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Effects of Reusing Baseline Volumes of Interest by Applying (Non-)Rigid Image Registration on Positron Emission Tomography Response Assessments

OBJECTIVES: Reusing baseline volumes of interest (VOI) by applying non-rigid and to some extent (local) rigid image registration showed good test-retest variability similar to delineating VOI on both scans individually. The aim of the present study was to compare response assessments and classificat...

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Detalles Bibliográficos
Autores principales: van Velden, Floris H. P., Nissen, Ida A., Hayes, Wendy, Velasquez, Linda M., Hoekstra, Otto S., Boellaard, Ronald
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3904976/
https://www.ncbi.nlm.nih.gov/pubmed/24489860
http://dx.doi.org/10.1371/journal.pone.0087167
Descripción
Sumario:OBJECTIVES: Reusing baseline volumes of interest (VOI) by applying non-rigid and to some extent (local) rigid image registration showed good test-retest variability similar to delineating VOI on both scans individually. The aim of the present study was to compare response assessments and classifications based on various types of image registration with those based on (semi)-automatic tumour delineation. METHODS: Baseline (n = 13), early (n = 12) and late (n = 9) response (after one and three cycles of treatment, respectively) whole body [(18)F]fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography (PET/CT) scans were acquired in subjects with advanced gastrointestinal malignancies. Lesions were identified for early and late response scans. VOI were drawn independently on all scans using an adaptive 50% threshold method (A50). In addition, various types of (non-)rigid image registration were applied to PET and/or CT images, after which baseline VOI were projected onto response scans. Response was classified using PET Response Criteria in Solid Tumors for maximum standardized uptake value (SUV(max)), average SUV (SUV(mean)), peak SUV (SUV(peak)), metabolically active tumour volume (MATV), total lesion glycolysis (TLG) and the area under a cumulative SUV-volume histogram curve (AUC). RESULTS: Non-rigid PET-based registration and non-rigid CT-based registration followed by non-rigid PET-based registration (CTPET) did not show differences in response classifications compared to A50 for SUV(max) and SUV(peak,), however, differences were observed for MATV, SUV(mean), TLG and AUC. For the latter, these registrations demonstrated a poorer performance for small lung lesions (<2.8 ml), whereas A50 showed a poorer performance when another area with high uptake was close to the target lesion. All methods were affected by lesions with very heterogeneous tracer uptake. CONCLUSIONS: Non-rigid PET- and CTPET-based image registrations may be used to classify response based on SUV(max) and SUV(peak). For other quantitative measures future studies should assess which method is valid for response evaluations by correlating with survival data.