Lu Xiaofan, Wang Yang, Jiang Liyun, Gao Jun, Zhu Yue, Hu Wenjun, Wang Jiashuo, Ruan Xinjia, Xu Zhengbao, Meng Xiaowei, Zhang Bing, Yan Fangrong
Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China.
Department of Radiology, The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School, Nanjing, China.
Front Oncol. 2019 Jun 21;9:488. doi: 10.3389/fonc.2019.00488. eCollection 2019.
The status of lymph node (LN) metastases plays a decisive role in the selection of surgical procedures and post-operative treatment. Several histopathologic features, known as predictors of LN metastasis, are commonly available post-operatively. Medical imaging improved pre-operative diagnosis, but the results are not fully satisfactory due to substantial false positives. Thus, a reliable and robust method for pre-operative assessment of LN status is urgently required. We developed a prediction model in a training set from the TCGA-BLCA cohort including 196 bladder urothelial carcinoma samples with confirmed LN metastasis status. Least absolute shrinkage and selection operator (LASSO) regression was harnessed for dimension reduction, feature selection, and LNM signature building. Multivariable logistic regression was used to develop the prognostic model, incorporating the LNM signature, and a genomic mutation of , and was presented with a LNM nomogram. The performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. Internal validation was evaluated by the testing set from the TCGA cohort and independent validation was assessed by two independent cohorts. The LNM signature, which consisted of 48 selected features, was significantly associated with LN status ( < 0.005 for both the training and testing sets of the TCGA cohort). Predictors contained in the individualized prediction nomogram included the LNM signature and mutation status. The model demonstrated good discrimination, with an area under the curve (AUC) of 98.7% (85.3% for testing set) and good calibration with = 0.973 (0.485 for testing set) in the Hosmer-Lemeshow goodness of fit test. Decision curve analysis demonstrated that the LNM nomogram was clinically useful. This study presents a pre-operative nomogram incorporating a LNM signature and a genomic mutation, which can be conveniently utilized to facilitate pre-operative individualized prediction of LN metastasis in patients with bladder urothelial carcinoma.
淋巴结（LN）转移状态在手术方式的选择和术后治疗中起着决定性作用。一些组织病理学特征，即已知的LN转移预测指标，通常在术后可得。医学影像改善了术前诊断，但由于大量假阳性结果，其效果并不完全令人满意。因此，迫切需要一种可靠且稳健的术前评估LN状态的方法。我们在来自TCGA - BLCA队列的训练集中开发了一个预测模型，该队列包括196个具有确诊LN转移状态的膀胱尿路上皮癌样本。利用最小绝对收缩和选择算子（LASSO）回归进行降维、特征选择和构建LNM特征。使用多变量逻辑回归来开发预后模型，纳入LNM特征和一种基因组突变，并呈现了一个LNM列线图。根据校准、区分度和临床实用性对列线图的性能进行评估。通过TCGA队列的测试集进行内部验证，并通过两个独立队列进行独立验证。由48个选定特征组成的LNM特征与LN状态显著相关（TCGA队列的训练集和测试集的P值均<0.005）。个体化预测列线图中包含的预测指标包括LNM特征和一种突变状态。该模型显示出良好的区分度，曲线下面积（AUC）为98.7%（测试集为85.3%），在Hosmer - Lemeshow拟合优度检验中校准良好，Hosmer - Lemeshow检验的χ²值为0.973（测试集为0.485）。决策曲线分析表明LNM列线图具有临床实用性。本研究提出了一种纳入LNM特征和基因组突变的术前列线图，可方便地用于促进膀胱尿路上皮癌患者术前LN转移的个体化预测。
