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Related Experiment Video

Updated: May 22, 2026

Optimization of Breast Biopsy and Mastectomy Sample Collection Procedures for Biobanking, Personalized Medicine, and Research Applications
06:42

Optimization of Breast Biopsy and Mastectomy Sample Collection Procedures for Biobanking, Personalized Medicine, and Research Applications

Published on: September 2, 2025

Optimizing breast core needle biopsy biomarker throughput using an AI-based workflow.

Frederik Deman1,2, Glenn Broeckx1,3, Quentin Degotte1

  • 1Department of Pathology, Ziekenhuis aan de Stroom (ZAS), Antwerp, Belgium.

Histopathology
|May 21, 2026
PubMed
Summary
This summary is machine-generated.

An AI-supported workflow significantly reduced breast cancer biomarker testing turnaround time by 30% without impacting diagnostic accuracy. This AI integration in pathology labs enhances efficiency for faster patient care.

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Last Updated: May 22, 2026

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Area of Science:

  • Pathology
  • Artificial Intelligence
  • Oncology

Background:

  • Breast cancer biomarker testing is crucial for treatment decisions.
  • Current workflows can have lengthy turnaround times impacting patient care.

Purpose of the Study:

  • To evaluate the feasibility and impact of an AI-supported workflow on breast cancer biomarker testing.
  • To assess the effect on turnaround time (TAT) and diagnostic relevance.

Main Methods:

  • A retrospective study compared pre- and post-implementation phases of an AI system (IBEX™ Breast) integrated into the Laboratory Information System (LIS).
  • AI stratified H&E slides, autonomously ordering biomarker tests (ER, PR, HER2, Ki-67) for high-likelihood invasive carcinoma cases.
  • Turnaround time (TATtech and TATclin) was measured, and the relevance of AI-initiated testing was assessed.

Main Results:

  • The AI-enhanced workflow significantly reduced TATtech by approximately 30% (32h to 23h) and TATclin by approximately 1 working day.
  • Diagnostic accuracy was maintained, with no unnecessary stains triggered by the AI system.
  • The relevance of AI-initiated testing for final diagnosis was confirmed.

Conclusions:

  • Integrated, LIS-embedded AI significantly optimizes breast cancer biomarker TAT in pathology laboratories.
  • AI enhances laboratory efficiency and supports rapid, patient-centered oncologic care without compromising diagnostic quality.