Simultaneous optimization of cryoprobe placement and thermal protocol for cryosurgery

Area of Science:

• Medical Physics
• Oncology
• Biomedical Engineering

Background:

• Cryotherapy is a minimally invasive treatment for various conditions, including cancer.
• Effective cryotherapy relies on precise control of probe placement and temperature distribution.
• Current methods for planning cryotherapy can be subjective and labor-intensive.

Purpose of the Study:

• To develop and evaluate a numerical optimization algorithm for simultaneous optimization of multiple cryoprobe placements and thermal protocols.
• To compare different objective functions for their impact on algorithm convergence and treatment outcome.
• To provide a more objective and automated approach to conformal cryotherapy treatment planning.

Main Methods:

• A numerical optimization algorithm was employed to determine optimal cryoprobe positions and temperature profiles.
• Three distinct objective functions were investigated to assess algorithm performance.
• Key performance metrics included convergence rate, minimum objective function value, and analysis of temperature-volume histograms and isotherm distributions.

Main Results:

• The optimization algorithm successfully achieved simultaneous optimization of multiple cryoprobe placements and thermal protocols.
• Results demonstrated that optimization outcomes are influenced by initial variable values, objective function form, optimization goals, and gradient calculation methods.
• The developed model showed potential for handling an unlimited number of variables without user intervention.

Conclusions:

• The proposed numerical optimization model offers a significant advancement over existing semi-empirical approaches for conformal cryotherapy.
• This automated method reduces subjectivity in cryosurgery treatment planning, potentially improving therapeutic outcomes.
• The algorithm's ability to handle complex scenarios with multiple probes and variables enhances its clinical applicability.