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Cancer Survival Analysis01:21

Cancer Survival Analysis

788
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
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Survival Prediction in Patients With Bladder Cancer Undergoing Radical Cystectomy Using a Machine Learning Algorithm:

Francesco Andrea Causio1,2, Vittorio De Vita1,2, Andrea Nappi1,3

  • 1Italian Society for Artificial Intelligence in Medicine (SIIAM - Società Italiana Intelligenza Artificiale in Medicina), Rome, Italy.

JMIR Perioperative Medicine
|February 19, 2026
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Summary
This summary is machine-generated.

This study developed an AI model to predict bladder cancer survival after cystectomy. The machine learning algorithm accurately forecasts disease-free survival, overall survival, and cause of death, aiding personalized treatment strategies.

Keywords:
artificial intelligenceclinical decision-makingcystectomydisease-free survivalmachine learningneoplasm stagingretrospective studiesstatistical modelsurinary bladder neoplasms

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Area of Science:

  • Oncology
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Traditional statistical models struggle with bladder cancer survival prediction post-cystectomy.
  • Radical cystectomy has a high rate of metastasis (50% within 2 years).
  • Artificial intelligence (AI) integration can enhance prognostic accuracy and treatment personalization.

Purpose of the Study:

  • Develop and evaluate a machine learning algorithm for predicting disease-free survival (DFS), overall survival (OS), and cause of death in bladder cancer patients.
  • Utilize a comprehensive dataset of clinical and pathological variables for prediction.
  • Enhance prognostic accuracy and personalize treatment strategies for bladder cancer patients.

Main Methods:

  • Retrospective analysis of 370 bladder cancer patients undergoing radical cystectomy.
  • Employed the CatBoost algorithm for regression (DFS, OS) and binary classification (tumor-related death).
  • Assessed model performance using Mean Absolute Error (MAE) and F1-score, with 5-fold cross-validation and SHAP values for interpretability.

Main Results:

  • CatBoost model achieved MAE of 18.68 months for DFS and 17.2 months for OS (improved to 14.6 months after feature filtering).
  • For tumor-related death classification, the model achieved 78.6% recall and 0.44 F1-score.
  • Key predictors included clinical/pathological tumor stage, systemic immune-inflammation index (SII), and bladder tumor position.

Conclusions:

  • The AI model shows significant promise in predicting survival and cause of death for bladder cancer patients post-cystectomy.
  • Clinical and pathological tumor staging, SII, and tumor position are crucial predictive factors.
  • AI offers an objective, data-driven tool to enhance personalized prognostic assessment and guide clinical decisions.