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Evaluating Cellularity Estimation Methods: Comparing AI Counting with Pathologists' Visual Estimates.

Tomoharu Kiyuna1, Eric Cosatto2, Kanako C Hatanaka3

  • 1Healthcare Life Science Division, NEC Corporation, Tokyo 108-8556, Japan.

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|June 19, 2024
PubMed
Summary
This summary is machine-generated.

An artificial intelligence (AI) model significantly improves tumor content ratio (TCR) assessment accuracy in lung cancer specimens compared to pathologists. This AI-driven approach enhances genetic testing precision for better patient outcomes.

Keywords:
U-Net modelartificial intelligence (AI)domain shiftnext generation sequencing (NGS)site dependencytumor content ratio (TCR)

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Area of Science:

  • Computational pathology
  • Digital pathology
  • Artificial intelligence in oncology

Background:

  • Next-generation sequencing (NGS) drives precision medicine by identifying cancer driver gene alterations.
  • Accurate genetic testing relies on sufficient tumor cells, necessitating precise tumor content ratio (TCR) evaluation.
  • Pathologist variability in TCR estimation from H&E images presents a significant challenge.

Purpose of the Study:

  • To develop and validate an AI model for accurate TCR calculation from H&E-stained lung cancer images.
  • To compare the accuracy of AI-based TCR estimation against pathologist visual assessment.
  • To assess the robustness and inter-institutional consistency of the developed AI model.

Main Methods:

  • Established a "gold standard" TCR by exhaustive cell labeling by three pathologists on 41 lung cancer cases.
  • Developed a compact, fast, fully convolutional neural network AI model for cell detection and classification.
  • Compared TCR accuracy between 13 pathologists and the AI model against the gold standard.

Main Results:

  • The AI model achieved 92% cell detection and 84% classification accuracy.
  • AI-based TCR calculation showed significantly lower error compared to pathologist visual assessment (p<0.05).
  • The AI model demonstrated greater inter-institutional robustness than the average pathologist assessment.

Conclusions:

  • AI models significantly improve the accuracy of tumor cellularity assessments in clinical settings.
  • Enhanced TCR accuracy via AI facilitates more efficient and reliable genetic testing.
  • The adoption of robust AI tools promises improved patient outcomes through precise cancer treatment.