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Deep learning-based survival analysis of bladder cancer patients in the Putuo District, Shanghai, China.

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Název: Deep learning-based survival analysis of bladder cancer patients in the Putuo District, Shanghai, China.
Autoři: Ruojing, Wang, Yuan, Shen, Feiya, Shi, Lijuan, Yang, Yicen, Qin
Zdroj: Frontiers in Oncology; 2025, p1-11, 11p
Témata: BLADDER cancer, DEEP learning, SURVIVAL analysis (Biometry), PUBLIC health administration, MACHINE learning, LOGISTIC regression analysis
Geografický termín: CHINA, SHANGHAI (China)
Abstrakt: Background: Bladder cancer poses significant health risks and necessitates effective public health management. Objective: To develop a deep-learning survival prediction model using TabNet and compare its performance with logistic regression. Methods: Data on bladder cancer patients were collected from the Putuo District subset of Shanghai Cancer Registration and Reporting System. A total of 620 patients were included, divided into a training cohort (n=434) and a validation cohort (n=186). Logistic regression analyses were conducted to identify risk factors, while the TabNet framework was used to develop a deep learning-based model. Model performance was evaluated using ROC curves, decision curve analysis, and calibration curves. Shapley Additive Explanations (SHAP) was applied to interpret feature importance. Results: Baseline characteristics showed no significant differences between the training and validation cohorts (P>0.05). The TabNet model demonstrated high discriminative ability in predicting both 5-year OS and CSS within the training cohort, with net benefits surpassing those of logistic regression, and showed good calibration. In the validation cohort, the TabNet model exhibited excellent performance in predicting 5-year OS and CSS. SHAP analysis revealed that age, T stage, and N stage were the most influential factors. Conclusion: The TabNet model showed robust performance in predicting bladder cancer survival, offering valuable insights for community-based management and follow-up strategies. [ABSTRACT FROM AUTHOR]
Copyright of Frontiers in Oncology is the property of Frontiers Media S.A. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: Deep learning-based survival analysis of bladder cancer patients in the Putuo District, Shanghai, China.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Ruojing%2C+Wang%22">Ruojing, Wang</searchLink><br /><searchLink fieldCode="AR" term="%22Yuan%2C+Shen%22">Yuan, Shen</searchLink><br /><searchLink fieldCode="AR" term="%22Feiya%2C+Shi%22">Feiya, Shi</searchLink><br /><searchLink fieldCode="AR" term="%22Lijuan%2C+Yang%22">Lijuan, Yang</searchLink><br /><searchLink fieldCode="AR" term="%22Yicen%2C+Qin%22">Yicen, Qin</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: Frontiers in Oncology; 2025, p1-11, 11p
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22BLADDER+cancer%22">BLADDER cancer</searchLink><br /><searchLink fieldCode="DE" term="%22DEEP+learning%22">DEEP learning</searchLink><br /><searchLink fieldCode="DE" term="%22SURVIVAL+analysis+%28Biometry%29%22">SURVIVAL analysis (Biometry)</searchLink><br /><searchLink fieldCode="DE" term="%22PUBLIC+health+administration%22">PUBLIC health administration</searchLink><br /><searchLink fieldCode="DE" term="%22MACHINE+learning%22">MACHINE learning</searchLink><br /><searchLink fieldCode="DE" term="%22LOGISTIC+regression+analysis%22">LOGISTIC regression analysis</searchLink>
– Name: SubjectGeographic
  Label: Geographic Terms
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  Data: <searchLink fieldCode="DE" term="%22CHINA%22">CHINA</searchLink><br /><searchLink fieldCode="DE" term="%22SHANGHAI+%28China%29%22">SHANGHAI (China)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Background: Bladder cancer poses significant health risks and necessitates effective public health management. Objective: To develop a deep-learning survival prediction model using TabNet and compare its performance with logistic regression. Methods: Data on bladder cancer patients were collected from the Putuo District subset of Shanghai Cancer Registration and Reporting System. A total of 620 patients were included, divided into a training cohort (n=434) and a validation cohort (n=186). Logistic regression analyses were conducted to identify risk factors, while the TabNet framework was used to develop a deep learning-based model. Model performance was evaluated using ROC curves, decision curve analysis, and calibration curves. Shapley Additive Explanations (SHAP) was applied to interpret feature importance. Results: Baseline characteristics showed no significant differences between the training and validation cohorts (P>0.05). The TabNet model demonstrated high discriminative ability in predicting both 5-year OS and CSS within the training cohort, with net benefits surpassing those of logistic regression, and showed good calibration. In the validation cohort, the TabNet model exhibited excellent performance in predicting 5-year OS and CSS. SHAP analysis revealed that age, T stage, and N stage were the most influential factors. Conclusion: The TabNet model showed robust performance in predicting bladder cancer survival, offering valuable insights for community-based management and follow-up strategies. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Frontiers in Oncology is the property of Frontiers Media S.A. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3389/fonc.2025.1619309
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        Text: English
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      – SubjectFull: CHINA
        Type: general
      – SubjectFull: SHANGHAI (China)
        Type: general
      – SubjectFull: BLADDER cancer
        Type: general
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      – SubjectFull: LOGISTIC regression analysis
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      – TitleFull: Deep learning-based survival analysis of bladder cancer patients in the Putuo District, Shanghai, China.
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            – D: 09
              M: 12
              Text: 2025
              Type: published
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