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  • Title: [Radiomics nomogram of MR: a prediction of cervical lymph node metastasis in laryngeal cancer].
    Author: Jia CL, Cao Y, Song Q, Zhang WB, Li JJ, Wu XX, Yu PY, Mou YK, Mao N, Song XC.
    Journal: Zhonghua Er Bi Yan Hou Tou Jing Wai Ke Za Zhi; 2020 Dec 07; 55(12):1154-1161. PubMed ID: 33342131.
    Abstract:
    Objective: To establish and validate a radiomics nomogram based on MR for predicting cervical lymph node metastasis in laryngeal cancer. Methods: One hundred and seventeen patients with laryngeal cancer who underwent MR examinations and received open surgery and neck dissection between January 2016 and December 2019 were included in this study. All patients were randomly divided into a training cohort (n=89) and test cohort (n=28) using computer-generated random numbers. Clinical characteristics and MR were collected. Radiological features were extracted from the MR images. Enhanced T1 and T2WI were selected for radiomics analysis, and the volume of interest was manually segmented from the Huiyihuiying radiomics cloud platform. The variance analysis (ANOVA) and the least absolute shrinkage and selection operator (LASSO) algorithm were used to reduce the dimensionality of the radiomics features in the training cohort. Then, a radiomic signature was established. The clinical risk factors were screened by using ANOVA and multivariate logistic regression. A nomogram was generated using clinical risk factors and the radiomic signature. The calibration curve and receiver operator characteristic (ROC) curve were used to confirm the nomogram's performance in the training and test sets. The clinical usefulness of the nomogram was evaluated by decision curve analysis (DCA). Furthermore, a testing cohort was used to validate the model. Results: The radiomics signature consisted of 21 features, and the nomogram model included the radiomics signature and the MR-reported lymph node status. The model showed good calibration and discrimination. The model yielded areas under the ROC curve (AUC) in the training cohort, specificity, and sensitivity of 0.930, 0.930 and 0.875. In the test cohort, the model yielded AUC, specificity and sensitivity of 0.883, 0.889 and 0.800. DCA indicated that the nomogram model was clinically useful. Conclusion: The MR-based radiomics nomogram model may be used to predict cervical lymph node metastasis of laryngeal cancer preoperatively. MR-based radiomics could serve as a potential tool to help clinicians make an optimal clinical decision. 目的： 探讨基于MR的影像组学列线图预测喉癌颈淋巴结转移的临床价值。 方法： 采用回顾性队列研究，收集2016年1月至2019年12月117例在烟台毓璜顶医院接受开放式手术并颈淋巴清扫的喉癌患者治疗前的临床资料及MR资料，完全随机法以约3∶1比例分为训练集89例和测试集28例。放射组学云平台（汇医慧影）上手动分割增强T1和T2WI的原发肿瘤感兴趣容积，提取影像组学特征。在训练集中，使用方差分析（analysis of variance，ANOVA）与套索算法（least absolute shrinkage and selection operator，LASSO）对影像组学特征进行降维，根据各自的加权系数建立影像组学标签。单因素ANOVA和多因素Logistic回归分析，确定颈淋巴转移的高危因素。联合高危因素和影像组学标签建立列线图。采用受试者工作特征（receiver operating characteristic，ROC）曲线、校准曲线评估列线图的效能，决策曲线分析（decision curve analysis，DCA）评估列线图的临床应用价值。测试集数据用于验证模型。 结果： 经降维后剩余21个影像组学特征。纳入MR淋巴结状态及影像组学标签建立列线图模型。ROC曲线和校准曲线均显示出良好的预测效能；在训练集中，列线图模型的曲线下面积（area under curve，AUC）、特异性、敏感性分别为0.930、0.930、0.875；在测试集中，AUC、特异性、敏感性分别为0.883、0.889、0.800。DCA显示列线图预测颈部淋巴结转移有一定的临床获益。 结论： 基于MR的影像组学列线图对喉癌患者术前颈淋巴结转移具有良好的预测效能，具有潜在的临床应用价值。.
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