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Related Experiment Video

Updated: May 13, 2025

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A weakly supervised deep learning framework for automated PD-L1 expression analysis in lung cancer.

Feng Jiao1, Zhanxian Shang2, Hongmin Lu1

  • 1Department of Oncology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.

Frontiers in Immunology
|April 15, 2025
PubMed
Summary

An AI tool called MiLT predicts tumor proportion score (TPS) for lung cancer immunotherapy. This artificial intelligence approach standardizes TPS evaluation, improving patient selection for immune checkpoint inhibitors and reducing pathologist variability.

Keywords:
MiLTPD-L1TPSautomated scoringlung cancer

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Area of Science:

  • Computational pathology
  • Artificial intelligence in oncology
  • Cancer immunotherapy biomarkers

Background:

  • Immune checkpoint inhibitors (ICIs) are vital in cancer immunotherapy, especially for lung cancer.
  • Accurate patient selection for ICIs relies on the tumor proportion score (TPS).
  • Manual TPS assessment by pathologists exhibits significant variability and inconsistency.

Purpose of the Study:

  • To develop an artificial intelligence (AI)-powered tool for predicting TPS from whole slide images.
  • To address the limitations of manual TPS evaluation in terms of consistency and efficiency.
  • To improve patient stratification for immune checkpoint inhibitor therapy.

Main Methods:

  • Development of a multi-instance learning for TPS (MiLT) tool.
  • Leveraging multiple instance learning (MIL) to minimize the need for detailed cell-level annotations.
  • Validation of MiLT performance against pathologist assessments on internal and external datasets.

Main Results:

  • MiLT demonstrated high consistency with pathologist TPS assessments (ICC = 0.960).
  • The AI tool showed robust performance across diverse patient cohorts.
  • MiLT provides standardized, efficient, and adaptable TPS predictions.

Conclusions:

  • MiLT offers a reliable, AI-assisted solution for TPS evaluation in lung cancer.
  • The tool has the potential to reduce inter-observer variability among pathologists.
  • Further clinical trials are warranted to integrate MiLT into routine pathological diagnostics and enhance immunotherapy decision-making.