title: Accurate 3D temperature dosimetry during hyperthermia therapy by combining invasive measurements and patient-specific simulations.
abstract: OBJECTIVE
  Dosimetry during deep local hyperthermia treatments in the head and neck currently relies on a limited number of invasively placed temperature sensors. The purpose of this study was to assess the feasibility of 3D dosimetry based on patient-specific temperature simulations and sensory feedback.
  
  
  METHODS
  The study includes 10 patients with invasive thermometry applied in at least two treatments. Based on their invasive thermometry, we optimised patient-group thermal conductivity and perfusion values for muscle, fat and tumour using a 'leave-one-out' approach. Next, we compared the accuracy of the predicted temperature (ΔT) and the hyperthermia treatment quality (ΔT50) of the optimisations based on the patient-group properties to those based on patient-specific properties, which were optimised using previous treatment measurements. As a robustness check, and to enable comparisons with previous studies, we optimised the parameters not only for an applicator efficiency factor of 40%, but also for 100% efficiency.
  
  
  RESULTS
  The accuracy of the predicted temperature (ΔT) improved significantly using patient-specific tissue properties, i.e. 1.0 °C (inter-quartile range (IQR) 0.8 °C) compared to 1.3 °C (IQR 0.7 °C) for patient-group averaged tissue properties for 100% applicator efficiency. A similar accuracy was found for optimisations using an applicator efficiency factor of 40%, indicating the robustness of the optimisation method. Moreover, in eight patients with repeated measurements in the target region, ΔT50 significantly improved, i.e. ΔT50 reduced from 0.9 °C (IQR 0.8 °C) to 0.4 °C (IQR 0.5 °C) using an applicator efficiency factor of 40%.
  
  
  CONCLUSIONS
  This study shows that patient-specific temperature simulations combined with tissue property reconstruction from sensory data provides accurate minimally invasive 3D dosimetry during hyperthermia treatments: T50 in sessions without invasive measurements can be predicted with a median accuracy of 0.4 °C.
authors.affiliation: a Hyperthermia Unit, Department of Radiation Oncology, Cancer Institute , Erasmus Medical Centre , Rotterdam , and.
authors.forename: René F
authors.initials: RF
authors.lastname: Verhaart
authors.affiliation: a Hyperthermia Unit, Department of Radiation Oncology, Cancer Institute , Erasmus Medical Centre , Rotterdam , and.
authors.forename: Gerda M
authors.initials: GM
authors.lastname: Verduijn
authors.affiliation: b Departments of Medical Informatics and Radiology , Biomedical Imaging Group Rotterdam, Erasmus Medical Centre , Rotterdam , the Netherlands.
authors.forename: Valerio
authors.initials: V
authors.lastname: Fortunati
authors.affiliation: a Hyperthermia Unit, Department of Radiation Oncology, Cancer Institute , Erasmus Medical Centre , Rotterdam , and.
authors.forename: Zef
authors.initials: Z
authors.lastname: Rijnen
authors.affiliation: b Departments of Medical Informatics and Radiology , Biomedical Imaging Group Rotterdam, Erasmus Medical Centre , Rotterdam , the Netherlands.
authors.forename: Theo
authors.initials: T
authors.lastname: van Walsum
authors.affiliation: b Departments of Medical Informatics and Radiology , Biomedical Imaging Group Rotterdam, Erasmus Medical Centre , Rotterdam , the Netherlands.
authors.forename: Jifke F
authors.initials: JF
authors.lastname: Veenland
authors.affiliation: a Hyperthermia Unit, Department of Radiation Oncology, Cancer Institute , Erasmus Medical Centre , Rotterdam , and.
authors.forename: Margarethus M
authors.initials: MM
authors.lastname: Paulides
mesh_terms: D006258:Head and Neck Neoplasms / Q000628:therapy*; D006801:Humans; D006979:Hyperthermia, Induced; D066230:Patient-Specific Modeling; D013696:Temperature; D062125:Thermometry
keywords: 3D dosimetry; Pennes’ bioheat equation; mild deep local hyperthermia; patient-specific; thermal tissue property optimisation