Details

Autor(en) / Beteiligte

• Bellizzi, Gennaro G.
• Drizdal, Tomas
• van Rhoon, Gerard C.
• Crocco, Lorenzo
• Isernia, Tommaso
• Paulides, Margarethus M.

Titel

Predictive value of SAR based quality indicators for head and neck hyperthermia treatment quality

Ist Teil von

• International journal of hyperthermia, 2019-01, Vol.36 (1), p.455-464

Ort / Verlag

England: Taylor & Francis

Erscheinungsjahr

2019

Quelle

MEDLINE

Beschreibungen/Notizen

• Purpose: Hyperthermia treatment quality determines treatment effectiveness as shown by the clinically derived thermal-dose effect relations. SAR based optimization factors are used as possible surrogate for temperature, since they are not affected by thermal tissue properties uncertainty and variations. Previously, target coverage (TC) at the 25% and 50% iso-SAR level was shown predictive for treatment outcome in superficial hyperthermia and the target-to-hot-spot-quotient (THQ) was shown to highly correlate with predictive temperature in deep pelvic hyperthermia. Here, we investigate the correlation with temperature for THQ and TC using an 'intermediate' scenario: semi-deep hyperthermia in the head & neck region using the HYPERcollar3D. Methods: Fifteen patient-specific models and two different planning approaches were used, including random perturbations to circumvent optimization bias. The predicted SAR indicators were compared to predicted target temperature distribution indicators T50 and T90, i.e., the median and 90th percentile temperature respectively. Results: The intra-patient analysis identified THQ, TC25 and TC50 as good temperature surrogates: with a mean correlation coefficient R 2 T50  = 0.72 and R 2 T90 =0.66. The inter-patient analysis identified the highest correlation with TC25 (R 2 T50  = 0.76, R 2 T90 =0.54) and TC50 (R 2 T50  = 0.74, R 2 T 90  = 0.56). Conclusion: Our investigation confirmed the validity of our current strategy for deep hyperthermia in the head & neck based on a combination of THQ and TC25. TC50 was identified as the best surrogate since it enables optimization and patient inclusion decision making using one single parameter.