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A Bioluminescent and Fluorescent Orthotopic Syngeneic Murine Model of Androgen-dependent and Castration-resistant Prostate Cancer

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Machine Learning and Urinary Incontinence in Prostate Cancer: A Generalized Additive Model of Physical Activity and

Ioannis Manolitsis¹, Georgios Feretzakis², Lazaros Tzelves¹

¹Second Department of Urology, Sismanoglio General Hospital, National and Kapodistrian University of Athens, 15126 Athens, Greece.

Studies in Health Technology and Informatics

|May 17, 2025

Summary

This summary is machine-generated.

Artificial intelligence in prostate cancer care can predict urinary incontinence (UI) severity. Age and comorbidities are key factors, emphasizing personalized treatment plans for improved patient quality of life.

Keywords:

Artificial Intelligence Cardiovascular Mortality Physical Activity Prostate Cancer Quality of Life Urinary Incontinence

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Area of Science:

Oncology
Medical Informatics
Artificial Intelligence

Background:

Prostate cancer treatment, particularly radical prostatectomy, frequently leads to urinary incontinence (UI).
Managing post-prostatectomy UI is crucial for improving patients' health-related quality of life.
The ASCAPE project leverages artificial intelligence (AI) to develop solutions for cancer patient care.

Purpose of the Study:

To investigate the relationship between various patient data variables and the occurrence/severity of urinary incontinence (UI) after radical prostatectomy.
To identify key predictors of UI using data from the ASCAPE project.
To inform the development of AI-driven, personalized care strategies for prostate cancer patients experiencing UI.

Main Methods:

Utilized Generalized Additive Models (GAM) for statistical analysis.
Analyzed patient-reported outcomes on UI using QLQ-PR25 questionnaires over a 12-month period.
Integrated objective data from wearable devices with patient-reported data.

Main Results:

Identified patient age and comorbidities as significant predictors of UI severity.
Found no significant association between physical activity levels and UI severity in the studied cohort.
Demonstrated the utility of combining patient-reported and objective wearable data for UI analysis.

Conclusions:

Age and comorbidities are primary determinants of post-prostatectomy UI severity.
A personalized approach to incontinence management is essential, considering individual patient characteristics and recovery trajectories.
AI-driven insights can enhance the personalization of care plans for prostate cancer survivors with UI.