Quantitative Analysis of Heterogeneous [18F]FDG Static (SUV) vs. Patlak (Ki) Whole-body PET Imaging Using Different Segmentation Methods: a Simulation Study
- 192 Downloads
Whole-body (WB) dynamic positron emission tomography (PET) enables imaging of highly quantitative physiological uptake parameters beyond the standardized uptake value (SUV). We present a novel dynamic WB anthropomorphic PET simulation framework to assess the potential of 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) net uptake rate constant (Ki) imaging in characterizing tumor heterogeneity.
Validated heterogeneous [18F]FDG tumor kinetics were modeled within the XCAT phantom (ground truth). Thereafter, static (SUV) and dynamic PET data were simulated and reconstructed, followed by indirect WB Patlak Ki imaging. Subsequently, we compared the methods of affinity propagation (AP) and automatic segmentation with active contour (MASAC) to evaluate the impact of tumor delineation. Finally, we extracted the metabolically active tumor volume (MATV), Dice similarity coefficient (DSC), and the intratumoral heterogeneity metrics of the area under the cumulative intensity histogram curve (CIHAUC), homogeneity, entropy, dissimilarity, high-intensity emphasis (HIE), and zone percentage (ZP), along with the target-to-background (TBR) and contrast-to-noise ratios (CNR).
Ki images presented higher TBR but lower CNR compared to SUV. In contrast to MASAC, AP segmentation resulted in smaller bias for MATV and DSC scores in Ki compared to SUV images. All metrics, except for ZP, were significantly different in AP segmentation between SUV and Ki images, with significant correlation observed for MATV, homogeneity, dissimilarity, and entropy. With MASAC segmentation, CIHAUC, homogeneity, and dissimilarity were significantly different between SUV and Ki images, with all metrics, except for HIE and ZP, being significantly correlated. In ground truth images, increased heterogeneity was observed with Ki compared to SUV, with a high correlation for all metrics.
A novel simulation framework was developed for the assessment of the quantitative benefits of WB Patlak PET on realistic heterogeneous tumor models. Quantitative analysis showed that WB Ki imaging may provide enhanced TBR and facilitate lesion segmentation and quantification beyond the SUV capabilities.
Key wordsPET Whole-body Parametric imaging Segmentation Heterogeneity
This work was supported by the Swiss National Science Foundation under grant SNSF 320030_176052, the Swiss Cancer Research Foundation under Grant KFS-3855-02-2016, and an Open Grant (2014GDDSIPL-06) from the Key Laboratory of Digital Signal and Image Processing of Guangdong Province, Shantou University.
Compliance with Ethical Standards
Conflict of Interest
The authors declare that they have no conflict of interest.
- 1.Chung MK, Jeong HS, Park SG, Jang JY, Son YI, Choi JY, Hyun SH, Park K, Ahn MJ, Ahn YC, Kim HJ, Ko YH, Baek CH (2009) Metabolic tumor volume of [18F]-fluorodeoxyglucose positron emission tomography/computed tomography predicts short-term outcome to radiotherapy with or without chemotherapy in pharyngeal cancer. Clin Cancer Res 15:5861–5868CrossRefGoogle Scholar
- 3.Obara P, Pu YL (2013) Prognostic value of metabolic tumor burden in lung cancer. Chin J Cancer Res 25:615–622Google Scholar
- 8.Hatt M, Majdoub M, Vallieres M, Tixier F, le Rest CC, Groheux D, Hindie E, Martineau A, Pradier O, Hustinx R, Perdrisot R, Guillevin R, el Naqa I, Visvikis D (2015) 18F-FDG PET uptake characterization through texture analysis: investigating the complementary nature of heterogeneity and functional tumor volume in a multi-cancer site patient cohort. J Nucl Med 56:38–44CrossRefGoogle Scholar
- 14.Dimitrakopoulou-Strauss A, Georgoulias V, Eisenhut M, Herth F, Koukouraki S, Mäcke HR, Haberkorn U, Strauss LG (2006) Quantitative assessment of SSTR2 expression in patients with non-small cell lung cancer using 68Ga-DOTATOC PET and comparison with 18F-FDG PET. Eur J Nucl Med Mol Imaging 33:823–830CrossRefGoogle Scholar
- 15.Choi Y, Hawkins RA, Huang SC, Brunken RC, Hoh CK, Messa C, Nitzsche EU, Phelps ME, Schelbert HR (1994) Evaluation of the effect of glucose-ingestion and kinetic-model configurations of FDG in the normal liver. J Nucl Med 35:818–823Google Scholar
- 17.Sachpekidis C, Mai EK, Goldschmidt H, Hillengass J, Hose D, Pan L, Haberkorn U, Dimitrakopoulou-Strauss A (2015) 18F-FDG dynamic PET/CT in patients with multiple myeloma patterns of tracer uptake and correlation with bone marrow plasma cell infiltration rate. Clin Nucl Med 40:E300–E307CrossRefGoogle Scholar
- 21.Wanet M, Lee JA, Weynand B, de Bast M, Poncelet A, Lacroix V, Coche E, Grégoire V, Geets X (2011) Gradient-based delineation of the primary GTV on FDG-PET in non-small cell lung cancer: a comparison with threshold-based approaches, CT and surgical specimens. Radiother Oncol 98:117–125CrossRefGoogle Scholar
- 33.Frigge M, Hoaglin DC, Iglewicz B (1989) Some implementations of the boxplot. Am Stat 43:50–54Google Scholar
- 35.Chen HW, Jiang JZ, Gao JL, Liu D, Axelsson J, Cui M, Gong NJ, Feng ST, Luo L, Huang B (2014) Tumor volumes measured from static and dynamic F-18-fluoro-2-deoxy-D-glucose positron emission tomography-computed tomography scan: comparison of different methods using magnetic resonance imaging as the criterion standard. J Comput Assist Tomogr 38:209–215CrossRefGoogle Scholar
- 38.Karakatsanis N, Lodge M, Fahrni G et al (2016) Simultaneous SUV/patlak-4D whole-body PET: a multi-parametric 4D imaging framework for routine clinical application. J Nucl Med 57(Suppl. 2):367Google Scholar
- 39.Karakatsanis N, Lodge M, Zhou Y et al (2015) Novel multi-parametric SUV/Patlak FDG-PET whole-body imaging framework for routine application to clinical oncology. J Nucl Med 56(Suppl. 3):625Google Scholar