Graduate College, Southern Medical University, Guangzhou, 510515, China.
Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou, 510080, China.
Abdom Radiol (NY). 2017 Jun;42(6):1695-1704. doi: 10.1007/s00261-017-1072-0.
To develop a CT-based radiomics signature and assess its ability for preoperatively predicting the early recurrence (≤1 year) of hepatocellular carcinoma (HCC).
A total of 215 HCC patients who underwent partial hepatectomy were enrolled in this retrospective study, and all the patients were followed up at least within 1 year. Radiomics features were extracted from arterial- and portal venous-phase CT images, and a radiomics signature was built by the least absolute shrinkage and selection operator (LASSO) logistic regression model. Preoperative clinical factors associated with early recurrence were evaluated. A radiomics signature, a clinical model, and a combined model were built, and the area under the curve (AUC) of operating characteristics (ROC) was used to explore their performance to discriminate early recurrence.
Twenty-one radiomics features were chosen from 300 candidate features to build a radiomics signature that was significantly associated with early recurrence (P < 0.001), and they presented good performance in the discrimination of early recurrence alone with an AUC of 0.817 (95% CI: 0.758-0.866), sensitivity of 0.794, and specificity of 0.699. The AUCs of the clinical and combined models were 0.781 (95% CI: 0.719-0.834) and 0.836 (95% CI: 0.779-0.883), respectively, with the sensitivity being 0.784 and 0.824, and the specificity being 0.619 and 0.708, respectively. Adding a radiomics signature into conventional clinical variables can significantly improve the accuracy of the preoperative model in predicting early recurrence (P = 0.01).
The radiomics signature was a significant predictor for early recurrence in HCC. Incorporating radiomics signature into conventional clinical factors performed better for preoperative estimation of early recurrence than with clinical variables alone.
开发一种基于 CT 的放射组学特征，并评估其术前预测肝细胞癌（HCC）早期复发（≤1 年）的能力。
本回顾性研究共纳入 215 例接受部分肝切除术的 HCC 患者，所有患者均至少随访 1 年。从动脉期和门静脉期 CT 图像中提取放射组学特征，并通过最小绝对值收缩和选择算子（LASSO）逻辑回归模型构建放射组学特征。评估与早期复发相关的术前临床因素。建立放射组学特征、临床模型和联合模型，并通过接受者操作特征（ROC）曲线下面积（AUC）评估其鉴别早期复发的性能。
从 300 个候选特征中选择 21 个放射组学特征构建放射组学特征，该特征与早期复发显著相关（P＜0.001），单独用于鉴别早期复发的效能良好，AUC 为 0.817（95%CI：0.758-0.866），灵敏度为 0.794，特异度为 0.699。临床模型和联合模型的 AUC 分别为 0.781（95%CI：0.719-0.834）和 0.836（95%CI：0.779-0.883），灵敏度分别为 0.784 和 0.824，特异度分别为 0.619 和 0.708。在常规临床变量中加入放射组学特征可显著提高术前模型预测早期复发的准确性（P＝0.01）。
放射组学特征是 HCC 早期复发的重要预测指标。将放射组学特征纳入常规临床因素可改善术前早期复发的预测效果，优于单纯使用临床变量。
