Hepatology Unit and Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, China;Department of Oncology, the First Affiliated Hospital of University of South China, Hengyang, China.
Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, China.
Diagn Interv Radiol. 2020 Sep;26(5):411-419. doi: 10.5152/dir.2020.19623.
The aim of this study was to develop and validate a radiomics nomogram based on radiomics features and clinical data for the non-invasive preoperative prediction of early recurrence (≤2 years) in patients with hepatocellular carcinoma (HCC).
We enrolled 262 HCC patients who underwent preoperative contrast-enhanced computed tomography and curative resection (training cohort, n=214; validation cohort, n=48). We applied propensity score matching (PSM) to eliminate redundancy between clinical characteristics and image features, and the least absolute shrinkage and selection operator (LASSO) was used to prevent overfitting. Next, a radiomics signature, clinical nomogram, and combined clinical-radiomics nomogram were built to predict early recurrence, and we compared the performance and generalization of these models.
The radiomics signature stratified patients into low-risk and high-risk, which show significantly difference in recurrence free survival and overall survival (P ≤ 0.01). Multivariable analysis identified dichotomised radiomics signature, alpha fetoprotein, and tumour number and size as key early recurrence indicators, which were incorporated into clinical and radiomics nomograms. The radiomics nomogram showed the highest area under the receiver operating characteristic curve (AUC), with significantly superior predictive performance over the clinical nomogram in the training cohort (0.800 vs 0.716, respectively; P = 0.001) and the validation cohort (0.785 vs 0.654, respectively; P = 0.039).
The radiomics nomogram is a non-invasive preoperative biomarker for predicting early recurrence in patients with HCC. This model may be of clinical utility for guiding surveillance follow-ups and identifying optimal interventional strategies.
本研究旨在开发和验证一种基于放射组学特征和临床数据的列线图，用于预测肝细胞癌（HCC）患者无侵袭性术前早期复发（≤2 年）。
我们纳入了 262 例接受术前增强 CT 检查和根治性切除术的 HCC 患者（训练队列，n=214；验证队列，n=48）。我们采用倾向评分匹配（PSM）消除临床特征和图像特征之间的冗余，并用最小绝对收缩和选择算子（LASSO）防止过拟合。然后，构建放射组学特征、临床列线图和联合临床-放射组学列线图，以预测早期复发，并比较这些模型的性能和泛化能力。
放射组学特征将患者分为低风险和高风险组，在无复发生存和总生存方面差异有统计学意义（P≤0.01）。多变量分析确定了二分类放射组学特征、甲胎蛋白和肿瘤数量和大小是早期复发的关键指标，将其纳入临床和放射组学列线图。放射组学列线图的受试者工作特征曲线下面积（AUC）最高，在训练队列（分别为 0.800 与 0.716，P=0.001）和验证队列（分别为 0.785 与 0.654，P=0.039）中均显著优于临床列线图。
放射组学列线图是预测 HCC 患者早期复发的一种非侵入性术前生物标志物。该模型可能对指导监测随访和识别最佳干预策略具有临床应用价值。
