for pCR prediction analyzed separately for each molecular subtype: TNBC, HER2+, and HR+/HER2-. Descriptive statistics only.
Measured: Within 4 weeks after surgery following NAC completion (approximately 5-7 months per participant)
Sensitivity and Specificity of pCR Prediction
Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for AI-predicted pCR using predefined risk thresholds.
Measured: Within 4 weeks after surgery following NAC completion (approximately 5-7 months per participant)
AI Test Processing Time
Time in days from data upload to Ataraxis platform to AI result receipt.
Measured: Within 2 weeks after enrollment
Hypothetical Treatment Change Rate
Proportion of cases where physicians indicate they would have modified treatment, assessed retrospectively after AI result disclosure following surgery and pCR evaluation. Includes regimen change, pembrolizumab addition/removal, cycle adjustment, or NAC omission.
Measured: After AI result disclosure following surgery (approximately 5-7 months per participant)
Correlation Between AI Score and Established Prognostic Factors
Spearman correlation coefficients between AI risk score and Ki67, tumor grade, clinical T stage, and clinical N stage.
Measured: Within 4 weeks after surgery following NAC completion (approximately 5-7 months per participant)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted8 Jan 2026
- Study start20 Jan 2026
- Primary completion31 Aug 2027 (estimated)
- Study completion31 Dec 2027 (estimated)
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.
- 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
- 150 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Young-Joon Kang
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Young-Joon Kang, Incheon St.Mary's Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07327970
- Sponsor's protocol ID
- ATARAXIS NEOP
- Acronym
- ATARAXIS NEOP
- Status last verified
- Mar 2026
- First submitted
- 25 Dec 2025
- Last updated
- 27 Mar 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 27 Mar 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Prospective Non-Interventional Study Using a Multi-Modal Prognostic Test (Ataraxis) for Evaluating the Clinical Integration in Early-Stage Invasive Breast Cancer
- ClinicalTrials.gov ID
- NCT07327970
- Acronym / sponsor ID
- ATARAXIS NEOP / ATARAXIS NEOP
- Conditions
- Breast Cancer
- Keywords
- Artificial Intelligence, Digital Pathology, Neoadjuvant Chemotherapy, Pathological Complete Response, Foundation Model, Treatment Response Prediction, Breast Neoplasms
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 150 participants (estimated)
- Lead sponsor
- Young-Joon KangUniversity, hospital or other
- Study start
- 20 Jan 2026 (actual)
- Primary completion
- 31 Aug 2027 (estimated)
- Study completion
- 31 Dec 2027 (estimated)
- First posted
- 8 Jan 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 evaluates the real-world clinical workflow integration of a previously developed artificial intelligence (AI) prognostic test in breast cancer patients receiving neoadjuvant chemotherapy, and validates its accuracy in predicting treatment response.
The Ataraxis AI test analyzes digitized images of tumor biopsy slides combined with basic clinical information (age, tumor stage, hormone receptor status) to generate a risk score. Prior studies showed the AI test can predict cancer recurrence with accuracy comparable to or better than existing genomic tests.
The study has two stages:
- Stage 1 (30 patients): Assess whether the AI test can be practically integrated into routine clinical workflow, including ease of use, report clarity, and time requirements.
- Stage 2 (70-120 additional patients): Validate the accuracy of AI-predicted pathological complete response (pCR) rates against actual surgical outcomes.
This study uses a blinded design where treating physicians remain blinded to AI results until post-surgical pCR assessment. AI analysis is performed by the research coordinator in collaboration with Ataraxis. After pCR evaluation, AI results are disclosed and physicians complete surveys assessing hypothetical treatment changes. This design eliminates AI influence on treatment decisions and ensures independent validation.
Participants are adults with Stage I-III breast cancer planned for neoadjuvant chemotherapy. The study involves no additional procedures beyond standard care except for completing surveys about the AI test experience.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| NAC Patients with AI Assessment | – | Diagnostic Test: multi-modal foundation AI testStage I-III invasive breast cancer patients undergoing neoadjuvant chemotherapy. All participants receive standard-of-care treatment. AI analysis is performed but results remain blinded from treating physicians during NAC. AI results are disclosed only after surgery and pCR assessment for retrospective evaluation. Treatment decisions are made independently of AI results. |
Who may qualify
- Ages
- 18 years and older
- Sex
- Female only
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Adult female patients with Stage I-III invasive breast cancer who are planned for neoadjuvant chemotherapy. Patients must have available H\&E-stained slides from diagnostic biopsy for AI analysis.
Key inclusion criteria (5 listed)
- Histologically confirmed Stage I-III invasive breast cancer
- Planned for neoadjuvant chemotherapy
- H\&E-stained slides available from core needle biopsy
- Age 18 years or older
- Able to provide written informed consent
Key exclusion criteria (4 listed)
- Metastatic breast cancer (Stage IV)
- Not a candidate for neoadjuvant chemotherapy
- H\&E slides not obtainable from core needle biopsy
- Unable to provide informed consent
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 |
|---|---|---|
| multi-modal foundation AI testMulti-modal AI test combining digital pathology features from H\&E-stained core needle biopsy slides with clinical information (age, molecular biomarkers, TNM stage) to generate a continuous risk score (0-1) predicting pathological complete response. Results provided as reference information only; does not influence treatment decisions. | Diagnostic test | NAC Patients with AI Assessment |
Primary outcome
Stage 1 - Feasibility: Clinical Workflow Compatibility Score
Time frame: Within 4 weeks after surgery following NAC completion (approximately 5-7 months per participant)
Mean score on 5-point Likert scale assessing AI system integration into existing clinical workflow, including ease of use, report comprehensibility, credibility, and time burden. Higher scores indicate better compatibility.
pCR Prediction: pCR Prediction Accuracy (AUC-ROC)
Time frame: Within 4 weeks after surgery following NAC completion (approximately 5-7 months per participant)
Area under the receiver operating characteristic curve for AI-predicted pCR probability versus actual pathological complete response status (defined as ypT0/is ypN0).
5 secondary outcomes
Subtype-specific pCR Prediction Accuracy
Time frame: Within 4 weeks after surgery following NAC completion (approximately 5-7 months per participant)
Sensitivity and Specificity of pCR Prediction
Time frame: Within 4 weeks after surgery following NAC completion (approximately 5-7 months per participant)
AI Test Processing Time
Time frame: Within 2 weeks after enrollment
Hypothetical Treatment Change Rate
Time frame: After AI result disclosure following surgery (approximately 5-7 months per participant)
Correlation Between AI Score and Established Prognostic Factors
Time frame: Within 4 weeks after surgery following NAC completion (approximately 5-7 months per participant)
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 27 Mar 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.
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 Young-Joon Kang at 1 site in South Korea.
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 4 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 27 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 South Korea. 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 Young Joon Kang, Ph.D. (+82322805179, yjkang.md@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Stage 1 - Feasibility: Clinical Workflow Compatibility Score (time frame: Within 4 weeks after surgery following NAC completion (approximately 5-7 months per participant)). 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?
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 27 Mar 2026.