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GOLDMARK: Governed Outcome-Linked Diagnostic Model Assessment Reference Kit.

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This summary is machine-generated.

We developed GOLDMARK, a standardized framework for computational pathology, to enable reproducible biomarker discovery from whole-slide images using artificial intelligence. This framework improves the reliability and comparability of AI models for predicting therapeutic response.

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Area of Science:

  • Computational pathology
  • Digital pathology
  • Artificial intelligence in medicine

Background:

  • Computational biomarkers (CBs) derived from histopathology whole-slide images (WSIs) using artificial intelligence (AI) show promise for predicting therapeutic response and prognosis.
  • Current computational pathology methods lack standardization in data formats, provenance tracking, and evaluation, hindering clinical translation.
  • Multiple-instance learning (MIL) with pathology foundation models (PFMs) is a common baseline for CB development but requires robust benchmarking.

Purpose of the Study:

  • To introduce GOLDMARK, a standardized benchmarking framework for computational pathology.
  • To provide standardized intermediate data formats, provenance tracking, and reproducible evaluation metrics for AI-driven biomarker discovery.
  • To facilitate direct comparison and validation of computational pathology methods across datasets and models.

Main Methods:

  • GOLDMARK utilizes a curated TCGA cohort with OncoKB level 1-3 biomarker labels for framework development.
  • The framework releases structured intermediate representations, including tile coordinate maps, feature embeddings, metadata, patient splits, trained models, and evaluation outputs.
  • Models are trained on TCGA data and evaluated on an independent MSKCC cohort, with reciprocal testing to ensure robustness.

Main Results:

  • Across 33 tumor-biomarker tasks, GOLDMARK achieved mean AUROCs of 0.689 (TCGA) and 0.630 (MSKCC).
  • The eight highest-performing tasks, corresponding to known morphologic-genomic associations, yielded mean AUROCs of 0.831 (TCGA) and 0.801 (MSKCC), demonstrating stable cross-site performance.
  • Variability between different foundation models was less significant than task-specific performance variations.

Conclusions:

  • GOLDMARK establishes a crucial shared experimental substrate for computational pathology, promoting reproducibility and standardization.
  • The framework enables reliable benchmarking and direct comparison of diverse computational pathology methods.
  • GOLDMARK facilitates the clinical-grade deployment of AI-driven biomarkers by ensuring standardized development and evaluation processes.