Feasibility of a semi-automated contrast-oriented algorithm for tumor segmentation in retrospectively gated PET images: phantom and clinical validation.

ABSTRACT

PET/CT plays an important role in radiotherapy planning for lung tumors. Several segmentation algorithms have been proposed for PET tumor segmentation. However, most of them do not take into account respiratory motion and are not well validated. The aim of this work was to evaluate a semi-automated contrast-oriented algorithm (COA) for PET tumor segmentation adapted to retrospectively gated (4D) images. The evaluation involved a wide set of 4D-PET/CT acquisitions of dynamic experimental phantoms and lung cancer patients. In addition, segmentation accuracy of 4D-COA was compared with four other state-of-the-art algorithms. In phantom evaluation, the physical properties of the objects defined the gold standard. In clinical evaluation, the ground truth was estimated by the STAPLE (Simultaneous Truth and Performance Level Estimation) consensus of three manual PET contours by experts. Algorithm evaluation with phantoms resulted in (i) no statistically significant diameter differences for different targets and movements (Δφ = 0.3 ± 1.6 mm); (ii) reproducibility for heterogeneous and irregular targets independent of user initial interaction and (iii) good segmentation agreement for irregular targets compared to manual CT delineation in terms of Dice Similarity Coefficient (DSC = 0.66 ± 0.04), Positive Predictive Value (PPV = 0.81 ± 0.06) and Sensitivity (Sen. = 0.49 ± 0.05). In clinical evaluation, the segmented volume was in reasonable agreement with the consensus volume (difference in volume (%Vol) = 40 ± 30, DSC = 0.71 ± 0.07 and PPV = 0.90 ± 0.13). High accuracy in target tracking position (ΔME) was obtained for experimental and clinical data (ΔME(exp) = 0 ± 3 mm; ΔME(clin) 0.3 ± 1.4 mm). In the comparison with other lung segmentation methods, 4D-COA has shown the highest volume accuracy in both experimental and clinical data. In conclusion, the accuracy in volume delineation, position tracking and its robustness on highly irregular target movements, make this algorithm a useful tool for 4D-PET based volume definition for radiotherapy planning of lung cancer and may help to improve the reproducibility in PET quantification for therapy response assessment and prognosis.