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Artificial Intelligence-Assisted Score Analysis for Predicting the Expression of the Immunotherapy Biomarker PD-L1 in Lung Cancer. | LitMetric

AI Article Synopsis

  • PD-L1 is a key biomarker for predicting how patients with lung cancer will respond to immunotherapy, but evaluating its expression is tricky for pathologists.
  • A new deep learning-based AI model was created to automatically analyze PD-L1 expression in lung cancer patients, using data from 1,288 individuals.
  • The AI models (M2 and M3) showed high accuracy and consistency with pathologist results, suggesting they could significantly enhance the diagnostic process for PD-L1 analysis in clinical settings.

Article Abstract

Programmed cell death ligand 1 (PD-L1) is a critical biomarker for predicting the response to immunotherapy. However, traditional quantitative evaluation of PD-L1 expression using immunohistochemistry staining remains challenging for pathologists. Here we developed a deep learning (DL)-based artificial intelligence (AI) model to automatically analyze the immunohistochemical expression of PD-L1 in lung cancer patients. A total of 1,288 patients with lung cancer were included in the study. The diagnostic ability of three different AI models (M1, M2, and M3) was assessed in both PD-L1 (22C3) and PD-L1 (SP263) assays. M2 and M3 showed improved performance in the evaluation of PD-L1 expression in the PD-L1 (22C3) assay, especially at 1% cutoff. Highly accurate performance in the PD-L1 (SP263) was also achieved, with accuracy and specificity of 96.4 and 96.8% in both M2 and M3, respectively. Moreover, the diagnostic results of these three AI-assisted models were highly consistent with those from the pathologist. Similar performances of M1, M2, and M3 in the 22C3 dataset were also obtained in lung adenocarcinoma and lung squamous cell carcinoma in both sampling methods. In conclusion, these results suggest that AI-assisted diagnostic models in PD-L1 expression are a promising tool for improving the efficiency of clinical pathologists.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9286729PMC
http://dx.doi.org/10.3389/fimmu.2022.893198DOI Listing

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