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Prioritizing cases from a multi-institutional cohort for a dataset of pathologist annotations.

Victor Garcia1, Emma Gardecki1, Stephanie Jou2

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We developed a method to create a validation dataset for artificial intelligence models assessing tumor-infiltrating lymphocytes in breast cancer. This approach prioritizes underrepresented patient groups for more equitable AI development.

Keywords:
DataPrioritizationSamplingValidation

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Area of Science:

  • Oncology
  • Computational Pathology
  • Artificial Intelligence

Background:

  • Standardized validation datasets are crucial for comparing artificial intelligence and machine learning (AI/ML) models in cancer research.
  • Stromal tumor-infiltrating lymphocytes (sTILs) are important prognostic markers in triple-negative breast cancer (TNBC).
  • Developing robust AI/ML models for sTILs assessment requires high-quality, diverse validation data.

Purpose of the Study:

  • To create a comprehensive validation dataset for AI/ML models assessing sTILs in TNBC.
  • To implement a novel case prioritization method to ensure representation of diverse patient subgroups.
  • To facilitate direct comparison of AI/ML model performance across different research groups.

Main Methods:

  • Obtained whole slide images (WSIs) and clinical metadata for TNBC core biopsies from two academic medical centers.
  • Selected regions of interest (ROIs) targeting diverse tissue morphologies and sTILs densities.
  • Implemented a hierarchical rank-sort method for case prioritization, focusing on underrepresented clinical factors.

Main Results:

  • Compiled data from 122 glass slides across 105 unique TNBC patients.
  • Improved the skewness of sTILs density distribution from 0.60 to 0.46 through case prioritization.
  • Increased the entropy of sTILs density bins from 1.20 to 1.24, enhancing data diversity.

Conclusions:

  • The developed prioritization method effectively enhances the representation of underrepresented patient subgroups in the validation dataset.
  • This approach is vital for creating a robust and equitable dataset for training and validating AI/ML models in TNBC research.
  • The methodology described facilitates the creation of pivotal studies for AI/ML model development in digital pathology.