Preoperative radiomics nomogram for microvascular invasion prediction in hepatocellular carcinoma using contrast-enhanced CT
Preoperative radiomics nomogram for microvascular invasion prediction in hepatocellular carcinoma using contrast-enhanced CT
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DOI：
10.1007/s00330-018-5985-y
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发表时间：
2019-07-01
影响因子：
5.9
通讯作者：
Tian, Jie
中科院分区：
文献类型：
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作者：
Ma, Xiaohong;Wei, Jingwei;Tian, Jie
ObjectivesTo开发和验证放射组学诺模图术前预测微血管浸润（MVI）在肝细胞癌（HCC）hepatocellular carcinoma.MethodsThe研究包括157例经组织学证实的HCC与或不MVI，和110例患者被分配到训练数据集和47验证数据集。从我们的病历中收集基线临床因素（CF）数据，并从所有患者术前获得的CT的动脉期（AP）、门静脉期（PVP）和延迟期（DP）中提取放射组学特征。放射组学分析包括肿瘤分割、特征提取、模型构建和模型评价。建立了预测HCC MVI的诺模图。诺模图的性能进行了评估，通过校准和歧视statistics.ResultsFive AP功能，7 PVP功能和9 DP功能是有效的MVI预测肝癌放射组学签名。PVP放射组学特征在验证数据集中表现出比AP和DP放射组学特征更好的性能，AUC为0.793。在临床模型中，年龄、肿瘤最大直径、甲胎蛋白和B型肝炎抗原是有效的预测因子。最终的列线图整合了PVP放射组学特征和四个CF。在训练数据集和验证数据集中，诺模图都实现了良好的校准，C指数分别为0.827和0.820。决策曲线分析表明，提出的诺模图是临床上有用的，与相应的净效益为0.357。结论上述放射组学列线图可以在术前预测HCC患者的MVI，并可能构成一个有用的临床工具，以指导随后的个性化治疗。关键点中心点以前没有报道的研究利用放射组学列线图术前预测HCC的MVI，使用3D造影剂，增强CTimaging.center放射组学临床因子（CF）诺模图预测MVI的性能上级放射组学特征或CF诺模图，alone.center结合PVP放射组学和CF的诺模图可用作术前预测HCC MVI的成像标记，并可指导个性化治疗。
ObjectivesTo develop and validate a radiomics nomogram for preoperative prediction of microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC).MethodsThe study included 157 patients with histologically confirmed HCC with or without MVI, and 110 patients were allocated to the training dataset and 47 to the validation dataset. Baseline clinical factor (CF) data were collected from our medical records, and radiomics features were extracted from the artery phase (AP), portal venous phase (PVP) and delay phase (DP) of preoperatively acquired CT in all patients. Radiomics analysis included tumour segmentation, feature extraction, model construction and model evaluation. A final nomogram for predicting MVI of HCC was established. Nomogram performance was assessed via both calibration and discrimination statistics.ResultsFive AP features, seven PVP features and nine DP features were effective for MVI prediction in HCC radiomics signatures. PVP radiomics signatures exhibited better performance than AP and DP radiomics signatures in the validation datasets, with the AUC 0.793. In the clinical model, age, maximum tumour diameter, alpha-fetoprotein and hepatitis B antigen were effective predictors. The final nomogram integrated the PVP radiomics signature and four CFs. Good calibration was achieved for the nomogram in both the training and validated datasets, with respective C-indexes of 0.827 and 0.820. Decision curve analysis suggested that the proposed nomogram was clinically useful, with a corresponding net benefit of 0.357.ConclusionsThe above-described radiomics nomogram can preoperatively predict MVI in patients with HCC and may constitute a usefully clinical tool to guide subsequent personalised treatment.Key Points center dot No previously reported study has utilised radiomics nomograms to preoperatively predict the MVI of HCC using 3D contrast-enhanced CT imaging.center dot The combined radiomics clinical factor (CF) nomogram for predicting MVI achieved superior performance than either the radiomics signature or the CF nomogram alone.center dot Nomograms combing PVP radiomics and CF may be useful as an imaging marker for predicting MVI of HCC preoperatively and could guide personalised treatment.