Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer
Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer
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用于术前预测结直肠癌淋巴结转移的放射组学列线图的开发和验证
DOI：
10.1200/jco.2015.65.9128
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发表时间：
2016-06-20
影响因子：
45.3
通讯作者：
Liu, Zai-yi
中科院分区：
文献类型：
--
作者：
Huang, Yan-qi;Liang, Chang-hong;Liu, Zai-yi
目的建立并验证用于术前预测结直肠癌（CRC）患者淋巴结（LN）转移的放射组学特征图。患者和方法该预测模型是在一个主要队列中建立的，该队列包括326例临床病理证实的结直肠癌患者，数据收集于2007年1月至2010年4月。从结直肠癌门静脉期计算机断层扫描（CT）中提取放射学特征。采用Lasso回归模型进行数据降维、特征选择和放射组学签名构建。多变量logistic回归分析用于建立预测模型，我们结合放射组学特征，ct报告的LN状态和独立的临床病理危险因素，并通过放射组学nomogram来呈现。对nomogram的校准、鉴别和临床实用性进行了评估。进行内部验证评估。2010年5月至2011年12月，独立验证队列包含200例连续患者。结果放射组学特征，包括24个选定的特征，与LN状态显著相关（对于主要队列和验证队列P < 0.001）。个体化预测图中的预测因子包括放射组学特征、ct报告的LN状态和癌胚抗原水平。在nomogram中加入组织学分级并不能显示出增加的预后价值。模型判别性好，C-index为0.736（内部验证C-index分别为0.759和0.766），定标性好。在验证队列中应用nomogram仍然具有良好的判别性（C-index, 0.778 [95% CI, 0.769 ~ 0.787]）和良好的校准。决策曲线分析表明放射组学图在临床上是有用的。结论本研究提出了一种结合放射组学特征、ct报告的淋巴结状态和临床危险因素的放射组学nomographic，可方便地用于CRC患者淋巴结转移的术前个体化预测。(C) 2016年由美国临床肿瘤学会出版
PurposeTo develop and validate a radiomics nomogram for preoperative prediction of lymph node (LN) metastasis in patients with colorectal cancer (CRC).Patients and MethodsThe prediction model was developed in a primary cohort that consisted of 326 patients with clinicopathologically confirmed CRC, and data was gathered from January 2007 to April 2010. Radiomic features were extracted from portal venous-phase computed tomography (CT) of CRC. Lasso regression model was used for data dimension reduction, feature selection, and radiomics signature building. Multivariable logistic regression analysis was used to develop the predicting model, we incorporated the radiomics signature, CT-reported LN status, and independent clinicopathologic risk factors, and this was presented with a radiomics nomogram. The performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. Internal validation was assessed. An independent validation cohort contained 200 consecutive patients from May 2010 to December 2011.ResultsThe radiomics signature, which consisted of 24 selected features, was significantly associated with LN status (P < .001 for both primary and validation cohorts). Predictors contained in the individualized prediction nomogram included the radiomics signature, CT-reported LN status, and carcinoembryonic antigen level. Addition of histologic grade to the nomogram failed to show incremental prognostic value. The model showed good discrimination, with a C-index of 0.736 (C-index, 0.759 and 0.766 through internal validation), and good calibration. Application of the nomogram in the validation cohort still gave good discrimination (C-index, 0.778 [95% CI, 0.769 to 0.787]) and good calibration. Decision curve analysis demonstrated that the radiomics nomogram was clinically useful.ConclusionThis study presents a radiomics nomogram that incorporates the radiomics signature, CT-reported LN status, and clinical risk factors, which can be conveniently used to facilitate the preoperative individualized prediction of LN metastasis in patients with CRC. (C) 2016 by American Society of Clinical Oncology