Gd-EOB-DTPA-enhanced MRI radiomics and deep learning models for predicting the pathological differentiation degree in hepatocellular carcinoma.

[OBJECTIVE] To investigate the value of Gd-EOB-DTPA enhanced MRI radiomics and deep learning models with clinical-radiologic characteristics in predicting the pathological differentiation degree in hepatocellular carcinoma (HCC).

[METHODS] This study included 409 (training set: 304; validation set: 105) HCC patients who underwent preoperative Gd-EOB-DTPA-enhanced MRI from three hospitals. Clinical and radiological (CR) characteristics were selected using univariate and multivariate analyses. Radiomics and deep learning (DL) features were extracted based on FeAture Explorer software and the 3D ResNet-18 extractor, respectively. CR, radiomics, DL, radiomics combined DL (DLR) and CR-DLR models were built using machine learning algorithms. The predictive capacity of the models was assessed by the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. DCA curves and the calibration curves were used as model validation.

[RESULTS] Age, AFP, capsule, and peritumoral hypointensity in HBP were independent predictors of the differentiation degree in HCC. In the validation set, the CR-DLR model (AUC = 0.794) were higher than that of the radiomics (AUC = 0.771),DL (AUC = 0.669) CR (AUC = 0.606) and DLR (AUC = 0.776) models. And the differences were statistically significant between the CR-DLR and CR (P = 0.026) or DL models (P = 0.013). The calibration and DCA curves suggesting that the CR-DLR model has a high prediction accuracy and a good net benefit for predicting differentiation degree.

[CONCLUSION] Combining deep learning, radiomics models with clinical and conventional radiological features has certain value and can help doctors formulate more favorable treatment plans for patients.