Very Fast Simulated Reannealing in radiation therapy treatment plan optimization

• Published in: International journal of radiation oncology, biology, physics Vol. 31; no. 1; pp. 179 - 188
• Main Authors: Morrill, Steven M., Lam, Kam Shing, Lane, Richard G., Langer, Mark, Rosen, Isaac I.
• Format: Journal Article Conference Proceeding
• Language: English
• Published: New York, NY Elsevier Inc 1995; Elsevier
• Subjects: Algorithms; Biological and medical sciences; Humans; Medical sciences; Miscellaneous; Optimization; Radiotherapy Dosage; Radiotherapy, Computer-Assisted - methods; Radiotherapy. Instrumental treatment. Physiotherapy. Reeducation. Rehabilitation, orthophony, crenotherapy. Diet therapy and various other treatments (general aspects); Simulated annealing; Stochastic Processes; Treatment planning; Treatment planning; Optimization; Simulated annealing; Biomedical data processing; Radiation dose; Treatment efficiency; Complication; Radiotherapy; Algorithm; Computer aid
• ISSN: 0360-3016; 1879-355X
• DOI: 10.1016/0360-3016(94)00350-T

Purpose : Very Fast Simulated Reannealing is a relatively new (1989) abd sophisticated algorithm for simulated annealing applications. It offers the advantages of annealing methods while requiring shorter execution times. The purpose of this investigation was to adapt Very Fast Simulated Reannealing to conformal treatment planning optimization. Methods and Materials : We used Very Fast Simulated Reannealing to optimize treatment for three clinical cases with two different cost functions. The first cost function was linear (minimum target dose) with nonlinear dose-volume weighted product of normal tissue complication probabilities and the tumor control probability Results : For the cost functions used in this study, the Very Fast Simulated Reanneling algorithm achieved results within 5-1% of the final solution (100,000 iterations) after 1000 iterations and within 3–5% of the final solution after 5000–10000 iterations. These solutions were superior to those produced by a conventional treatment plan based on an analysis of the resulting dose-volume histograms. However, this technique is a stochastic method and results vary in a statistical manner. Successive solutions may differ by up to 10%. Conclusion : Very Fast Reannealing with modifications, is suitable for radiation therapy treatment planning optimization. It produced results within 3–10% of the optimal solution, produced using another optimization algorithm (Mixed Integer Programming), in clinically useful execution times.