Nam Joon Yeul, Sinn Dong Hyun, Bae Junho, Jang Eun Sun, Kim Jin-Wook, Jeong Sook-Hyang
Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea.
Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
JHEP Rep. 2020 Aug 30;2(6):100175. doi: 10.1016/j.jhepr.2020.100175. eCollection 2020 Dec.
BACKGROUND & AIMS: Personalised risk prediction of the development of hepatocellular carcinoma (HCC) among patients with liver cirrhosis on potent antiviral therapy is important for targeted screening and individualised intervention. This study aimed to develop and validate a new model for risk prediction of HCC development based on deep learning, and to compare it with previously reported risk models.
A novel deep-learning-based model was developed from a cohort of 424 patients with HBV-related cirrhosis on entecavir therapy with 2 residual blocks, including 7 layers of a neural network, and it was validated using an independent external cohort (n = 316). The deep-learning-based model was compared to 6 previously reported models (platelet, age, and gender-hepatitis B score [PAGE-B], Chinese University HCC score [CU-HCC], HCC-Risk Estimating Score in CHB patients Under Entecavir [HCC-RESCUE], age, diabetes, race, etiology of cirrhosis, sex, and severity HCC score [ADRESS-HCC], modified PAGE-B score [mPAGE], and Toronto HCC risk index [THRI]) using Harrell's concordance ()-index.
During a median 5.2 yr of follow-up (inter-quartile range 2.8-6.9 yr), 86 patients (20.3%) developed HCC. The deep-learning-based model had a Harrell's -index of 0.719 in the derivation cohort and 0.782 in the validation cohort. Goodness of fit was confirmed by the Hosmer-Lemeshow test ( >0.05). Moreover, this model in the validation cohort had the highest -index among the 6 previously reported models: PAGE-B (0.570), CU-HCC (0.548), HCC-RESCUE (0.577), ADRESS-HCC (0.551), mPAGE (0.598), and THRI (0.587) (all <0.001). The misclassification rate of this model was 23.7% (model accuracy: 76.3%) in the validation group.
The deep-learning-based model had better performance than the previous models for predicting the HCC risk in patients with HBV-related cirrhosis on potent antivirals.
For early detection of hepatocellular carcinoma, it is important to maintain regular surveillance. However, there is currently no standard prediction model for risk stratification that can be used to establish a personalised surveillance strategy. We develop and validate a deep-learning-based model that showed better performance than previous models.
对接受强效抗病毒治疗的肝硬化患者发生肝细胞癌（HCC）进行个性化风险预测，对于靶向筛查和个体化干预至关重要。本研究旨在开发并验证一种基于深度学习的HCC发生风险预测新模型，并将其与先前报道的风险模型进行比较。
从424例接受恩替卡韦治疗的HBV相关肝硬化患者队列中开发了一种基于深度学习的新型模型，该模型有2个残差块，包括一个7层神经网络，并使用独立的外部队列（n = 316）进行验证。使用Harrell一致性（）指数将基于深度学习的模型与6种先前报道的模型（血小板、年龄和性别-乙肝评分[PAGE-B]、中国大学HCC评分[CU-HCC]、恩替卡韦治疗的CHB患者HCC风险估计评分[HCC-RESCUE]、年龄、糖尿病、种族、肝硬化病因、性别和严重程度HCC评分[ADRESS-HCC]、改良PAGE-B评分[mPAGE]和多伦多HCC风险指数[THRI]）进行比较。
在中位5.2年的随访期间（四分位间距2.8 - 6.9年），86例患者（20.3%）发生了HCC。基于深度学习的模型在推导队列中的Harrell指数为0.719，在验证队列中为0.782。Hosmer-Lemeshow检验证实了拟合优度（>0.05）。此外，该模型在验证队列中的指数在6种先前报道的模型中最高：PAGE-B（0.570）、CU-HCC（0.548）、HCC-RESCUE（0.577）、ADRESS-HCC（0.551）、mPAGE（0.598）和THRI（0.587）（均<0.001）。该模型在验证组中的误分类率为23.7%（模型准确率：76.3%）。
对于预测接受强效抗病毒治疗的HBV相关肝硬化患者的HCC风险，基于深度学习的模型比先前的模型表现更好。
为了早期发现肝细胞癌，定期监测很重要。然而，目前尚无用于风险分层的标准预测模型可用于制定个性化监测策略。我们开发并验证了一种基于深度学习的模型，其表现优于先前的模型。
