Prospective Validation of Ataraxis AI Test for Predicting Treatment Response in Neoadjuvant Breast Cancer (ATARAXIS NEOP)

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.

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 Overview

• Status: Recruiting
• Conditions: Breast Cancer
• Intervention / Treatment: Diagnostic test: multi-modal foundation AI test

• Study Type: Observational
• Enrollment (Estimated): 150

Contacts and Locations

• Study Contact: Name: Young Joon Kang, Ph.D.; Phone Number: +82322805179; Email: yjkang.md@gmail.com

Study Locations

• South Korea
  • Incheon, South Korea, 21431
    • Recruiting
    • Incheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea
    • Contact: Young Joon Kang; Phone Number: 01026383847; Email: yjkang.md@gmail.com

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• 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.

Description

Inclusion Criteria:

• 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

Exclusion Criteria:

• 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

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort

Intervention / Treatment

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; 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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 Outcome Measures

Outcome Measure	Measure Description	Time Frame
Subtype-specific pCR Prediction Accuracy	AUC-ROC for pCR prediction analyzed separately for each molecular subtype: TNBC, HER2+, and HR+/HER2-. Descriptive statistics only.	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.	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.	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.	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.	Within 4 weeks after surgery following NAC completion (approximately 5-7 months per participant)

Collaborators and Investigators

• Sponsor: Young-Joon Kang

Study record dates

Study Major Dates

• Study Start (Actual): January 20, 2026
• Primary Completion (Estimated): August 31, 2027
• Study Completion (Estimated): December 31, 2027

Study Registration Dates

• First Submitted: December 25, 2025
• First Submitted That Met QC Criteria: December 25, 2025
• First Posted (Actual): January 8, 2026

Study Record Updates

• Last Update Posted (Actual): March 27, 2026
• Last Update Submitted That Met QC Criteria: March 23, 2026
• Last Verified: March 1, 2026

More Information

Terms related to this study

• Keywords: Artificial Intelligence; Breast Neoplasms; Neoadjuvant Chemotherapy; Pathological Complete Response; Digital Pathology; Foundation Model; Treatment Response Prediction
• Additional Relevant MeSH Terms: Neoplasms by Site; Neoplasms; Skin Diseases; Breast Diseases; Skin and Connective Tissue Diseases; Breast Neoplasms
• Other Study ID Numbers: ATARAXIS NEOP

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No