Chen Yuyan, Liu Zelong, Mo Yunxian, Li Bin, Zhou Qian, Peng Sui, Li Shaoqiang, Kuang Ming
Department of Liver Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
Division of Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
Front Oncol. 2021 Mar 10;11:605296. doi: 10.3389/fonc.2021.605296. eCollection 2021.
Preoperative prediction of post-hepatectomy liver failure (PHLF) in patients with hepatocellular carcinoma (HCC) is significant for developing appropriate treatment strategies. We aimed to establish a radiomics-based clinical model for preoperative prediction of PHLF in HCC patients using gadolinium-ethoxybenzyl-diethylenetriamine (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI). A total of 144 HCC patients from two medical centers were included, with 111 patients as the training cohort and 33 patients as the test cohort, respectively. Radiomics features and clinical variables were selected to construct a radiomics model and a clinical model, respectively. A combined logistic regression model, the liver failure (LF) model that incorporated the developed radiomics signature and clinical risk factors was then constructed. The performance of these models was evaluated and compared by plotting the receiver operating characteristic (ROC) curve and calculating the area under the curve (AUC) with 95% confidence interval (CI). The radiomics model showed a higher AUC than the clinical model in the training cohort and the test cohort for predicting PHLF in HCC patients. Moreover, the LF model had the highest AUCs in both cohorts [0.956 (95% CI: 0.955-0.962) and 0.844 (95% CI: 0.833-0.886), respectively], compared with the radiomics model and the clinical model. We evaluated quantitative radiomics features from MRI images and presented an externally validated radiomics-based clinical model, the LF model for the prediction of PHLF in HCC patients, which could assist clinicians in making treatment strategies before surgery.
术前预测肝细胞癌（HCC）患者肝切除术后肝衰竭（PHLF）对于制定合适的治疗策略具有重要意义。我们旨在建立一种基于放射组学的临床模型，用于使用钆塞酸二钠（Gd-EOB-DTPA）增强磁共振成像（MRI）对HCC患者术前预测PHLF。总共纳入了来自两个医疗中心的144例HCC患者，分别将111例患者作为训练队列，33例患者作为测试队列。分别选择放射组学特征和临床变量构建放射组学模型和临床模型。然后构建一个联合逻辑回归模型，即肝衰竭（LF）模型，该模型纳入了所开发的放射组学特征和临床危险因素。通过绘制受试者操作特征（ROC）曲线并计算曲线下面积（AUC）及其95%置信区间（CI）来评估和比较这些模型的性能。在训练队列和测试队列中，放射组学模型在预测HCC患者PHLF方面的AUC高于临床模型。此外，与放射组学模型和临床模型相比，LF模型在两个队列中的AUC最高[分别为0.956（95%CI：0.955 - 0.962）和0.844（95%CI：0.833 - 0.886）]。我们评估了MRI图像的定量放射组学特征，并提出了一种经过外部验证的基于放射组学的临床模型，即用于预测HCC患者PHLF的LF模型，该模型可协助临床医生在手术前制定治疗策略。
