Prediction of tumor response via a pretreatment MRI radiomics-based nomogram in HCC treated with TACE.
Prediction of tumor response via a pretreatment MRI radiomics-based nomogram in HCC treated with TACE.
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通过治疗前基于 MRI 放射组学的列线图预测接受 TACE 治疗的 HCC 的肿瘤反应
DOI：
10.1007/s00330-021-07910-0
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发表时间：
2021-10
影响因子：
5.9
通讯作者：
Ji J
中科院分区：
文献类型：
--
作者：
Kong C;Zhao Z;Chen W;Lv X;Shu G;Ye M;Song J;Ying X;Weng Q;Weng W;Fang S;Chen M;Tu J;Ji J
建立和验证基于磁共振成像的肝动脉化疗栓塞术(TACE)前放射组学模型，用于预测中晚期肝细胞癌(HCC)患者的肿瘤反应。纳入99例接受TACE治疗的中晚期肝癌患者(69例接受培训，30例接受TACE治疗)。术前行MRI检查，术后3个月按mRECIST标准评价疗效。从TACE前T2加权图像中提取396个放射组学特征，并应用最小绝对收缩和选择算子(LASSO)回归进行特征选择和模型构建。通过接收机工作特性(ROC)曲线、校准曲线和判决曲线对模型的性能进行了评估。AFP值、Child-Pugh评分和BCLC分期在TACE应答(TR)和无TACE应答(NTR)患者之间有显著差异。用套索选择6个放射组学特征，用每个特征的总和乘以套索的非零系数计算放射组学评分(Rad-Score)。在训练和验证队列中，基于RAD-Score的ROC曲线的AUC分别为0.812和0.866。为了提高诊断效率，Rad-Score进一步与上述临床指标相结合，形成了新的预测诺模图。结果表明，在训练和验证队列中，AUC值分别增加到0.861和0.884。决策曲线分析表明，放射组学诺模图具有一定的临床应用价值。基于放射组学和临床指标的预测诺模图可以很好地预测中晚期肝癌的RR，并可进一步应用于临床预后的辅助诊断。·即使临床病理特征相同的患者，TACE的治疗结果也有很大差异。·放射组学在预测TACE疗效方面表现出色。·决策曲线表明，基于放射组学特征和临床指标的新型预测模型具有较大的临床实用价值。网上版载有补充材料，可在10.1007/s00330-021-07910-0查阅。
To develop and validate a pre-transcatheter arterial chemoembolization (TACE) MRI-based radiomics model for predicting tumor response in intermediate-advanced hepatocellular carcinoma (HCC) patients. Ninety-nine intermediate-advanced HCC patients (69 for training, 30 for validation) treated with TACE were enrolled. MRI examinations were performed before TACE, and the efficacy was evaluated according to the mRECIST criterion 3 months after TACE. A total of 396 radiomics features were extracted from T2-weighted pre-TACE images, and least absolute shrinkage and selection operator (LASSO) regression was applied to feature selection and model construction. The performance of the model was evaluated by receiver operating characteristic (ROC) curves, calibration curves, and decision curves. The AFP value, Child-Pugh score, and BCLC stage showed a significant difference between the TACE response (TR) and non-TACE response (nTR) patients. Six radiomics features were selected by LASSO and the radiomics score (Rad-score) was calculated as the sum of each feature multiplied by the non-zero coefficient from LASSO. The AUCs of the ROC curve based on Rad-score were 0.812 and 0.866 in the training and validation cohorts, respectively. To improve the diagnostic efficiency, the Rad-score was further integrated with the above clinical indicators to form a novel predictive nomogram. Results suggested that the AUC increased to 0.861 and 0.884 in the training and validation cohorts, respectively. Decision curve analysis showed that the radiomics nomogram was clinically useful. The radiomics and clinical indicator-based predictive nomogram can well predict TR in intermediate-advanced HCC and can further be applied for auxiliary diagnosis of clinical prognosis. • The therapeutic outcome of TACE varies greatly even for patients with the same clinicopathologic features. • Radiomics showed excellent performance in predicting the TACE response. • Decision curves demonstrated that the novel predictive model based on the radiomics signature and clinical indicators has great clinical utility. The online version contains supplementary material available at 10.1007/s00330-021-07910-0.
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