K Mah

Poster — Wed Eve—44: CO‐Registered Multi‐Modality Pattern Analysis Segmentation System (COMPASS) for Radiation Targeting of Head and Neck Cancer Using FDG PET/CT

Introduction: Previous attempts to segment tumours for radiation therapy targeting based on FDG‐PET image thresholds have had little success. However, if the texture information available in PET and CTimages is used, more accurate and reliable differentiation of abnormal and normal tissues may be possible. Objective: To develop an automated image segmentation method for head and neck cancer (HNC) using texture analysis of co‐registered FDG‐PET/CT images. Methods: CO‐registered Multi‐modality Pattern Analysis Segmentation System (COMPASS) was developed using a region‐of‐interest‐based Decision Tree K‐Nearest‐Neighbors (DTKNN) classifier. 14 PET and 13 CT texture features such as coarseness, busyness and Left/right symmetrical ratio were calculated for each voxel from corresponding PET and CTimages within a window centered on the voxel. Then the voxel was classified as “tumor” or “non‐tumor” using the DTKNN classifier. PET/CT images of 10 patients with HNC who had their primary tumors and positive nodes manually segmented by three radiation oncologists were used for evaluation. Results: The sensitivity per patient was 83%±19% when “true positive voxels” were defined as those voxels identified by at least two physicians as tumor. The specificity was 95%±2% when “true negative” voxels were all soft tissue voxels not identified by any of three physicians as tumor. Results of COMPASS were significantly better than those of three previously published PET threshold‐based methods. Conclusions: This work suggests that an automated segmentation method based on texture classification of FDG‐PET/CT images has the potential to provide accurate delineation of HNC.

SU-EE-A4-05: Individual Target Volume Definition in NSCLC Using PET

Purpose: To determine whether quantitatively segmented PETimages could be used to identify the volume containing a tumor and its total motion. If possible, PET could provide individualized internal target volumes (ITV) in lungcancer.Method and Materials: A physiological phantom containing background level of Na‐22 was used. Two spheres filled with 0.5 mCi/ml of Na‐22 were used to simulate tumors; each was oscillated within one lung of the phantom with 4 preset motion extents in S/I, A/P, and M/L directions. PET and CTimaging were performed on an integrated PET/CT scanner. A CT‐based GTV was generated using a threshold of −850 HU. A population‐based margin of 15 mm, reflecting both motion and set‐up uncertainties, was added to generate a CT‐based PTV. A PET‐based ITV was defined using a threshold of three standard deviations above normal lung background. A set‐up margin of 7.5 mm was added to PET‐based ITVs to create PTVs. Image‐based PTVs were compared to ideal PTVs. Clinical validation of this methodology was performed on 7 patients with parenchymal lung lesions with the addition of digital fluoroscopy. 18‐FDG was used for patient PET scanning. Results: For the phantom study, PET‐based PTVs were closer to the ideal PTV than those based on CT. While the PET‐based PTVs were approximately half the size of the CT‐based PTVs, in no case would the PET‐based PTVs have resulted in geographical miss. For majority of the patients, PET accurately predicted or slightly over‐predicted the tumor motion extents compared to fluoroscopy; differences were within 2 voxels. Conclusion: Based on the phantom study and initial clinical validation, we have found that quantitatively segmented PETimages can provide an accurate individualized ITV that correlates with a tumor and its motion. Conflict of Interest: Research was supported by NCI Canada with funds from Ontario Cancer Society.