Abstract
OBJECTIVE: To establish a deep learning model for testing the feasibility of combining magnetic resonance imaging (MRI) deep learning features with clinical features for preoperative prediction of cytokeratin 19 (CK19) status of hepatocellular carcinoma (HCC). METHODS: A retrospective experiment was conducted based on the data of 116 HCC patients with confirmed CK19 status. A single sequence multi-scale feature fusion deep learning model (MSFF-IResnet) and a multi-scale and multimodality feature fusion model (MMFF-IResnet) were established based on the hepatobiliary phase (HBP), diffusion weighted imaging (DWI) sequences of enhanced MRI images, and the clinical features significantly correlated with CK19 status. The classification performance of the models were evaluated to assess the effectiveness of the deep learning models for predicting CK19 status of HCC before surgery. RESULTS: Multivariate analysis showed that an increased neutrophil-to-lymphocyte ratio (P=0.029) and incomplete tumor capsule (P=0.028) were independent predictors of CK19 expression in HCC. The deep learning models improved by multi-scale feature fusion and multi-modality feature fusion methods achieved better classification results than the traditional machine learning models and baseline models, and the final MMFF-IResnet model showed the best classification performance with an AUC of 84.2%, an accuracy of 80.6%, a sensitivity of 80.1% and a specificity of 81.2%. CONCLUSION: The multi-scale and multi-modality feature fusion model based on MRI and clinical parameters is capable of predicting CK19 status of HCC, demonstrating the feasibility of combining deep learning methods with MRI and clinical features for preoperative prediction of CK19 status.