Multiobjective anatomy-based dose optimization for HDR-brachytherapy with constraint free deterministic algorithms

• Published in: Physics in medicine & biology 2002-07, Vol.47 (13), p.2263-2280, Article 306
• Main Authors: Milickovic, N, Lahanas, M, Papagiannopoulou, M, Zamboglou, N, Baltas, D
• Format: Article
• Language: English
• Subjects: Algorithms; Biological and medical sciences; Brachytherapy - methods; Brain Neoplasms - radiotherapy; Breast Neoplasms - radiotherapy; Computer Simulation; Dose-Response Relationship, Radiation; Female; Humans; Male; Medical sciences; Neoplasms - radiotherapy; Prostatic Neoplasms - radiotherapy; Quality Control; Radiation Dosage; Radiotherapy Planning, Computer-Assisted - methods; Reproducibility of Results; Sensitivity and Specificity; Uterine Cervical Neoplasms - radiotherapy
• ISSN: 0031-9155; 1361-6560
• DOI: 10.1088/0031-9155/47/13/306

In high dose rate (HDR) brachytherapy, conventional dose optimization algorithms consider multiple objectives in the form of an aggregate function that transforms the multiobjective problem into a single-objective problem. As a result, there is a loss of information on the available alternative possible solutions. This method assumes that the treatment planner exactly understands the correlation between competing objectives and knows the physical constraints. This knowledge is provided by the Pareto trade-off set obtained by single-objective optimization algorithms with a repeated optimization with different importance vectors. A mapping technique avoids non-feasible solutions with negative dwell weights and allows the use of constraint free gradient-based deterministic algorithms. We compare various such algorithms and methods which could improve their performance. This finally allows us to generate a large number of solutions in a few minutes. We use objectives expressed in terms of dose variances obtained from a few hundred sampling points in the planning target volume (PTV) and in organs at risk (OAR). We compare two- to four-dimensional Pareto fronts obtained with the deterministic algorithms and with a fast-simulated annealing algorithm. For PTV-based objectives, due to the convex objective functions, the obtained solutions are global optimal. If OARs are included, then the solutions found are also global optimal, although local minima may be present as suggested.