Case studies

Supporting AI-based germline mutation pre-screening with comprehensive whole slide image datasets

Oncology
Breast Cancer
Germline Mutation Testing
Biomarker Pre-Screening
Clinical Data
Whole Slide Images
Pathology Data
Molecular Test Results

Background

Germline mutation testing is essential for assessing inherited breast cancer risk and informing targeted treatment strategies1.  

In early-stage breast cancer, timely and accurate detection of pathogenic BRCA1/2 variants can significantly impact surgical planning, systemic therapy choices, and long-term surveillance2.  

AI-driven approaches are emerging that analyze histopathology images to help prioritize patients for genetic testing, with the potential to accelerate diagnostic workflows and improve the consistency of clinical decision-making3.

Objective

A biotech company approached Aigora with the goal of obtaining a tailored, comprehensive whole slide image dataset suitable for the development, validation, and potential future regulatory evaluation of an AI-powered gBRCA pre-screening solution for early-stage breast cancer.

Delivery

To support this objective, Aigora curated a regulatory-grade multimodal dataset integrating H&E-stained whole slide images of early-stage breast cancer tissue with detailed clinical, pathological, and molecular data.

Dataset characteristics

  • Clinical data

    Patient demographics, neoadjuvant treatment information, cancer laterality

  • Whole slides images

    De-identified H&E whole slide images from early-stage breast cancer biopsies

  • Pathology data

    Histological grading, staging, and subtypes, ER/PR status

  • Molecular test results

    HER2 and gBRCA mutation status

References

  1. Bedrosian I, Somerfield MR, Achatz MI, et al. Germline Testing in Patients With Breast Cancer: ASCO-Society of Surgical Oncology Guideline. J Clin Oncol. 2024;42(5):584-604. doi:10.1200/JCO.23.02225
  2. t’Kint de Roodenbeke MD, Pondé N, Buisseret L, Piccart M. Management of early breast cancer in patients bearing germline BRCA mutations. Semin Oncol. 2020;47(5):243-248. doi:10.1053/j.seminoncol.2020.07.006
  3. Tiwari A, Ghose A, Hasanova M, et al. The current landscape of artificial intelligence in computational histopathology for cancer diagnosis. Discov Oncol. 2025;16(1):438. doi:10.1007/s12672-025-02212-z
Project metrics

  • Number of cases

    300

  • Number of variables per case

    30+

  • Delivery timeline

    3 months

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