A Radiomics Nomogram for the Preoperative Prediction of Lymph Node Metastasis in Bladder Cancer
A Radiomics Nomogram for the Preoperative Prediction of Lymph Node Metastasis in Bladder Cancer
复制标题
术前预测膀胱癌淋巴结转移的放射组学列线图
DOI：
10.1158/1078-0432.ccr-17-1510
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发表时间：
2017-11-15
影响因子：
11.5
通讯作者：
Lin, Tianxin
中科院分区：
文献类型：
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作者：
Wu, Shaoxu;Zheng, Junjiong;Lin, Tianxin
目的：建立并验证放射组学列线图用于膀胱癌淋巴结（LN）转移的术前预测。实验设计：共118例符合条件的膀胱癌患者被分为训练集（n = 80）和验证集（n = 38）。从每个患者的动脉期CT图像中提取放射组学特征。然后在训练集中用最小绝对收缩和选择算子算法构建放射组学签名。结合独立的危险因素，用多变量logistic回归模型建立放射组学诺模图。在训练集中评估诺模图性能，并在验证集中进行验证。最后，决策曲线分析与组合的训练和验证集，以估计诺模图的临床有用性。结果：由9个LN状态相关特征组成的放射组学特征具有良好的预测效果。放射组学诺模图（其结合了放射组学特征和CT报告的LN状态）在训练集[AUC，0.9262; 95%置信区间（CI），0.8657-0.9868]和验证集（AUC，0.8986; 95% CI，0.7613-0.9901）中也显示出良好的校准和区分。决策曲线表明我们的诺模图的临床实用性。令人鼓舞的是，诺模图还显示了CT报告的LN阴性（cN 0）亚组的良好区分能力（AUC，0.8810; 95% CI，0.8021-0.9598）。结论：放射组学列线图是一种非侵入性术前预测工具，结合放射组学特征和CT报告的LN状态，显示出膀胱癌患者LN转移的良好预测准确性。需要多中心验证以获得其临床应用的高水平证据。临床癌症研究; 23（22）; 6904-11。©2017 AACR.
Purpose: To develop and validate a radiomics nomogram for the preoperative prediction of lymph node (LN) metastasis in bladder cancer. Experimental Design: A total of 118 eligible bladder cancer patients were divided into a training set (n = 80) and a validation set (n = 38). Radiomics features were extracted from arterial-phase CT images of each patient. A radiomics signature was then constructed with the least absolute shrinkage and selection operator algorithm in the training set. Combined with independent risk factors, a radiomics nomogram was built with a multivariate logistic regression model. Nomogram performance was assessed in the training set and validated in the validation set. Finally, decision curve analysis was performed with the combined training and validation set to estimate the clinical usefulness of the nomogram. Results: The radiomics signature, consisting of nine LN status–related features, achieved favorable prediction efficacy. The radiomics nomogram, which incorporated the radiomics signature and CT-reported LN status, also showed good calibration and discrimination in the training set [AUC, 0.9262; 95% confidence interval (CI), 0.8657–0.9868] and the validation set (AUC, 0.8986; 95% CI, 0.7613–0.9901). The decision curve indicated the clinical usefulness of our nomogram. Encouragingly, the nomogram also showed favorable discriminatory ability in the CT-reported LN-negative (cN0) subgroup (AUC, 0.8810; 95% CI, 0.8021–0.9598). Conclusions: The presented radiomics nomogram, a noninvasive preoperative prediction tool that incorporates the radiomics signature and CT-reported LN status, shows favorable predictive accuracy for LN metastasis in patients with bladder cancer. Multicenter validation is needed to acquire high-level evidence for its clinical application. Clin Cancer Res; 23(22); 6904–11. ©2017 AACR.