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AI is revolutionizing cytology cell blocks for advanced cancer diagnosis. Deep learning enhances biomarker analysis, improving treatment decisions and precision oncology through morphology-driven insights.

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Area of Science:

  • Oncology
  • Computational Pathology
  • Cytopathology

Background:

  • Cytology cell blocks are crucial for advanced malignancy diagnosis and therapeutic biomarker evaluation.
  • They preserve tumor cells and microenvironment, making them suitable for immunohistochemistry.
  • Computational pathology and deep learning offer new avenues for biomarker interpretation.

Purpose of the Study:

  • To highlight Artificial Intelligence (AI) applications in interpreting biomarkers on cytology cell blocks.
  • To review AI's role in assessing biomarkers like PD-L1, HER2, ER/PR, Ki-67, ALK/ROS1, BRAF V600E, and p16.
  • To discuss AI's potential in inferring biomarker status from morphology and analyzing spatial pathobiology.

Main Methods:

  • Review of AI applications in biomarker interpretation on cytology cell blocks.
  • Exploration of deep learning models for quantitative assessment of tumor-immune interactions.
  • Discussion of emerging predictive models inferring biomarker status from morphology.

Main Results:

  • AI enhances biomarker interpretation on cell blocks, improving reproducibility and reducing variability.
  • AI applications cover key biomarkers guiding immunotherapy, targeted therapy, and hormonal therapy.
  • Emerging AI models can predict biomarker status directly from cell block morphology.

Conclusions:

  • Cytology cell blocks are a vital platform for AI-driven biomarker interpretation.
  • AI integration into cytology workflows promises advancements in morphology-driven precision oncology.
  • AI enhances the diagnostic and therapeutic utility of cell block specimens in advanced cancers.