Development and external validation of a model to predict recurrence in patients with non-muscle invasive bladder cancer
BackgroundMost patients initially diagnosed with non-muscle invasive bladder cancer (NMIBC) still have frequent recurrence after urethral bladder tumor electrodesiccation supplemented with intravesical instillation therapy, and their risk of recurrence is difficult to predict. Risk prediction models...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2024.1467527/full |
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| author | Jiajia Tang Jiajia Tang Longmei Fan Longmei Fan Tianyu Huang Tianyu Huang Rongrong Yang Rongrong Yang Xinqi Yang Xinqi Yang Yuanjian Liao Mingshun Zuo Neng Zhang Jiangrong Zhang Jiangrong Zhang |
| author_facet | Jiajia Tang Jiajia Tang Longmei Fan Longmei Fan Tianyu Huang Tianyu Huang Rongrong Yang Rongrong Yang Xinqi Yang Xinqi Yang Yuanjian Liao Mingshun Zuo Neng Zhang Jiangrong Zhang Jiangrong Zhang |
| author_sort | Jiajia Tang |
| collection | DOAJ |
| description | BackgroundMost patients initially diagnosed with non-muscle invasive bladder cancer (NMIBC) still have frequent recurrence after urethral bladder tumor electrodesiccation supplemented with intravesical instillation therapy, and their risk of recurrence is difficult to predict. Risk prediction models used to predict postoperative recurrence in patients with NMIBC have limitations, such as a limited number of included cases and a lack of validation. Therefore, there is an urgent need to develop new models to compensate for the shortcomings and potentially provide evidence for predicting postoperative recurrence in NMIBC patients.MethodsClinicopathologic characteristics and follow-up data were retrospectively collected from 556 patients with NMIBC who underwent transurethral resection of bladder tumors by electrocautery (TURBT) from January 2014 to December 2023 at the Affiliated Hospital of Zunyi Medical University and 167 patients with NMIBC who underwent the same procedure from January 2018 to April 2024 at the Third Affiliated Hospital of Zunyi Medical University. Independent risk factors affecting the recurrence of NMIBC were screened using the least absolute shrinkage and selection operator (Lasso) and Cox regression analysis. Cox risk regression models and randomized survival forest (RSF) models were developed. The optimal model was selected by comparing the area under the curve (AUC) of the working characteristics of the subjects in both and presented as a column-line graph.ResultsThe study included data from 566 patients obtained from the affiliated hospital of Zunyi Medical University and 167 patients obtained from the third affiliated hospital of Zunyi Medical University. Tumor number, urine leukocytes, urine occult blood, platelets, and red blood cell distribution width were confirmed as independent risk factors predicting RFS by Lasso-Cox regression analysis. The Cox proportional risk regression model and RSF model were constructed based on Lasso, which showed good predictive efficacy in both training and validation sets, especially the traditional Cox proportional risk regression model. In addition, the discrimination, consistency, and clinical utility of the column-line graph were assessed using C-index, area under the curve (AUC), calibration curve, and decision curve analysis (DCA). Patients at high risk of recurrence can be identified early based on risk stratification.ConclusionInternal and external validation has demonstrated that the model is highly discriminative and stable and can be used to assess the risk of early recurrence in NMIBC patients and to guide clinical decision-making. |
| format | Article |
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| institution | OA Journals |
| issn | 1664-3224 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Immunology |
| spelling | doaj-art-2fa8121237a34fd5b4a8d1efa1a2a0ca2025-08-20T02:36:02ZengFrontiers Media S.A.Frontiers in Immunology1664-32242025-01-011510.3389/fimmu.2024.14675271467527Development and external validation of a model to predict recurrence in patients with non-muscle invasive bladder cancerJiajia Tang0Jiajia Tang1Longmei Fan2Longmei Fan3Tianyu Huang4Tianyu Huang5Rongrong Yang6Rongrong Yang7Xinqi Yang8Xinqi Yang9Yuanjian Liao10Mingshun Zuo11Neng Zhang12Jiangrong Zhang13Jiangrong Zhang14School of Nursing, Zunyi Medical University, Zunyi, ChinaDepartment of Urology, the Affiliated Hospital of ZunYi Medical University, Zunyi, ChinaSchool of Nursing, Zunyi Medical University, Zunyi, ChinaDepartment of Urology, the Affiliated Hospital of ZunYi Medical University, Zunyi, ChinaSchool of Nursing, Zunyi Medical University, Zunyi, ChinaDepartment of Urology, the Affiliated Hospital of ZunYi Medical University, Zunyi, ChinaSchool of Nursing, Zunyi Medical University, Zunyi, ChinaDepartment of Urology, the Affiliated Hospital of ZunYi Medical University, Zunyi, ChinaSchool of Nursing, Zunyi Medical University, Zunyi, ChinaDepartment of Urology, the Affiliated Hospital of ZunYi Medical University, Zunyi, ChinaDepartment of Urology, the Affiliated Hospital of ZunYi Medical University, Zunyi, ChinaDepartment of Urology, the Affiliated Hospital of ZunYi Medical University, Zunyi, ChinaDepartment of Urology, the Affiliated Hospital of ZunYi Medical University, Zunyi, ChinaSchool of Nursing, Zunyi Medical University, Zunyi, ChinaDepartment of Urology, the Affiliated Hospital of ZunYi Medical University, Zunyi, ChinaBackgroundMost patients initially diagnosed with non-muscle invasive bladder cancer (NMIBC) still have frequent recurrence after urethral bladder tumor electrodesiccation supplemented with intravesical instillation therapy, and their risk of recurrence is difficult to predict. Risk prediction models used to predict postoperative recurrence in patients with NMIBC have limitations, such as a limited number of included cases and a lack of validation. Therefore, there is an urgent need to develop new models to compensate for the shortcomings and potentially provide evidence for predicting postoperative recurrence in NMIBC patients.MethodsClinicopathologic characteristics and follow-up data were retrospectively collected from 556 patients with NMIBC who underwent transurethral resection of bladder tumors by electrocautery (TURBT) from January 2014 to December 2023 at the Affiliated Hospital of Zunyi Medical University and 167 patients with NMIBC who underwent the same procedure from January 2018 to April 2024 at the Third Affiliated Hospital of Zunyi Medical University. Independent risk factors affecting the recurrence of NMIBC were screened using the least absolute shrinkage and selection operator (Lasso) and Cox regression analysis. Cox risk regression models and randomized survival forest (RSF) models were developed. The optimal model was selected by comparing the area under the curve (AUC) of the working characteristics of the subjects in both and presented as a column-line graph.ResultsThe study included data from 566 patients obtained from the affiliated hospital of Zunyi Medical University and 167 patients obtained from the third affiliated hospital of Zunyi Medical University. Tumor number, urine leukocytes, urine occult blood, platelets, and red blood cell distribution width were confirmed as independent risk factors predicting RFS by Lasso-Cox regression analysis. The Cox proportional risk regression model and RSF model were constructed based on Lasso, which showed good predictive efficacy in both training and validation sets, especially the traditional Cox proportional risk regression model. In addition, the discrimination, consistency, and clinical utility of the column-line graph were assessed using C-index, area under the curve (AUC), calibration curve, and decision curve analysis (DCA). Patients at high risk of recurrence can be identified early based on risk stratification.ConclusionInternal and external validation has demonstrated that the model is highly discriminative and stable and can be used to assess the risk of early recurrence in NMIBC patients and to guide clinical decision-making.https://www.frontiersin.org/articles/10.3389/fimmu.2024.1467527/fullLasso-Cox regressionnomogramrandom forestnon-muscle invasive bladder cancerrecurrence |
| spellingShingle | Jiajia Tang Jiajia Tang Longmei Fan Longmei Fan Tianyu Huang Tianyu Huang Rongrong Yang Rongrong Yang Xinqi Yang Xinqi Yang Yuanjian Liao Mingshun Zuo Neng Zhang Jiangrong Zhang Jiangrong Zhang Development and external validation of a model to predict recurrence in patients with non-muscle invasive bladder cancer Frontiers in Immunology Lasso-Cox regression nomogram random forest non-muscle invasive bladder cancer recurrence |
| title | Development and external validation of a model to predict recurrence in patients with non-muscle invasive bladder cancer |
| title_full | Development and external validation of a model to predict recurrence in patients with non-muscle invasive bladder cancer |
| title_fullStr | Development and external validation of a model to predict recurrence in patients with non-muscle invasive bladder cancer |
| title_full_unstemmed | Development and external validation of a model to predict recurrence in patients with non-muscle invasive bladder cancer |
| title_short | Development and external validation of a model to predict recurrence in patients with non-muscle invasive bladder cancer |
| title_sort | development and external validation of a model to predict recurrence in patients with non muscle invasive bladder cancer |
| topic | Lasso-Cox regression nomogram random forest non-muscle invasive bladder cancer recurrence |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2024.1467527/full |
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