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<p>In routine breast cancer diagnostics, pathologists often review each case twice—first to determine the need for immunohistochemical (IHC) stains, and a second time to issue the final diagnosis—creating significant workload and delays. We present an artificial intelligence-based system designed to streamline this process by distinguishing invasive carcinoma from fibroadenoma and in situ lesions in Whole Slide Images of H&amp;E-stained breast biopsies, enabling automatic IHC stain requests for these lesions, while abstaining on non-target or low-confidence cases. The system leverages a weakly supervised method, trained directly on final diagnostic labels without the need for manual annotations. It achieves over 91% sensitivity and specificity across histological types in internal validation and shows strong generalizability in two external pilot studies. In a real-world setting, the system could determine if IHC stains should be ordered (sensitivity and specificity &gt; 96%) and which stains (sensitivity and specificity &gt; 81%). Compared to both expert pathologists and state-of-the-art models, our model performs competitively. Designed for real-world deployment, it is fully integrated into the Digital Imaging and Communications in Medicine (DICOM) standard. In validation data, it could have saved up to 43&#xa0;h of pathologists’ time. Our model represents a scalable solution for more efficient diagnostic BC workflows.</p>

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AUPA: weakly supervised approach for streamlining breast cancer diagnostic workflow by WSI histological type classification for efficient IHC triage

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月26日

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  • M. J. Garcia-Gonzalez,
  • Jhon Comba Miranda,
  • Jose Luis Rey Sanjurjo,
  • Laura Valeria Perez-Herrera,
  • Blanca Cimadevila Alvarez,
  • Xabier Echeberria-Barrio,
  • Francesco Zola,
  • Ivan Macia Oliver,
  • Valery Naranjo Ornedo,
  • Karen Lopez-Linares Roman

摘要

In routine breast cancer diagnostics, pathologists often review each case twice—first to determine the need for immunohistochemical (IHC) stains, and a second time to issue the final diagnosis—creating significant workload and delays. We present an artificial intelligence-based system designed to streamline this process by distinguishing invasive carcinoma from fibroadenoma and in situ lesions in Whole Slide Images of H&E-stained breast biopsies, enabling automatic IHC stain requests for these lesions, while abstaining on non-target or low-confidence cases. The system leverages a weakly supervised method, trained directly on final diagnostic labels without the need for manual annotations. It achieves over 91% sensitivity and specificity across histological types in internal validation and shows strong generalizability in two external pilot studies. In a real-world setting, the system could determine if IHC stains should be ordered (sensitivity and specificity > 96%) and which stains (sensitivity and specificity > 81%). Compared to both expert pathologists and state-of-the-art models, our model performs competitively. Designed for real-world deployment, it is fully integrated into the Digital Imaging and Communications in Medicine (DICOM) standard. In validation data, it could have saved up to 43 h of pathologists’ time. Our model represents a scalable solution for more efficient diagnostic BC workflows.