Deep learning based on multiparametric MRI predicts early recurrence in hepatocellular carcinoma patients with solitary tumors ≤5 cm.

Purpose: To evaluate the effectiveness of a constructed deep learning model in predicting early recurrence after surgery in hepatocellular carcinoma (HCC) patients with solitary tumors ≤5 cm. Materials and methods: Our study included a total of 331 HCC patients who underwent curative resection, with all patients having preoperative dynamic contrast-enhanced MRI (DCE-MRI). Patients who recurred within two years after surgery were defined as early recurrence. The enrolled patients were randomly divided into the training group and the testing group. A ResNet-based deep learning model with eight conventional neural network branches was built to predict the early recurrence status of these patients. Patient characteristics and laboratory tests were further filtered by regression models and then integrated with deep learning models to improve the prediction performance. Results: Among 331 HCC patients, 70 (21.1 %) experienced early recurrence. In multivariate Cox regression analysis, only tumor size (Hazard ratio (HR=1.394, 95 %CI:1.011-1.920, p value=0.043) and deep learning extracted image features (HR: 38440, 95 %CI:2321-636600, p value<0.001) were significant risk factors for early recurrence. In the training and testing cohort, the AUCs of the image-based deep learning prediction model were 0.839 and 0.833. By integrating tumor size with image-based deep learning model to construct a combined model, we found that the AUCs of the combined model to assess early recurrence in the training and validation cohort were 0.846 and 0.842. We further developed a nomogram to visualize the preoperative combined model, and the prediction performance of nomogram showed a good fitness in the testing cohort. Conclusions: The proposed deep learning-based prediction model using DCE-MRI is useful for assessing early recurrence in HCC patients with single tumors ≤5 cm.