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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:
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| 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. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| 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. |
| 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) |
| 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 |
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Inclusion Criteria:
Exclusion Criteria:
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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.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Young Joon Kang, Ph.D. | Contact | +82322805179 | yjkang.md@gmail.com |
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Incheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea | Recruiting | Incheon | 21431 | South Korea |
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| ID | Term |
|---|---|
| D001943 | Breast Neoplasms |
| ID | Term |
|---|---|
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
| D001941 | Breast Diseases |
| D012871 | Skin Diseases |
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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.
|
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) |
| D017437 |
| Skin and Connective Tissue Diseases |