AI Article Synopsis
- - The study focused on creating and validating a nomogram to predict low-grade, non-muscle invasive upper urinary tract urothelial carcinoma (LG-NMI UTUC), which helps in selecting candidates for endoscopic management (EM).
- - Using data from 454 surgical patients and 26 patients who received EM, the researchers applied a multivariate logistic regression model, finding that the nomogram predicted LG-NMI UTUC more accurately than existing models.
- - The nomogram was shown to be effective in identifying suitable EM candidates and gauging disease progression in patients, making it a valuable tool for clinical decision-making.
Article Abstract
Objective: 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.
Methods: 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.
Results: 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.
Conclusion: 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.
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| http://dx.doi.org/10.1245/s10434-023-14514-z | DOI Listing |
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