Prospective Validation of Ataraxis AI Test for Predicting Treatment Response in Neoadjuvant Breast Cancer

NCT07327970 | Recruiting

• 1 other identifier: ATARAXIS NEOP
• Study Type: observational
• Target: 150
• Locations: 1 country
• Sites: 1

Brief 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. 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.

Conditions

Breast Cancer

Keywords

Artificial Intelligence; Digital Pathology; Neoadjuvant Chemotherapy; Pathological Complete Response; Foundation Model; Treatment Response Prediction; Breast Neoplasms

Outcome Measures

Primary Outcomes (2)

• 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.

  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).

  Within 4 weeks after surgery following NAC completion (approximately 5-7 months per participant)

Secondary Outcomes (5)

• Subtype-specific pCR Prediction Accuracy

  Within 4 weeks after surgery following NAC completion (approximately 5-7 months per participant)

• Sensitivity and Specificity of pCR Prediction

  Within 4 weeks after surgery following NAC completion (approximately 5-7 months per participant)

• AI Test Processing Time

  Within 2 weeks after enrollment

• Hypothetical Treatment Change Rate

  After AI result disclosure following surgery (approximately 5-7 months per participant)

• Correlation Between AI Score and Established Prognostic Factors

  Within 4 weeks after surgery following NAC completion (approximately 5-7 months per participant)

Study Arms (1)

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.

Diagnostic Test: multi-modal foundation AI test

Interventions

multi-modal foundation AI test DIAGNOSTIC_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.

NAC Patients with AI Assessment

Eligibility Criteria

• Age: 18 Years+
• Sex: female
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

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.

You may qualify if:

• 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

You may not qualify if:

• 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

Sponsors & Collaborators

• Young-Joon Kang: lead

Study Sites (1)

Incheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea

Incheon, 21431, South Korea

RECRUITING

Biospecimen

Retention: SAMPLES WITHOUT DNA

H\&E-stained histopathology slides from core needle biopsy specimens obtained during routine clinical care. Slides are digitized into whole slide images (WSI) for AI analysis. No additional tissue collection is performed for this study. Physical tissue specimens remain in the pathology department per institutional protocols. Only digitized images are uploaded to the AI analysis platform.

MeSH Terms

Conditions

Breast Neoplasms

Condition Hierarchy (Ancestors)

Neoplasms by Site; Neoplasms; Breast Diseases; Skin Diseases; Skin and Connective Tissue Diseases

Central Study Contacts

Young Joon Kang, Ph.D.

CONTACT

+82322805179; yjkang.md@gmail.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Assistant Professor

Study Record Dates

• First Submitted: December 25, 2025
• First Posted: January 8, 2026
• Study Start: January 20, 2026
• Primary Completion (Estimated): August 31, 2027
• Study Completion (Estimated): December 31, 2027
• Last Updated: March 27, 2026

Record last verified: 2026-03

Data Sharing

• IPD Sharing: Will not share

Locations

KR (1)