本研究旨在构建一个临床实用模型，精确预测膀胱癌患者的淋巴结（LN）转移。我们包括了四个独立的队列，并应用最小绝对收缩和选择算子回归与多元 logistic 回归。通过准确性、敏感性、特异性和曲线下面积 (AUC) 来比较 LN 得分与 CT/MRI 的诊断效果。共纳入 606 名患者建立基本预测模型。经过多步基因选择后，我们构建了一个包含 5 个基因的 LN 转移预测模型。该模型可准确预测 LN 转移，AUC 为 0.781。为了满足临床实际应用需求，我们利用 SYSMH 队列 (n = 105) 将该模型转化为快速 LN 评分系统。高 LN 得分患者的 LN 转移率为 72.2%，低 LN 得分患者的 LN 转移率为 3.4%。LN 得分的准确性优于 CT/MRI (0.882 vs. 0.727)。应用 LN 得分可以纠正 CT/MRI 误诊的 88%（22/25）患者的诊断。临床实用的 LN 得分可以精确、快速、方便地预测 LN 分期，并帮助术前诊断 LN 转移和指导精确治疗。© 2023. 作者，由 Springer Nature Limited 独家许可。

The aim of this study was to construct a clinically practical model to precisely predict lymph node (LN) metastasis in bladder cancer patients.Four independent cohorts were included. The least absolute shrinkage and selection operator regression with multivariate logistic regression were applied. The diagnostic efficacy of LN score and CT/MRI was compared by accuracy, sensitivity, specificity, and area under curve (AUC).A total of 606 patients were included to develop a basic prediction model. After multistep gene selection, the LN metastasis prediction model was constructed with 5 genes. The model can accurately predict LN metastasis with an AUC of 0.781. For clinically practical use, we transformed the model into a Fast LN Scoring System using the SYSMH cohort (n = 105). High LN score patients exhibited a 72.2% LN metastasis rate, while low LN score patients showed a 3.4% LN metastasis rate. The LN score achieved a superior accuracy than CT/MRI (0.882 vs. 0.727). Application of LN score can correct the diagnosis of 88% (22/25) patients who were misdiagnosed by CT/MRI.The clinically practical LN score can precisely, rapidly, and conveniently predict LN status, which will assist preoperative diagnosis for LN metastasis and guide precise therapy.© 2023. The Author(s), under exclusive licence to Springer Nature Limited.