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Tumor-Infiltrating Lymphocyte Recognition in Primary Melanoma by Deep Learning Convolutional Neural Network.

Filippo Ugolini1, Francesco De Logu2, Luigi Francesco Iannone2

  • 1Section of Pathological Anatomy, Department of Health Sciences, University of Florence, Florence, Italy.

The American Journal of Pathology
|September 21, 2023
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Summary

Artificial intelligence (AI) using a convolution neural network (CNN) accurately assesses tumor-infiltrating lymphocytes (TILs) in primary melanoma (PM). This AI-based TIL score is a reliable prognostic marker for patient outcomes.

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Area of Science:

  • Digital pathology
  • Artificial intelligence in oncology
  • Melanoma research

Background:

  • Tumor-infiltrating lymphocytes (TILs) are crucial prognostic indicators in primary melanoma (PM).
  • Standardized assessment of TILs is challenging with conventional methods.
  • AI in digital pathology offers potential for automated and standardized TIL evaluation.

Purpose of the Study:

  • To develop and validate a novel convolution neural network (CNN) for automated TIL assessment in PM.
  • To establish an AI-based TIL density index (AI-TIL) for prognostic evaluation.
  • To compare the performance of AI-TIL with conventional TIL assessment methods.

Main Methods:

  • A CNN was trained and validated on 307 primary melanoma whole slide images (WSIs).
  • The CNN classified tumor patches based on the presence or absence of TILs, generating an AI-TIL index.
  • Performance was evaluated on an independent testing set for specificity and sensitivity.

Main Results:

  • The CNN demonstrated high performance, achieving 100% specificity and sensitivity in TIL recognition on the testing set.
  • The AI-TIL index showed strong correlation with conventional TIL evaluation and patient clinical outcomes.
  • The AI-TIL index was identified as an independent prognostic marker for favorable outcomes in PM.

Conclusions:

  • A fully automated AI-TIL assessment is superior to conventional methods for differentiating PM clinical outcomes.
  • The developed AI-TIL index serves as a robust and independent prognostic marker.
  • Further research is needed to create user-friendly tools for clinical integration of AI-TIL assessment.