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

Abstract

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 (GTVp_CT/MRI), together with ATLAAS-generated contours (GTVp_ATLAAS) were used to derive the RT planning GTV (GTVp_final). 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 GTVp_ATLAAS was smaller than GTVp_CT/MRI in 70% of the cases, with an average conformity index of 0.70. The information provided by ATLAAS was used to grow the GTVp_CT/MRI in 10 cases (up to 10.6mL) and to shrink the GTVp_CT/MRI in 7 cases (up to 12.3mL). ATLAAS provided complementary information to CT/MRI and GTVp_ATLAAS 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.

Keywords: Automatic PET segmentation; Image Segmentation; Intensity Modulated Radiation Therapy; Positron Emission Tomography.