Preoperative noninvasive prediction of pathological grading of urothelial carcinoma of bladder with a nomogram model based on ultrasound features and inflammatory indicators
10.16781/j.CN31-2187/R.20250280
- VernacularTitle:基于超声特征与炎症指标列线图模型的膀胱尿路上皮癌病理分级术前无创预测研究
- Author:
Le TAO
1
;
Hao ZHANG
;
Qunqun ZHOU
;
Tingting LIN
;
Dan FAN
;
Chang LU
;
Hejing HUANG
Author Information
1. 海军军医大学(第二军医大学)第二附属医院超声诊断科,上海 200003
- Keywords:
urinary bladder neoplasms;
urothelial carcinoma of bladder;
ultrasonography;
inflammation factors;
nomogram;
pathological grading
- From:
Academic Journal of Naval Medical University
2025;46(10):1304-1312
- CountryChina
- Language:Chinese
-
Abstract:
Objective To evaluate the value of a nomogram model based on ultrasonographic features and inflammatory indicators in the preoperative noninvasive prediction of pathological grading of urothelial carcinoma of bladder(UCB).Methods A retrospective analysis was conducted on 471 patients with pathologically confirmed UCB,and the patients were assigned to high-grade group(401 cases)or low-grade group(70 cases).Basic clinical data(gender,age,macroscopic hematuria),ultrasonographic features(lesion location,blood flow signal,etc.),and blood inflammatory indicators(e.g.neutrophil-to-lymphocyte ratio[NLR])were collected.Independent predictors were screened using univariate and multivariate logistic regression,and a nomogram model was constructed.Model performance was evaluated using the receiver operating characteristic(ROC)curve,calibration curve,and decision curve analysis(DCA).Results Multivariate logistic analysis identified gender(odds ratio[OR]=2.68),age(OR=1.08),macroscopic hematuria(OR=3.19),lesion located in the trigone(OR=4.59),positive blood flow signal(OR=2.87),and NLR(OR=1.03)were independent predictors of high-grade UCB(all P＜0.05).The combined model(clinical features+ultrasonographic characteristics+inflammatory indicators)achieved an area under curve(AUC)of 0.892,which was significantly higher than the clinical feature-only model(AUC=0.799)and the clinical+ultrasonographic model(AUC=0.856).The calibration curve demonstrated good consistency between predicted and actual outcomes,and DCA confirmed its optimal clinical net benefit.Conclusion The nomogram model integrating clinical features,ultrasonographic characteristics,and inflammatory indicators can effectively discriminate UCB pathological grading,providing a reliable preoperative noninvasive assessment tool for personalized treatment decisions.
