Wu Cuiyun, Chen Junfa, Fan Yuqian, Zhao Ming, He Xiaodong, Wei Yuguo, Ge Weidong, Liu Yang
Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, China.
Department of Clinical Pathology, Graduate School, Hebei Medical University, Shijiazhuang, China.
Front Oncol. 2022 Jul 6;12:943942. doi: 10.3389/fonc.2022.943942. eCollection 2022.
The study developed and validated a radiomics nomogram based on a combination of computed tomography (CT) radiomics signature and clinical factors and explored the ability of radiomics for individualized prediction of Ki-67 expression in hepatocellular carcinoma (HCC).
First-order, second-order, and high-order radiomics features were extracted from preoperative enhanced CT images of 172 HCC patients, and the radiomics features with predictive value for high Ki-67 expression were extracted to construct the radiomic signature prediction model. Based on the training group, the radiomics nomogram was constructed based on a combination of radiomic signature and clinical factors that showed an independent association with Ki-67 expression. The area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA) were used to verify the performance of the nomogram.
Sixteen higher-order radiomic features that were associated with Ki-67 expression were used to construct the radiomics signature (AUC: training group, 0.854; validation group, 0.744). In multivariate logistic regression, alfa-fetoprotein (AFP) and Edmondson grades were identified as independent predictors of Ki-67 expression. Thus, the radiomics signature was combined with AFP and Edmondson grades to construct the radiomics nomogram (AUC: training group, 0.884; validation group, 0.819). The calibration curve and DCA showed good clinical application of the nomogram.
The radiomics nomogram developed in this study based on the high-order features of CT images can accurately predict high Ki-67 expression and provide individualized guidance for the treatment and clinical monitoring of HCC patients.
本研究开发并验证了一种基于计算机断层扫描（CT）影像组学特征与临床因素相结合的影像组学列线图，并探讨影像组学对肝细胞癌（HCC）中Ki-67表达进行个体化预测的能力。
从172例HCC患者的术前增强CT图像中提取一阶、二阶和高阶影像组学特征，提取对高Ki-67表达具有预测价值的影像组学特征，构建影像组学特征预测模型。基于训练组，结合与Ki-67表达具有独立相关性的影像组学特征和临床因素构建影像组学列线图。采用受试者工作特征曲线（ROC）下面积、校准曲线和决策曲线分析（DCA）来验证列线图的性能。
使用16个与Ki-67表达相关的高阶影像组学特征构建影像组学特征（AUC：训练组为0.854；验证组为0.744）。在多因素逻辑回归分析中，甲胎蛋白（AFP）和Edmondson分级被确定为Ki-67表达的独立预测因子。因此，将影像组学特征与AFP和Edmondson分级相结合，构建影像组学列线图（AUC：训练组为0.884；验证组为0.819）。校准曲线和DCA显示列线图具有良好的临床应用价值。
本研究基于CT图像高阶特征开发的影像组学列线图能够准确预测高Ki-67表达，为HCC患者的治疗和临床监测提供个体化指导。
