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Updated: Jun 27, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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Spatial Biomarker Deep Learning Model Predicts Response to PI3K Inhibition in Head and Neck Cancer.

Antoine Desilets1,2, Minh Tri Le2,3, Catalina Moreno2

  • 1Hematology-Oncology Service, Department of Medicine, Centre Hospitalier de l'Université de Montréal (CHUM), Montreal, QC H2X 0A9, Canada.

Cancers
|June 26, 2026
PubMed
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This summary is machine-generated.

Artificial intelligence identified spatial biomarkers from H&E slides that predict improved survival for head and neck squamous cell carcinoma (HNSCC) patients treated with buparlisib. These AI-derived features offer a scalable approach for patient selection in R/M HNSCC.

Area of Science:

  • Computational pathology
  • Biomarker discovery
  • Artificial intelligence in oncology

Background:

  • Buparlisib plus paclitaxel improved survival in recurrent/metastatic head and neck squamous cell carcinoma (R/M HNSCC).
  • Predictive biomarkers for buparlisib benefit in R/M HNSCC remain undefined.

Purpose of the Study:

  • To evaluate artificial intelligence (AI)-extracted spatial biomarkers from H&E slides for predicting overall survival benefit from buparlisib in R/M HNSCC.
  • To assess if AI-derived features can identify patients most likely to benefit from buparlisib treatment.

Main Methods:

  • Deep learning analysis of whole-slide H&E images from BERIL-1 trial patients.
  • Evaluation of tumor-infiltrating lymphocyte density, tumor microenvironment heterogeneity, and granulocyte fraction.
Keywords:
BERIL-1H&Eartificial intelligencebuparlisibdigital pathologyhead and neck cancerspatial biomarkerssquamous cell carcinoma

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  • Cox proportional hazards models to assess biomarker-treatment interactions.

    • Main Results:

    • High tumor-infiltrating lymphocyte density, tumor microenvironment heterogeneity, and granulocyte enrichment in the invasive margin predicted improved overall survival with buparlisib.
    • AI-derived spatial metrics outperformed CD3 immunohistochemistry.
    • High granulocyte-tumor cell proximity correlated with improved survival on buparlisib.

    Conclusions:

    • AI-extracted spatial features from H&E slides are associated with overall survival benefit from buparlisib in R/M HNSCC.
    • These scalable biomarkers support image-based patient selection strategies.
    • AI-driven spatial biomarkers are being prospectively evaluated in the BURAN phase 3 trial.