Head and neck target delineation using a novel PET automatic segmentation algorithm

Purpose To evaluate the feasibility and impact of using a novel advanced PET auto-segmentation method in Head and Neck (H&N) radiotherapy treatment (RT) planning. Methods ATLAAS, Automatic decision Tree-based Learning Algorithm for Advanced Segmentation, previously developed and validated on pre-clinical data, was applied to 18F-FDG-PET/CT scans of 20 H&N patients undergoing Intensity Modulated Radiation Therapy. Primary Gross Tumour Volumes (GTVs) manually delineated on CT/MRI scans (GTVpCT/MRI), together with ATLAAS-generated contours (GTVpATLAAS) were used to derive the RT planning GTV (GTVpfinal). ATLAAS outlines were compared to CT/MRI and final GTVs qualitatively and quantitatively using a conformity metric. Results The ATLAAS contours were found to be reliable and useful. The volume of GTVpATLAAS was smaller than GTVpCT/MRI in 70% of the cases, with an average conformity index of 0.70. The information provided by ATLAAS was used to grow the GTVpCT/MRI in 10 cases (up to 10.6 mL) and to shrink the GTVpCT/MRI in 7 cases (up to 12.3 mL). ATLAAS provided complementary information to CT/MRI and GTVpATLAAS contributed to up to 33% of the final GTV volume across the patient cohort. Conclusions ATLAAS can deliver operator independent PET segmentation to augment clinical outlining using CT and MRI and could have utility in future clinical studies.