Survival Analysis of Lymphoepithelioma-Like Carcinoma of the Urinary Bladder and the Effect of Surgical Treatment Modalities on Prognosis

Wu, Jie; Shou, Jianzhong; Wang, Yu-Chen

doi:10.3389/fsurg.2021.706537

Cited by 2 publications

(1 citation statement)

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“…Our model contained the following variables produced from clinical practice: age at diagnosis, marital status at diagnosis, sex, primary site-labeled, grade, ICD-O-3 hist/behav, derived AJCC stage group 7th ed (2010–2015), derived AJCC T 7th ed (2010–2015), RX Summ-surg prim site (1998+), chemotherapy recode (yes, no/unk), CS tumor size (2004–2015), the total number of in situ/malignant tumors for patients, in which age was a significant risk variable for the bladder cancer-specific mortality, suggesting that the risk of death in bladder cancer patients would increase significantly with age. Prognostic analyses for bladder cancer conducted by other studies also showed that age played a crucial role in cancer death, and the death rate in patients increased with the increase of age at diagnosis [ 23 , 24 ]. Another risk factor appeared to be sex.…”

Section: Discussionmentioning

confidence: 99%

Competitive Risk Model for Specific Mortality Prediction in Patients with Bladder Cancer: A Population-Based Cohort Study with Machine Learning

Xue

Wang

et al. 2022

Journal of Oncology

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Background. Noncancer death accounts for a high proportion of all patients with bladder cancer, while these patients are often excluded from the survival analysis, which increases the selection bias of the study subjects in the prediction model. Methods. Clinicopathological information of bladder cancer patients was retrieved from the Surveillance, Epidemiology, and End Results (SEER) database, and the patients were categorized at random into the training and validation cohorts. The random forest method was used to calculate the importance of clinical variables in the training cohort. Multivariate and univariate analyses were undertaken to assess the risk indicators, and the prediction nomogram based on the competitive risk model was constructed. The model’s performance was evaluated utilizing the calibration curve, consistency index (C index), and the area under the receiver operator characteristic curve (AUC). Results. In total, we enrolled 39285 bladder cancer patients in the study (27500 patients were allotted to the training cohort, whereas 11785 were allotted to the validation cohort). A competitive risk model was constructed to predict bladder cancer-specific mortality. The overall C index of patients in the training cohort was 0.876, and the AUC values were 0.891, 0.871, and 0.853, correspondingly, for 1-, 3-, and 5-year cancer-specific mortality. On the other hand, the overall C index of patients in the validation cohort was 0.877, and the AUC values were 0.894, 0.870, and 0.847 for 1-, 3-, and 5-year correspondingly, suggesting a remarkable predictive performance of the model. Conclusions. The competitive risk model proved to be of great accuracy and reliability and could help clinical decision-makers improve their management and approaches for managing bladder cancer patients.

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Section: Discussionmentioning

confidence: 99%