A Deep Learning Model of Histologic Tumor Differentiation as a Prognostic Tool in Hepatocellular Carcinoma

Abstract

Tumor differentiation represents an important driver of the biological behavior of various forms of cancer. Histologic features of tumor differentiation in hepatocellular carcinoma (HCC) include cytoarchitecture, immunohistochemistry profile, and reticulin framework. In this study, we evaluate the performance of an artificial intelligence (AI)–based model in quantifying features of HCC tumor differentiation and predicting cancer-related outcomes. We developed a supervised AI model using a cloud-based, deep learning platform to quantify histologic features of HCC differentiation, including various morphologic parameters (nuclear density, area, circularity, chromatin pattern, and pleomorphism), mitotic figures, immunohistochemistry markers (HepPar 1 and glypican-3), and reticulin expression. We applied this AI model to patients undergoing HCC curative resection and assessed whether AI-based features added value to standard clinical and pathologic data in predicting HCC-related outcomes. Ninety-nine HCC resection specimens were included. Three AI-based histologic variables were most relevant to HCC prognostic assessment: (1) percentage of tumor occupied by neoplastic nuclei (nuclear area percent), (2) quantitative reticulin expression in the tumor, and (3) HepPar 1 low (ie, expressed in <50% of the tumor)/glypican-3–positive immunophenotype. Statistical models that included these AI-based variables outperformed models with combined clinical pathologic features for overall survival (C-indexes of 0.81 vs 0.68), disease-free survival (C-indexes of 0.73 vs 0.68), metastasis (C-indexes of 0.78 vs 0.65), and local recurrence (C-indexes of 0.72 vs 0.68) for all cases, with similar results in the subgroup analysis of World Health Organization grade 2 HCCs. Our AI model serves as a proof of concept that HCC differentiation can be objectively quantified digitally by assessing a combination of biologically relevant histopathologic features. In addition, several AI-derived features were independently predictive of HCC-related outcomes in our study population, most notably nuclear area percent, hepar-low/glypican-3–negative phenotype, and decreasing levels of reticulin expression, highlighting the relevance of quantitative analysis of tumor differentiation features in this context.