我们的目的是开发和验证术前列线图，预测低级别、非肌层浸润性上尿路尿路上皮癌 (LG-NMI UTUC)，从而帮助准确选择内镜治疗 (EM) 候选者。这是一项回顾性研究其中包括 454 名接受根治性手术的患者（队列 1 和队列 2），以及 26 名接受 EM 的患者（队列 3）。利用多元逻辑回归模型，根据队列 1 的数据开发了预测 LG-NMI UTUC 的列线图。该列线图的准确性与传统的欧洲泌尿外科协会 (EAU) 和国家综合癌症网络 (NCCN) 模型进行了比较。使用队列 2 数据进行外部验证，并通过队列 3 中的疾病进展指标评估列线图的预后价值。在队列 1 中，多变量分析强调影像学上不存在侵袭性疾病（比值比 [OR] 7.04；p = 0.011） 、不存在肾积水（OR 2.06；p = 0.027）、乳头状结构（OR 24.9；p < 0.001）和缺乏高级别尿细胞学检查（OR 0.22；p < 0.001）作为 LG-NMI 疾病的独立预测因素。该列线图在预测准确性方面优于两种传统模型（0.869 vs. 0.759-0.821），并且在决策曲线分析中表现出更高的净效益。该模型的临床疗效在队列 2 中得到了证实。此外，列线图分层了队列 3 中的疾病无进展生存率。我们的列线图 (https://kmur.shinyapps.io/UTUC_URS/) 准确预测了 LG-NMI UTUC，从而确定适合 EM 的候选人。此外，该模型还可作为对接受 EM 的患者进行预后分层的有用工具。© 2023。外科肿瘤学会。

We aimed to develop and validate a preoperative nomogram that predicts low-grade, non-muscle invasive upper urinary tract urothelial carcinoma (LG-NMI UTUC), thereby aiding in the accurate selection of endoscopic management (EM) candidates.This was a retrospective study that included 454 patients who underwent radical surgery (Cohort 1 and Cohort 2), and 26 patients who received EM (Cohort 3). Utilizing a multivariate logistic regression model, a nomogram predicting LG-NMI UTUC was developed based on data from Cohort 1. The nomogram's accuracy was compared with conventional European Association of Urology (EAU) and National Comprehensive Cancer Network (NCCN) models. External validation was performed using Cohort 2 data, and the nomogram's prognostic value was evaluated via disease progression metrics in Cohort 3.In Cohort 1, multivariate analyses highlighted the absence of invasive disease on imaging (odds ratio [OR] 7.04; p = 0.011), absence of hydronephrosis (OR 2.06; p = 0.027), papillary architecture (OR 24.9; p < 0.001), and lack of high-grade urine cytology (OR 0.22; p < 0.001) as independent predictive factors for LG-NMI disease. The nomogram outperformed the two conventional models in predictive accuracy (0.869 vs. 0.759-0.821) and exhibited a higher net benefit in decision curve analysis. The model's clinical efficacy was corroborated in Cohort 2. Moreover, the nomogram stratified disease progression-free survival rates in Cohort 3.Our nomogram ( https://kmur.shinyapps.io/UTUC_URS/ ) accurately predicts LG-NMI UTUC, thereby identifying suitable candidates for EM. Additionally, the model serves as a useful tool for prognostic stratification in patients undergoing EM.© 2023. Society of Surgical Oncology.