A radiomics approach to predict lymph node metastasis and clinical outcome of intrahepatic cholangiocarcinoma
A radiomics approach to predict lymph node metastasis and clinical outcome of intrahepatic cholangiocarcinoma
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预测肝内胆管癌淋巴结转移和临床结果的放射组学方法
DOI：
10.1007/s00330-019-06142-7
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发表时间：
2019-07-01
影响因子：
5.9
通讯作者：
Wang, Xue-Hao
中科院分区：
文献类型：
--
作者：
Ji, Gu-Wei;Zhu, Fei-Peng;Wang, Xue-Hao
ObjectivesThis study was conducted in order to establish and validate a radiomics model for predicting lymph node（LN）metastasis of intrahepatic cholangiocarcinoma（IHC）and to determine its pregnancy values.MethodsFor this retrospective study，a radiomics model was developed in a primary cohort of 103 IHC patients who undertaken curative-intent resection and lymphadenectomy.从动脉期计算机断层扫描（CT）扫描中提取放射组学特征。放射组学签名是基于高度可重复的特征，使用最小绝对收缩和选择算子（LASSO）方法建立的。采用多因素Logistic回归分析建立放射组学模型，将放射组学特征和其他独立预测因素结合起来。模型性能由其鉴别、校准和临床有用性决定。该模型进行了内部验证，在52个连续patients.ResultsThe放射组学签名包括8 LN状态相关的功能，并表现出显着的关联与LN转移在两个队列（p< 0.001）。结合放射组学特征和CA 19-9水平的放射组学列线图在主要队列（AUC 0.8462）和验证队列（AUC 0.8921）中显示出良好的校准和区分。令人鼓舞的是，放射组学列线图在CT报告的LN阴性亚组中得出的AUC为0.9224。决策曲线分析证实了该诺模图的临床实用性。高转移风险患者的总生存率和无复发生存率显著低于低转移风险患者（均P < 0.001）。放射组学列线图是一个独立的术前预测的整体和无复发surviv.ConclusionsOur放射组学模型提供了一个强大的诊断工具，预测LN转移，特别是在CT报告LN阴性IHC患者，这可能有助于临床决策。关键点·放射组学列线图显示出良好的性能预测LN转移的IHC患者，特别是在CT报告LN阴性亚组。·高危患者在根治性切除后的预后仍然很差。·放射组学模型可以促进临床决策，并定义从手术中受益最多的患者子集。
ObjectivesThis study was conducted in order to establish and validate a radiomics model for predicting lymph node (LN) metastasis of intrahepatic cholangiocarcinoma (IHC) and to determine its prognostic value.MethodsFor this retrospective study, a radiomics model was developed in a primary cohort of 103 IHC patients who underwent curative-intent resection and lymphadenectomy. Radiomics features were extracted from arterial phase computed tomography (CT) scans. A radiomics signature was built based on highly reproducible features using the least absolute shrinkage and selection operator (LASSO) method. Multivariate logistic regression analysis was adopted to establish a radiomics model incorporating radiomics signature and other independent predictors. Model performance was determined by its discrimination, calibration, and clinical usefulness. The model was internally validated in 52 consecutive patients.ResultsThe radiomics signature comprised eight LN-status–related features and showed significant association with LN metastasis in both cohorts (p< 0.001). A radiomics nomogram that incorporates radiomics signature and CA 19-9 level showed good calibration and discrimination in the primary cohort (AUC 0.8462) and validation cohort (AUC 0.8921). Promisingly, the radiomics nomogram yielded an AUC of 0.9224 in the CT-reported LN-negative subgroup. Decision curve analysis confirmed the clinical utility of this nomogram. High risk for metastasis portended significantly lower overall and recurrence-free survival than low risk for metastasis (bothp< 0.001). The radiomics nomogram was an independent preoperative predictor of overall and recurrence-free survival.ConclusionsOur radiomics model provided a robust diagnostic tool for prediction of LN metastasis, especially in CT-reported LN-negative IHC patients, that may facilitate clinical decision-making.Key Points• The radiomics nomogram showed good performance for prediction of LN metastasis in IHC patients, particularly in the CT-reported LN-negative subgroup.• Prognosis of high-risk patients remains dismal after curative-intent resection.• The radiomics model may facilitate clinical decision-making and define patient subsets benefiting most from surgery.