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Related Experiment Video

Updated: May 20, 2025

Focal Laser Ablation of Prostate Cancer: An Office Procedure
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Multi-objective optimization framework to plan laser ablation procedure for prostate tumors through a genetic

Gabriele Adabbo1, Assunta Andreozzi2, Marcello Iasiello2

  • 1Università degli Studi del Molise, Dipartimento di Medicina e Scienze della Salute "Vincenzo Tiberio", Via Francesco de Sanctis 1 86100 Campobasso, Italy.

Computer Methods and Programs in Biomedicine
|May 9, 2025
PubMed
Summary

This study optimizes laser-induced thermal ablation for prostate cancer by coupling genetic algorithms and finite element simulations. The method identifies optimal settings for maximal tumor destruction while minimizing damage to healthy tissue.

Keywords:
Bayesian artificial neural networksBioheat transferGenetic algorithmLaser ablationMulti-objective optimizationProstate cancer

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Area of Science:

  • Oncology
  • Biomedical Engineering
  • Computational Science

Background:

  • Prostate cancer is common, with many patients receiving overtreatment.
  • Minimally invasive therapies like laser-induced hyperthermia offer alternatives to surgery, radiation, and chemotherapy.
  • Standardization is lacking for laser-induced thermal ablation, risking damage to healthy prostate tissue.

Purpose of the Study:

  • To perform multi-objective optimization for laser-induced thermal ablation in prostate tumors.
  • To identify optimal procedure settings balancing cancer treatment and healthy tissue preservation.
  • To develop a standardized protocol for laser thermal ablation of prostate cancer.

Main Methods:

  • Coupling finite element simulations with a genetic algorithm for multi-objective optimization.
  • Utilizing Pennes' bioheat equation to model thermal damage in tumor and healthy prostate tissue.
  • Developing linear regression and Bayesian artificial neural networks to correlate treatment variables with outcomes.

Main Results:

  • The multi-objective genetic algorithm effectively identified optimal treatment settings.
  • A trade-off solution achieved complete tumor necrosis with minimal (188 mm³) damage to healthy prostate.
  • Artificial neural networks provided better predictive accuracy than linear regression.

Conclusions:

  • An effective methodology for optimizing laser-induced thermal ablation protocols was developed.
  • Coupling genetic algorithms and finite element simulations enables selection of optimal time and laser settings.
  • This approach aids surgeons in planning prostate cancer treatments, reducing outcome uncertainty.