Evaluation of radiomics as a predictor of efficacy and the tumor immune microenvironment in anti-PD-1 mAb treated recurrent/metastatic squamous cell carcinoma of the head and neck patients

Abstract

Background: We retrospectively evaluated radiomics as a predictor of the tumor microenvironment (TME) and efficacy with anti-PD-1 mAb (IO) in R/M HNSCC.

Methods: Radiomic feature extraction was performed on pre-treatment CT scans segmented using 3D slicer v4.10.2 and key features were selected using LASSO regularization method to build classification models with XGBoost algorithm by incorporating cross-validation techniques to calculate accuracy, sensitivity, and specificity. Outcome measures evaluated were disease control rate (DCR) by RECIST 1.1, PFS, and OS and hypoxia and CD8 T cells in the TME.

Results: Radiomics features predicted DCR with accuracy, sensitivity, and specificity of 76%, 73%, and 83%, for OS 77%, 86%, 70%, PFS 82%, 75%, 89%, and in the TME, for high hypoxia 80%, 88%, and 72% and high CD8 T cells 91%, 83%, and 100%, respectively.

Conclusion: Radiomics accurately predicted the efficacy of IO and features of the TME in R/M HNSCC. Further study in a larger patient population is warranted.

Keywords: artificial intelligence; head and neck squamous cell carcinoma; imaging; immunotherapy; machine learning; pembrolizumab; radiomics; treatment response.