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Related Experiment Video

Updated: Mar 31, 2026

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Predictive role of Bladder wall dosimetry in conventional prostate Radiotherapy: Integrating AI and statistical

Antonio Piras1, Davide Cusumano2, Tommaso Angileri3

  • 1UO Radioterapia Oncologica, Villa Santa Teresa, 90011 Bagheria, Palermo, Italy; Ri.Med Foundation, 90133 Palermo, Italy; Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, Molecular and Clinical Medicine, University of Palermo 90127 Palermo, Italy; Radiation Oncology, Mater Olbia Hospital, Olbia, Sassari, Italy.

Physica Medica : PM : an International Journal Devoted to the Applications of Physics to Medicine and Biology : Official Journal of the Italian Association of Biomedical Physics (AIFB)
|March 29, 2026
PubMed
Summary

Bladder wall dosimetry improves prediction of urinary symptoms after prostate radiotherapy. Artificial intelligence models significantly enhance risk stratification, enabling personalized treatment strategies for genitourinary toxicity.

Keywords:
Bladder wallDosimetryGenitourinary toxicityMachine learningProstate cancerRadiotherapy

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Area of Science:

  • Radiation oncology
  • Medical physics
  • Urology

Background:

  • Genitourinary toxicity is a frequent complication following prostate radiotherapy.
  • Standard bladder dosimetry may not accurately capture anatomical factors influencing toxicity.
  • This study investigates bladder wall (BW) dosimetry and artificial intelligence (AI) for improved toxicity prediction.

Purpose of the Study:

  • To evaluate the predictive capability of bladder wall (BW) dosimetry for genitourinary toxicity.
  • To assess the added value of artificial intelligence (AI) models in predicting toxicity.
  • To identify potential dose constraints for the bladder wall.

Main Methods:

  • Retrospective analysis of 177 prostate cancer patients treated with conventional radiotherapy.
  • Collected clinical, dosimetric, and toxicity data.
  • Employed logistic regression and machine learning (SVM, gradient boosting, k-NN) to analyze associations between bladder/BW parameters and acute urinary symptoms.

Main Results:

  • BW-specific metrics showed significant associations with Grade ≥1 dysuria (BW V51Gy) and pollakiuria (BW V65Gy).
  • Machine learning, particularly gradient boosting, substantially improved predictive performance (AUC up to 0.97) compared to logistic regression (AUC 0.60-0.63).
  • Identified optimal bladder wall dose constraints: BW V51Gy < 5 cc and BW V65Gy < 3 cc.

Conclusions:

  • Bladder wall dosimetry provides a more precise dose distribution, enhancing the prediction of genitourinary toxicity.
  • AI-based modeling significantly improves risk stratification for urinary symptoms.
  • Findings support the clinical adoption of personalized dose constraints for bladder wall to mitigate toxicity.