The Hepatic Histomorphology and Application of Ensemble Transfer Learning in Convolutional Neural Network Models in the Biopsies of Hepatic Langerhans Cell Histiocytosis

BACKGROUND AND AIMS: Langerhans cell histiocytosis (LCH) commonly affects children. The histological distinction of hepatic LCH from its mimickers is challenging and often requires judicious use of immunohistochemistry in the correct clinical and radiological context. We described the patterns of hepatic LCH and applied an ensemble transfer-learning model to predict and distinguish hepatic Langerhans cell infiltration from histological mimics. MATERIALS AND METHODS: Clinical, histological, and immunophenotypic data from 6 pediatric LCH cases with liver biopsies were retrieved from the archives. The histological patterns were reviewed. Histological images were obtained from all cases of hepatic LCH with histologically overt Langerhans cell infiltration, as well as from cases with histological mimickers. A soft-voting-based ensemble transfer learning model was applied to the images after splitting them into training, validation, and test sets. The performance metrics were evaluated. RESULTS: All cases were multisystem LCH (MS-LCH). The hepatic histomorphology showed sclerosing cholangiopathy with/without Langerhans cell infiltration as the most common pattern. Bile extravasation and cystic dilatation were noted in a single case. Eosinophilic microabscesses, cholangiopathy, and portal-based aggregates are the important histological clues. The ensemble learning model had an area under the curve (receiver operating characteristic) of 0.99, with sensitivities, specificities, and accuracies of 33.3%, 100%, and 75%, respectively. CONCLUSION: Sclerosing cholangiopathy with or without Langerhans cells is the most common pattern of hepatic LCH. An ensemble transfer learning model can serve as a valuable screening tool for histopathologists to predict and diagnose hepatic LCH.