Individualized Prognostication Based on Deep‐Learning Models Using Computed Tomography as an Imaging Biomarker After Hepatocellular Carcinoma Resection

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INTRODUCTION: No reports described the deep-learning (DL) models using computed tomography (CT) as an imaging biomarker for predicting postoperative long-term outcomes in patients with hepatocellular carcinoma (HCC). This study aimed to validate the DL models for individualized prognostication after HCC resection using CT as an imaging biomarker. METHODS: This study included 1733 patients undergoing hepatic resection for solitary HCC. Participants were classified into training, validation, and test datasets. DL predictive models were developed using clinical variables and CT imaging to predict recurrence within 2 and 5 years and overall survival (OS) of > 5 and > 10 years postoperatively. Youden index was utilized to identify cutoff values. Permutation importance was used to calculate the importance of each explanatory variable. RESULTS: DL predictive models for recurrence within 2 and 5 years and OS of > 5 and > 10 years postoperatively were developed in the test datasets, with the area under the curve of 0.70, 0.70, 0.80, and 0.80, respectively. Permutation importance demonstrated that CT imaging analysis revealed the highest importance value. The postoperative recurrence rates within 2 and 5 years were 52.6% versus 18.5% (p < 0.001) and 78.9% versus 46.7% (p < 0.001) and overall mortality within 5 and 10 years postoperatively were 45.1% versus 9.2% (p < 0.001) and 87.1% versus 43.2% (p < 0.001) in the high-risk versus low-risk groups, respectively. CONCLUSIONS: Our DL models using CT as an imaging biomarker are useful for individualized prognostication and may help optimize treatment planning for patients with HCC.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.