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  • 1
    Abstract: PURPOSE: Evaluation of the effect of co-registered 4D-(18)FDG-PET/CT for SBRT target delineation in patients with central versus peripheral lung tumors. METHODS: Analysis of internal target volume (ITV) delineation of central and peripheral lung lesions in 21 SBRT-patients. Manual delineation was performed by 4 observers in 2 contouring phases: on respiratory gated 4DCT with diagnostic 3DPET available aside (CT-ITV) and on co-registered 4DPET/CT (PET/CT-ITV). Comparative analysis of volumes and inter-reader agreement. RESULTS: 11 cases of peripheral and 10 central lesions were evaluated. In peripheral lesions, average CT-ITV was 6.2cm(3) and PET/CT-ITV 8.6cm(3), resembling a mean change in hypothetical radius of 2mm. For both CT-ITVs and PET/CT-ITVs inter reader agreement was good and unchanged (0.733 and 0.716; p=0.58). All PET/CT-ITVs stayed within the PTVs derived from CT-ITVs. In central lesions, average CT-ITVs were 42.1cm(3), PET/CT-ITVs 44.2cm(3), without significant overall volume changes. Inter-reader agreement improved significantly (0.665 and 0.750; p〈0.05). 2/10 PET/CT-ITVs exceeded the PTVs derived from CT-ITVs by 〉1ml in average for all observers. CONCLUSION: The addition of co-registered 4DPET data to 4DCT based target volume delineation for SBRT of centrally located lung tumors increases the inter-observer agreement and may help to avoid geographic misses.
    Type of Publication: Journal article published
    PubMed ID: 26116339
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  • 2
    Abstract: PURPOSE: The aim of this study was to evaluate the impact of consensus algorithms on segmentation results when applied to clinical PET images. In particular, whether the use of the majority vote or STAPLE algorithm could improve the accuracy and reproducibility of the segmentation provided by the combination of three semiautomatic segmentation algorithms was investigated. METHODS: Three published segmentation methods (contrast-oriented, possibility theory and adaptive thresholding) and two consensus algorithms (majority vote and STAPLE) were implemented in a single software platform (Artiview(R)). Four clinical datasets including different locations (thorax, breast, abdomen) or pathologies (primary NSCLC tumours, metastasis, lymphoma) were used to evaluate accuracy and reproducibility of the consensus approach in comparison with pathology as the ground truth or CT as a ground truth surrogate. RESULTS: Variability in the performance of the individual segmentation algorithms for lesions of different tumour entities reflected the variability in PET images in terms of resolution, contrast and noise. Independent of location and pathology of the lesion, however, the consensus method resulted in improved accuracy in volume segmentation compared with the worst-performing individual method in the majority of cases and was close to the best-performing method in many cases. In addition, the implementation revealed high reproducibility in the segmentation results with small changes in the respective starting conditions. There were no significant differences in the results with the STAPLE algorithm and the majority vote algorithm. CONCLUSION: This study showed that combining different PET segmentation methods by the use of a consensus algorithm offers robustness against the variable performance of individual segmentation methods and this approach would therefore be useful in radiation oncology. It might also be relevant for other scenarios such as the merging of expert recommendations in clinical routine and trials or the multiobserver generation of contours for standardization of automatic contouring.
    Type of Publication: Journal article published
    PubMed ID: 26567163
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