Department of Radiology, Zhengzhou University People's Hospital (Henan Provincial People's Hospital), Zhengzhou, 450003, Henan, China.
National Digital Switching System Engineering & Technological R&D Center, Zhengzhou, 450002, Henan, China.
Abdom Radiol (NY). 2020 Jan;45(1):64-72. doi: 10.1007/s00261-019-02198-7.
To appraise the ability of the computed tomography (CT) radiomics signature for prediction of early recurrence (ER) in patients with hepatocellular carcinoma (HCC).
A set of 325 HCC patients were enrolled in this retrospective study and the whole dataset was divided into 2 cohorts, including "training set" (225 patients) and "test set" (100 patients). All patients who underwent partial hepatectomy were followed up at least within 1 year. 656 Radiomics features were extracted from arterial-phase and portal venous-phase CT images. Lasso regression model was used for data dimension reduction, feature selection, and radiomics signature building. Univariate analysis was used to identify clinical and radiomics significant features. Models (radiomics signature, clinical model, and combined model) were evaluated by area under the curve (AUC) of receiver operating characteristic curve. The models' performances for prediction of ER were assessed.
The radiomics signature was built by 14 selected radiomics features and was significantly associated with ER (P < 0.001); the AUCs of the "train set" and the "test set" were 0.818 (95% CI 0.760-0.865) and 0.719 (95% CI 0.621-0.805), respectively. The tumor size, tumor capsule, and γ-glutamyl transferase (GGT) were significantly associated with ER in the clinical model (P < 0.05). The combined model showed incremental prognostic value, with the AUCs of "training dataset" and "test dataset" were 0.846 (95% CI 0.792-0.890) and 0.737 (95% CI 0.640-0.820), respectively. The radiomics signature, tumor size, and the level of GGT were independent predictors of ER (P < 0.05).
The CT radiomics signature can be conveniently used to predict the ER in patient with HCC. The combined model performed better for prediction of ER than radiomics signature or clinical model.
评价 CT 放射组学特征预测肝细胞癌（HCC）患者早期复发（ER）的能力。
本回顾性研究纳入了 325 例 HCC 患者，将全数据集分为 2 个队列，包括“训练集”（225 例患者）和“测试集”（100 例患者）。所有接受部分肝切除术的患者均至少随访 1 年。从动脉期和门静脉期 CT 图像中提取 656 个放射组学特征。使用 Lasso 回归模型进行数据降维、特征选择和放射组学特征构建。单因素分析用于确定临床和放射组学显著特征。通过受试者工作特征曲线下面积（AUC）评估模型（放射组学特征、临床模型和联合模型）。评估模型预测 ER 的性能。
构建了由 14 个选定的放射组学特征组成的放射组学特征，与 ER 显著相关（P<0.001）；“训练集”和“测试集”的 AUC 分别为 0.818（95%CI 0.760-0.865）和 0.719（95%CI 0.621-0.805）。在临床模型中，肿瘤大小、肿瘤包膜和γ-谷氨酰转移酶（GGT）与 ER 显著相关（P<0.05）。联合模型显示出增量预测价值，“训练数据集”和“测试数据集”的 AUC 分别为 0.846（95%CI 0.792-0.890）和 0.737（95%CI 0.640-0.820）。放射组学特征、肿瘤大小和 GGT 水平是 ER 的独立预测因素（P<0.05）。
CT 放射组学特征可方便地用于预测 HCC 患者的 ER。联合模型在预测 ER 方面优于放射组学特征或临床模型。
