2827 Clincal IMRT Planning Testing for the Multi-Criteria Optimization (MCO) Methodology

C. Yang, L. Peng, S. Sim, M. Weiss

Monmouth Medical Center, Long Branch, NJ

Purpose/Objective(s): To apply a biological model based algorithm for acquiring optimized IMRT planning solutions. Thisinteractive planning tool will help users to select the best available plans in the IMRT solution space.

Materials/Methods: IMRT often is a time consuming iterative optimization process between evaluation of the dose distributionand redefinition of the object function. An IMRT planning optimization tool (Multi-Criteria Optimization, MCO™) has beenintroduced for non-clinical evaluation to acquire the best available solutions. Based on a Pareto’s solution concept, this toolcould search the solution space and offer users a limited set of deliverable IMRT plans. With this interactive process, users canset the target and critical structures dose constraints with the biological model (EUD) to obtain the best solution. We usedPinnacle system as the benchmark to compare the dosimetric gain from the MCO algorithm, DVH indicated excellent sparingwith better PTV coverage is achievable from the MCO process in KonRad system.

Results: Dosimetric findings are summarized as 1) MCO optimization testing shows that much better dose distribution can beachieved compared to the current planning results. Due to the confined solution space, the optimal results are easily achievable.2) MCO with Pareto’s approach is durable in the solution searching process. It is interactive with the graphical interface whichthe dose distribution along with the DVH can be compared simultaneously (Fig. 1). 3) IMRT dose optimization and summarybased on the MCO methodology are very conceivable. With pre-calculated IMRT solutions, final results help users to select thebest available plan from the solution domain in real time.

Conclusions: From this interactive MCO planning tool, we can calculate the best IMRT results in a very reasonable time frame.Human factors for determining an acceptable plan can be dramatically reduced.

Figure 1. An interactive tool for isodose and DVH evaluations

Author Disclosure: C. Yang, None; L. Peng, None; S. Sim, None; M. Weiss, None.

2828 Quantitative Delineation of PET Standard Uptake Values for Radiotherapy Treatment Planning:Validation and Application To Head and Neck Cancer

J. E. Bayouth, F. Qing, M. M. Graham, M. Yao

University of Iowa Hospitals & Clinics, Iowa City, IA

Purpose/Objective(s): Develop and validate a methodology to quantitatively delineate regions of abnormal FDG uptake forhead and neck (H&N) tumors based on standard uptake values (SUV) computed from raw pixel values within the treatmentplanning system, and apply this technique to evaluate regions of abnormal FDG uptake in relationship to anatomically definedclinical target volumes (CTV).

Materials/Methods: PET/CT images from 18 patients acquired on a Siemens Biograph with biopsy proven H&N cancer (10tonsil, 5 base of tongue, 1 pyriform sinus, 1 oral cavity, and 1 supraglottis) were analyzed. Maximum SUV was identified in5 separate anatomical sites (typically the gross tumor, abnormal lymph nodes, stomach, left and right kidney) for each patienton the Nuclear Medicine (NM) workstation (MS Viewer). Corresponding regions of interest were drawn on PET/CT imagestransferred into the Radiotherapy treatment (RT) planning system (Philips Pinnacle) and the maximum pixel values on PETimages were recorded. Information on patient weight, injected activity, and time from injection to initial scanning wasdetermined from the DICOM header of the PET images and used to compute the image set unique relationship between pixelvalues and SUV for each patient. SUV values between the NM workstation and the RT planning system were compared. Onceverified, the SUV/pixel value relationship was utilized to perform objective, quantitative tumor delineation on the RT planningsystem to contour voxels of FDG uptake with an SUV � 2.5. The volumes of SUV� 2.5 outside of the anatomically definedprimary tumor and grossly involved nodes (CTV1), high risk nodes and tissue (CTV2), and lower risk nodes and tissues (CTV3)regions were computed. The mean SUV outside the CTV(s), and the minimum expansion of the CTV(s) required to cover theSUV� 2.5 volume were also determined.

S675Proceedings of the 48th Annual ASTRO Meeting