SU-FF-J-128: An Automatic Method to Delineate 18F-FDG PET Tumor Volumes for Radiation Treatment Planning

Abstract

Purpose: To develop and evaluate an automatic method of segmenting biological tumor volumes in 18F‐FDG‐PET images for radiation treatment planning. Method and Materials: We developed a method of correcting errors in biologic tumor volume due to the finite spatial resolution in 18F‐FDG PET images. The PET image is acquired, then used to generate a “spillover image”S, defined as the difference between an iteratively deconvolved image and the original PET image. The biologic tumor boundary was defined along the contour where the spillover imageS changes sign. This algorithm was validated with tumors simulated with various sizes (11–28 mm) and background levels, and also with PET/CT images from 10 patients having FDG‐avid head/neck tumors having anatomical boundaries clearly defined with CT. PET‐derived biologic tumor volumes were compared against the CT‐derived anatomic tumor volumes and against the volumes extracted by applying a simple threshold to the PET image.Results: The biologic tumor volumes derived with the proposed method matched the CT‐derived anatomic tumor volumes for all tumor sizes having background activities from 0 to 33% of the tumor activity in the simulated data, and agreed to within 90% for all tumors evaluated in the patient data. For all tumor sizes, the PET volumes derived from this algorithm matched more closely with the CT‐derived volumes than those obtained with a fixed threshold applied to the PET images.Conclusion: The proposed algorithm incorporates the spatial resolution of the imaging system to automatically segment biologic tumor volumes. The algorithm automatically compensates for differences in tumor size and tumor:background contrast, and does not require a fixed threshold to be applied to the PET image. Such a scheme will be useful for defining biologic tumor volumes with 18F‐FDG PET for radiation treatment planning. Research sponsored by Siemens Oncology Care.