Optimization in hyperthermia treatment planning: The impact of tissue perfusion uncertainty

• Published in: Medical physics (Lancaster) Vol. 37; no. 9; pp. 4540 - 4550
• Main Authors: de Greef, M., Kok, H. P., Correia, D., Bel, A., Crezee, J.
• Format: Journal Article
• Language: English
• Published: United States American Association of Physicists in Medicine 01-09-2010
• Subjects: Antennas; Bioelectrochemistry; Biothermics and thermal processes in biology; cancer; Electric fields; Female; Humans; hyperthermia; Hyperthermia, Induced - methods; Medical treatment planning; Models, Biological; muscle; Muscles; optimisation; optimization; Perfusion; Radiation Dosage; radiation therapy; Radiation treatment; Temperature; Thermal models; Tissues; treatment planning; Treatment strategy; tumours; Uncertainty; Uterine Cervical Neoplasms - therapy; treatment planning; optimization; hyperthermia
• ISSN: 0094-2405; 2473-4209
• DOI: 10.1118/1.3462561

Purpose:
Hyperthermia treatment planning (HTP) potentially provides a valuable tool for monitoring and optimization of treatment. However, one of the major problems in HTP is that different sources of uncertainty degrade its reliability. Perfusion uncertainty is one of the largest uncertainties and hence there is an ongoing debate whether optimization should be limited to power-based strategies. In this study a systematic analysis is carried out addressing this question.
Methods:
The influence of perfusion uncertainty on optimization was analyzed for five patients with cervix uteri carcinoma heated with the AMC-8 70 MHz phased-array waveguide system. The effect of variations (up to ±50%) in both the muscle and tumor perfusion level was investigated. For every patient, reference solutions were calculated using constrained temperature-based optimization for 25 different and known perfusion distributions. Reference solutions were compared to those found by temperature-based optimization using standard perfusion values and four SAR-based optimization methods. The effect of heterogeneity was investigated by creating
5
×
100
perfusion distributions for different levels of local variation (±25% and ±50%) and scale (1 and 2 cm). Here the performance of the temperature-based optimization method was compared to a SAR-based method that showed good performance in the previous analysis.
Results:
Solutions found with temperature-based optimization using a deviating perfusion distribution during optimization were found within
1.0

°
C
from the true optimum. For the SAR-based methods, deviations up to
2.9

°
C
were found. The spread found in these deviations was comparable, typically
0.5
–
1.0

°
C
. When applying intramuscle variation to the perfusion, temperature-based optimization proved to be the best strategy in 95% of the evaluated cases applying ±50% local variation.
Conclusions:
Temperature-based optimization proves to be superior to SAR-based optimization both under variation of perfusion level as well as under the application of intratissue variation. The spread in achieved temperatures is comparable. These results are valid under the assumption of constant perfusion at hyperthermic levels. Although similar results are expected from models including thermoregulation, additional analysis is required to confirm this. In view of uncertainty in tissue perfusion and other modeling uncertainties, the authors propose feedback guided temperature-based optimization as the best candidate to improve thermal dose delivery during hyperthermia treatment.