ABSTRACT
PURPOSE: To investigate the performance of various algorithms for deformable image registration (DIR) to propagate regions of interest (ROIs) using multiple commercial platforms. METHODS AND MATERIALS: Thirteen institutions participated in the study with six commercial platforms: RayStation (RaySearch Laboratories, Stockholm, Sweden), MIM (Cleveland, OH, USA), VelocityAI and Smart Adapt (Varian Medical Systems, Palo Alto, CA, USA), Mirada XD (Mirada Medical Ltd, Oxford, UK), and ABAS (Elekta AB, Stockholm, Sweden). The DIR algorithms were tested on synthetic images generated with the ImSimQA package (Oncology Systems Limited, Shrewsbury, UK) by applying two specific Deformation Vector Fields (DVF) to real patient data-sets. Head-and-neck (HN), thorax, and pelvis sites were included. The accuracy of the algorithms was assessed by comparing the DIR-mapped ROIs from each center with those of reference, using the Dice Similarity Coefficient (DSC) and Mean Distance to Conformity (MDC) metrics. Statistical inference on validation results was carried out in order to identify the prognostic factors of DIR performances. RESULTS: DVF intensity, anatomic site and participating center were significant prognostic factors of DIR performances. Sub-voxel accuracy was obtained in the HN by all algorithms. Large errors, with MDC ranging up to 6 mm, were observed in low-contrast regions that underwent significant deformation, such as in the pelvis, or large DVF with strong contrast, such as the clinical tumor volume (CTV) in the lung. Under these conditions, the hybrid DIR algorithms performed significantly better than the free-form intensity based algorithms and resulted robust against intercenter variability. CONCLUSIONS: The performances of the systems proved to be site specific, depending on the DVF type and the platforms and the procedures used at the various centers. The pelvis was the most challenging site for most of the algorithms, which failed to achieve sub-voxel accuracy. Improved reproducibility was observed among the centers using the same hybrid registration algorithm.
Subject(s)
Image Processing, Computer-Assisted/instrumentation , Phantoms, Imaging , Algorithms , Humans , Tomography, X-Ray ComputedABSTRACT
INTRODUCTION: (18)Fluorine-fluorodeoxyglucose positron emission tomography/computed tomography is not yet accepted as a standard pretreatment evaluation of thymic epithelial neoplasm (TEN). Statistical correlation between standardized uptake value of tumor/mediastinum ratio and patients' WHO risk class has been reported. PET metabolic tumor volume (MTV) and total glycolytic volume (TGV) have been reported as additional prognostic imaging biomarkers in several human tumors. Purpose of study was to establish whether MTV and TGV add prognostic information in TEN. MATERIALS AND METHODS: A retrospective dynamic cohort study of prospectively collected data (2006-2012) on 23 consecutive patients with pathologically proven TEN (no thymic carcinoma) was conducted. All patients underwent chest CT, and PET for staging. SUV T/M ratio, semi-quantitative and volumetric analyses of TEN were calculated. Patients were categorized according to WHO classification (low-risk and high-risk thymomas). Statistical analysis was performed with bootstrap method. Multi-collinearity was established using Pearson correlation coefficient. Cut-off point for TGV was compared using Mantel Cox log rank test. RESULTS: SUV T/M ratio, MTV, and TGV correlate with low- and high-risk TEN. However, the statistical correlation between TGV and WHO classification (ρ = 0.897) was higher than SUV T/M ratio (ρ = 0.873). Since sample distributions were not uniformly smooth, only one cut-off value was identified: a TGV of 383 served as a cut-off value between low-risk and high-risk TEN. CONCLUSION: TGV is a PET reproducible imaging marker in patients with TEN, provides prognostic information, and could be useful in pretreatment stratification of patients. Nevertheless, it needs validation in larger cohort studies.
