Impact of Number of Segmented Tissues on SAR Prediction Accuracy in Deep Pelvic Hyperthermia Treatment Planning

SIMPLE SUMMARY: Hyperthermia treatment planning is the process of optimizing treatment quality using pre-treatment simulations. Although it has become a powerful tool, prediction accuracy is strongly dependent on the patient model. For deep hyperthermia in the pelvis, it is common that only four tissue categories are discriminated (bone, fat, muscle-like, and tumor). For the head and neck region, more tissues have been shown to be required for good prediction accuracy. Delineating is a labor-intensive and difficult process. Hence, it is important to find the optimum between accuracy and labor, but for deep pelvic hyperthermia, there are no published studies showing the impact of the number of tissues. We studied the trade-off between the segmentation detail needed and segmentation feasibility. Our findings indicate that including high water content tissues can impact simulation accuracy. Although our results, in general, underline the suitability of our current clinical protocol, they help to prioritize improvements for specific cases. ABSTRACT: In hyperthermia, the general opinion is that pre-treatment optimization of treatment settings requires a patient-specific model. For deep pelvic hyperthermia treatment planning (HTP), tissue models comprising four tissue categories are currently discriminated. For head and neck HTP, we found that more tissues are required for increasing accuracy. In this work, we evaluated the impact of the number of segmented tissues on the predicted specific absorption rate (SAR) for the pelvic region. Highly detailed anatomical models of five healthy volunteers were selected from a virtual database. For each model, seven lists with varying levels of segmentation detail were defined and used as an input for a modeling study. SAR changes were quantified using the change in target-to-hotspot-quotient and maximum SAR relative differences, with respect to the most detailed patient model. The main finding of this study was that the inclusion of high water content tissues in the segmentation may result in a clinically relevant impact on the SAR distribution and on the predicted hyperthermia treatment quality when considering our pre-established thresholds. In general, our results underline the current clinical segmentation protocol and help to prioritize any improvements.