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Predictive nomograms for risk and prognostic factors in metastatic bladder cancer: a population-based study.
Shi, Shuibo; Peng, Guangbei; Luo, Longhua; Li, Dongshui.
Afiliación
  • Shi S; Department of Urology, the First Affiliated Hospital of Nanchang University, Nanchang, China.
  • Peng G; Children's Medical Center of Jiangxi Province, Nanchang, China.
  • Luo L; Department of Urology, the First Affiliated Hospital of Nanchang University, Nanchang, China.
  • Li D; Department of Urology, the First Affiliated Hospital of Nanchang University, Nanchang, China.
Transl Cancer Res ; 12(12): 3284-3302, 2023 Dec 31.
Article en En | MEDLINE | ID: mdl-38192983
ABSTRACT

Background:

Given the poor prognosis of patients with metastatic bladder cancer (MBC), the development of an effective diagnostic and prognostic model is significant in cancer management and for guidance in clinical practice.

Methods:

We acquired data of 23,180 bladder cancer patients from Surveillance Epidemiology and End Results (SEER) database registered from 2010 to 2019. The optimal cut-off value for patient age and tumor size was determined by x-tile software. Independent risk factors for MBC were identified by univariate and multivariate logistic regression analyses and prognosis factors were identified by univariate and multivariate cox regression analyses, and risk and prognostic nomograms were constructed. The accuracy of the nomograms was verified by receiver operating characteristic (ROC) curves, calibration curves, and its clinical utility was determined by decision curve analysis (DCA) curves and clinical impact curves (CIC). Kaplan-Meier (K-M) survival curves further confirmed the clinical validity of the prognostic model.

Results:

Through logistic regression analyses, we derived that age, histological type, tumor size, T stage, and N stage were independent risk factors for metastasis in bladder cancer patients. By cox regression analyses, age, chemotherapy, histological type, bone, lung and liver metastases were identified as risk factors influencing prognosis of MBC patients. Area under the curve (AUC) of the risk nomogram was 0.80, the AUC values of 1/2/3 years were 0.74/0.71/0.71 in the training group and 0.81/0.77/0.77 in the validation group. Based on calibration curves, DCA curves, CIC and K-M curves, the nomograms were validated with excellent predictive performance and clinical utility for MBC.

Conclusions:

The nomograms we constructed have perfect predictive accuracy and clinical practicality for MBC patients, enabling clinicians to provide treatment advice and clinical guidance to patients.
Palabras clave

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Etiology_studies / Guideline / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Transl Cancer Res Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: China

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Etiology_studies / Guideline / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Transl Cancer Res Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: China