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April 5, 2026Cancer Research

Abstract 78: OncoPredikt: A deep-learning framework for tumor detection and biomarker quantification in breast cancer IHC whole-slide images.

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Authors

GSGowhar ShafiPSPM ShivamurthyASAditya Satpute

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Overview

AI-driven approach demonstrates improved tumor detection and biomarker quantification in breast cancer, suggesting enhanced diagnostic accuracy.

Key Points

  • The aim is to improve tumor detection and biomarker quantification in breast cancer using AI.
  • Developed AI-based OncoPredikt model trained on H&E images and validated on IHC WSIs.
  • Conducted automated tumor detection and biomarker quantification for ER, PR, HER2, and Ki67.
  • Validated workflows against pathologist annotations.
  • Tumor masks achieved Dice Similarity Coefficient >0.8 on H&E and IHC WSIs.
  • AI approached reliably discriminated HER2 ultra-low (0+) from no staining (0).
  • Automated assessments showed strong concordance with pathologist scoring across ER/PR and Ki67 indices.

Cite This Study

Shafi et al. (2026) studied this question.

synapsesocial.com/papers/69d1fd62a79560c99a0a35dahttps://doi.org/10.1158/1538-7445.am2026-78
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Abstract PO2-07-05: Deep learning model for automated quantification of HER2 expression in invasive breast cancers from immunohistochemical whole slide images2024 · 1 citations
  2. 2Abstract PO4-26-09: The Advantage of Artificial-Intelligence in HER2 IHC 0 and 1+ Scoring in Breast Cancer2024
  3. 3Abstract 5470: Low-magnification deep learning model for rapid HER2 status prediction from H&E whole-slide images.2026
  4. 4Abstract PD11-10: Optimizing HER2 Diagnostic Pathways: AI Assistance Enriches Gene Amplified Cases in Equivocal Category and Reduces Turnaround Time2026
  5. 5Abstract 87: <i>Path2Prot</i> offers a new way for breast tumor subtyping and treatment response prediction from AI-inferred proteomic biomarkers.2026