Lei JunQiang, Xu YongSheng, Zhu YuanHui, Jiang ShanShan, Tian Song, Zhu Yi
Department of Radiology, First Hospital of Lan Zhou University, LanZhou, China.
Intelligent Imaging Medical Engineering Research Center of Gansu Province, LanZhou, China.
Abdom Radiol (NY). 2025 Mar 10. doi: 10.1007/s00261-025-04853-8.
To develop an automated deep learning (DL) methodology for detecting small hepatocellular carcinoma (sHCC) in cirrhotic livers, leveraging Gd-EOB-DTPA-enhanced MRI.
The present retrospective study included a total of 120 patients with cirrhosis, comprising 78 patients with sHCC and 42 patients with non-HCC cirrhosis, who were selected through stratified sampling. The dataset was divided into training and testing sets (8:2 ratio). The nnU-Net exhibits enhanced capabilities in segmenting small objects. The segmentation performance was assessed using the Dice coefficient. The ability to distinguish between sHCC and non-HCC lesions was evaluated through ROC curves, AUC scores and P values. The case-level detection performance for sHCC was evaluated through several metrics: accuracy, sensitivity, and specificity.
The AUCs for distinguishing sHCC patients from non-HCC patients at the lesion level were 0.967 and 0.864 for the training and test cohorts, respectively, both of which were statistically significant at P < 0.001. At the case level, distinguishing between patients with sHCC and patients with cirrhosis resulted in accuracies of 92.5% (95% CI, 85.1-96.9%) and 81.5% (95% CI, 61.9-93.7%), sensitivities of 95.1% (95% CI, 86.3-99.0%) and 88.2% (95% CI, 63.6-98.5%), and specificities of 87.5% (95% CI, 71.0-96.5%) and 70% (95% CI, 34.8-93.3%) for the training and test sets, respectively.
The DL methodology demonstrated its efficacy in detecting sHCC within a cohort of patients with cirrhosis.
利用钆塞酸二钠增强磁共振成像（Gd-EOB-DTPA-enhanced MRI），开发一种用于检测肝硬化肝脏中微小肝细胞癌（sHCC）的自动化深度学习（DL）方法。
本回顾性研究共纳入120例肝硬化患者，其中包括78例sHCC患者和42例非HCC肝硬化患者，通过分层抽样选取。数据集按8:2的比例分为训练集和测试集。nnU-Net在分割小目标方面具有增强的能力。使用Dice系数评估分割性能。通过ROC曲线、AUC分数和P值评估区分sHCC和非HCC病变的能力。通过几个指标评估sHCC的病例级检测性能：准确性、敏感性和特异性。
在病变水平上，训练队列和测试队列区分sHCC患者与非HCC患者的AUC分别为0.967和0.864，两者在P < 0.001时均具有统计学意义。在病例水平上，区分sHCC患者和肝硬化患者时，训练集和测试集的准确率分别为９２.５％（９５％CI，８５.１－９６.９％）和８１.５％（９５％CI，６１.９－９３.７％），敏感性分别为９５.１％（９５％CI，８６.３－９９.０％）和８８.２％（９５％CI，６３.６－９８.５％），特异性分别为８７.５％（９５％CI，７１.０－９６.５％）和７０％（９５％CI，３４.８－９３.３％）。
深度学习方法在检测肝硬化患者队列中的sHCC方面显示出其有效性。
