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.
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