Deep Learning Model Based on Contrast-Enhanced Computed Tomography Imaging to Predict Postoperative Early Recurrence after the Curative Resection of a Solitary Hepatocellular Carcinoma

SIMPLE SUMMARY: Patients with postoperative early recurrence of hepatocellular carcinoma within 2 years are at high risk for poor prognosis, and identifying high-risk patients with postoperative early recurrence is becoming increasingly important in the clinical practice for hepatocellular carcinoma. However, preoperatively predicting the early recurrence remains difficult. Thus, we developed a deep learning model that accurately predicts early postoperative hepatocellular carcinoma recurrence; in addition, the contrast-enhanced computed tomography imaging analysis was the most important factor to predict early hepatocellular carcinoma recurrence in clinical variables of the current deep learning model. Guiding the treatment strategy for patients with hepatocellular carcinoma may be possible using contrast-enhanced computed tomography images by utilizing the deep learning method. ABSTRACT: We aimed to develop the deep learning (DL) predictive model for postoperative early recurrence (within 2 years) of hepatocellular carcinoma (HCC) based on contrast-enhanced computed tomography (CECT) imaging. This study included 543 patients who underwent initial hepatectomy for HCC and were randomly classified into training, validation, and test datasets at a ratio of 8:1:1. Several clinical variables and arterial CECT images were used to create predictive models for early recurrence. Artificial intelligence models were implemented using convolutional neural networks and multilayer perceptron as a classifier. Furthermore, the Youden index was used to discriminate between high- and low-risk groups. The importance values of each explanatory variable for early recurrence were calculated using permutation importance. The DL predictive model for postoperative early recurrence was developed with the area under the curve values of 0.71 (test datasets) and 0.73 (validation datasets). Postoperative early recurrence incidences in the high- and low-risk groups were 73% and 30%, respectively (p = 0.0057). Permutation importance demonstrated that among the explanatory variables, the variable with the highest importance value was CECT imaging analysis. We developed a DL model to predict postoperative early HCC recurrence. DL-based analysis is effective for determining the treatment strategies in patients with HCC.