Hepatocellular Carcinoma Immune Microenvironment Analysis: A Comprehensive Assessment with Computational and Classical Pathology.

Abstract

The spatial variability and clinical relevance of the tumor immune microenvironment (TIME) are still poorly understood for hepatocellular carcinoma (HCC). In this study, we aim to develop a deep learning (DL)-based image analysis model for the spatial analysis of immune cell biomarkers and microscopically evaluate the distribution of immune infiltration. Ninety-two HCC surgical liver resections and 51 matched needle biopsies were histologically classified according to their immunophenotypes: inflamed, immune-excluded, and immune-desert. To characterize the TIME on immunohistochemistry (IHC)-stained slides, we designed a multistage DL algorithm, IHC-TIME, to automatically detect immune cells and their localization in the TIME in tumor-stroma and center-border segments. Two models were trained to detect and localize the immune cells on IHC-stained slides. The framework models (i.e., immune cell detection models and tumor-stroma segmentation) reached 98% and 91% accuracy, respectively. Patients with inflamed tumors showed better recurrence-free survival than those with immune-excluded or immune-desert tumors. Needle biopsies were found to be 75% accurate in representing the immunophenotypes of the main tumor. Finally, we developed an algorithm that defines immunophenotypes automatically based on the IHC-TIME analysis, achieving an accuracy of 80%. Our DL-based tool can accurately analyze and quantify immune cells on IHC-stained slides of HCC. Microscopic classification of the TIME can stratify HCC according to the patient prognosis. Needle biopsies can provide valuable insights for TIME-related prognostic prediction, albeit with specific constraints. The computational pathology tool provides a new way to study the HCC TIME.