Zhou Hang, Jiang Tao, Li Qunying, Zhang Chao, Zhang Cong, Liu Yajing, Cao Jing, Sun Yu, Jin Peile, Luo Jiali, Pan Minqiang, Huang Pintong
Department of Ultrasound, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Front Oncol. 2021 Jun 8;11:672055. doi: 10.3389/fonc.2021.672055. eCollection 2021.
The aim was to build a predictive model based on ultrasonography (US)-based deep learning model (US-DLM) and clinical features (Clin) for differentiating hepatocellular carcinoma (HCC) from other malignancy (OM) in cirrhotic patients. 112 patients with 120 HCCs and 60 patients with 61 OMs were included. They were randomly divided into training and test cohorts with a 4:1 ratio for developing and evaluating US-DLM model, respectively. Significant Clin predictors of OM in the training cohort were combined with US-DLM to build a nomogram predictive model (US-DLM+Clin). The diagnostic performance of US-DLM and US-DLM+Clin were compared with that of contrast enhanced magnetic resonance imaging (MRI) liver imaging and reporting system category M (MRI LR-M). US-DLM was the best independent predictor for evaluating OMs, followed by clinical information, including high cancer antigen 199 (CA199) level and female. The US-DLM achieved an AUC of 0.74 in the test cohort, which was comparable with that of MRI LR-M (AUC=0.84, p=0.232). The US-DLM+Clin for predicting OMs also had similar AUC value (0.81) compared with that of LR-M+Clin (0.83, p>0.05). US-DLM+Clin obtained a higher specificity, but a lower sensitivity, compared to that of LR-M +Clin (Specificity: 82.6% 73.9%, p=0.007; Sensitivity: 78.6% 92.9%, p=0.006) for evaluating OMs in the test set. The US-DLM+Clin model is valuable in differentiating HCC from OM in the setting of cirrhosis.
目的是基于基于超声（US）的深度学习模型（US-DLM）和临床特征（Clin）构建一个预测模型，用于区分肝硬化患者的肝细胞癌（HCC）与其他恶性肿瘤（OM）。纳入了112例患有120个HCC的患者和60例患有61个OM的患者。他们以4:1的比例随机分为训练队列和测试队列，分别用于开发和评估US-DLM模型。将训练队列中OM的显著临床预测因素与US-DLM相结合，构建列线图预测模型（US-DLM+Clin）。将US-DLM和US-DLM+Clin的诊断性能与对比增强磁共振成像（MRI）肝脏成像和报告系统类别M（MRI LR-M）的诊断性能进行比较。US-DLM是评估OM的最佳独立预测因素，其次是临床信息，包括高癌抗原199（CA199）水平和女性。US-DLM在测试队列中的AUC为0.74，与MRI LR-M的AUC（0.84，p=0.232）相当。用于预测OM的US-DLM+Clin与LR-M+Clin相比，也具有相似的AUC值（0.81）（0.83，p>0.05）。在测试集中评估OM时，与LR-M +Clin相比，US-DLM+Clin具有更高的特异性，但敏感性较低（特异性：82.6% 73.9%，p=0.007；敏感性：78.6% 92.9%，p=0.006）。US-DLM+Clin模型在区分肝硬化背景下的HCC与OM方面具有价值。
