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Related Concept Videos

Cancer Survival Analysis01:21

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Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
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Updated: Apr 15, 2026

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
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Analytical Validation of Multimodal AI Test Predicting Breast Cancer Recurrence Risk (Ataraxis Breast RISK).

Marc Dantone1, Martin Lacsamana1, Ken G Zeng1

  • 1Ataraxis AI, New York, NY 10016, USA.

Diagnostics (Basel, Switzerland)
|April 14, 2026
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Summary
This summary is machine-generated.

A new artificial intelligence (AI) test, Ataraxis Breast RISK (ATX), accurately predicts breast cancer recurrence risk using digital pathology images, offering a faster alternative to traditional gene expression tests.

Keywords:
analytical validationartificial intelligencebreast cancerdigital pathology

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Area of Science:

  • Digital Pathology and Artificial Intelligence
  • Oncology and Cancer Biomarkers
  • Medical Diagnostics and Prognostics

Background:

  • Traditional gene expression tests for breast cancer recurrence risk are time-consuming and tissue-intensive.
  • Artificial intelligence (AI) applied to digital pathology images offers a novel approach to identify prognostic morphological biomarkers.
  • AI model validation requires a specialized analytical approach distinct from conventional methods.

Purpose of the Study:

  • To report the analytical validation of a novel artificial intelligence-based breast cancer prognostic test, Ataraxis Breast RISK (ATX).
  • To assess the performance and reliability of ATX across multiple validation axes.
  • To confirm the clinical readiness of ATX for integration into diagnostic workflows.

Main Methods:

  • ATX utilizes a survival analysis model incorporating morphological features extracted from H&E-stained slides by a pan-cancer foundation model.
  • The model integrates extracted features with clinical variables to generate a calibrated breast cancer recurrence risk score.
  • Validation encompassed intra-operator repeatability, inter-operator reproducibility, limit of blank, limit of detection, inter-laboratory reproducibility, data perturbation robustness, and a clinical validation bridging study in CLIA-certified laboratories.

Main Results:

  • Exceptional intra-operator (ICC 0.99, 100% agreement) and inter-operator (ICC 0.99, 100% agreement) repeatability was achieved.
  • High inter-laboratory reproducibility (ICC 0.97, 94.7% agreement) was demonstrated across multiple scanners.
  • ATX maintained robust performance under simulated data perturbations (average C-index 0.62, 90.0% agreement) and showed comparable performance in the bridging study (C-index 0.63 vs. 0.62).

Conclusions:

  • The Ataraxis Breast RISK (ATX) test successfully met all predefined analytical acceptance criteria.
  • The validation results provide strong evidence for the analytical readiness of ATX for clinical application.
  • AI-driven digital pathology offers a promising, efficient, and reliable tool for breast cancer recurrence risk stratification.