Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

At a glance: The STEP trials

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.
ADVANCED SEARCH
Log in
Top
Molecular Imaging and Biology
Published in: Molecular Imaging and Biology 2/2019

01-04-2019 | Research Article

Quantitative Analysis of Heterogeneous [18F]FDG Static (SUV) vs. Patlak (Ki) Whole-body PET Imaging Using Different Segmentation Methods: a Simulation Study

Authors: Mingzan Zhuang, Nicolas A. Karakatsanis, Rudi A. J. O. Dierckx, Habib Zaidi

Published in: Molecular Imaging and Biology | Issue 2/2019

Login to get access

Abstract

Purpose

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.

Procedures

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).

Results

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.

Conclusions

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.
Appendix
Available only for authorised users
Literature
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 15:5861–5868CrossRefPubMed
2.
La TH, Filion EJ, Turnbull BB et al (2009) Metabolic tumor volume predicts for recurrence and death in head-and-neck cancer. Int J Radiat Oncol Biol Phys 74:1335–1341CrossRefPubMedPubMedCentral
3.
4.
Kristian A, Revheim ME, Qu H, Mælandsmo GM, Engebråten O, Seierstad T, Malinen E (2013) Dynamic 18F-FDG-PET for monitoring treatment effect following anti-angiogenic therapy in triple-negative breast cancer xenografts. Acta Oncol 52:1566–1572CrossRefPubMed
5.
Dimitrakopoulou-Strauss A, Pan L, Strauss LG (2012) Quantitative approaches of dynamic FDG-PET and PET/CT studies (dPET/CT) for the evaluation of oncological patients. Cancer Imaging 12:283–289CrossRefPubMedPubMedCentral
6.
Zaidi H, Karakatsanis N (2018) Towards enhanced PET quantification in clinical oncology. Br J Radiol 91:20170508CrossRefPubMed
7.
Karakatsanis NA, Casey ME, Lodge MA, Rahmim A, Zaidi H (2016) Whole-body direct 4D parametric PET imaging employing nested generalized Patlak expectation-maximization reconstruction. Phys Med Biol 61:5456–5485CrossRefPubMedPubMedCentral
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 56:38–44CrossRefPubMed
9.
Tixier F, Vriens D, Cheze-Le Rest C et al (2016) Comparison of tumor uptake heterogeneity characterization between static and parametric 18F-FDG PET images in non-small cell lung cancer. J Nucl Med 57:1033–1039CrossRefPubMed
10.
Visser EP, Philippens ME, Kienhorst L et al (2008) Comparison of tumor volumes derived from glucose metabolic rate maps and SUV maps in dynamic 18F-FDG PET. J Nucl Med 49:892–898CrossRefPubMed
11.
12.
13.
Vriens D, Disselhorst JA, Oyen WJG, de Geus-Oei LF, Visser EP (2012) Quantitative assessment of heterogeneity in tumor metabolism using FDG-PET. Int J Radiat Oncol Biol Phys 82:E725–E731CrossRefPubMed
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 33:823–830CrossRefPubMed
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 35:818–823PubMed
16.
Lin KP, Huang SC, Choi Y, Brunken RC, Schelbert HR, Phelps ME (1995) Correction of spillover radioactivities for estimation of the blood time-activity curve from the imaged lv chamber in cardiac dynamic FDG PET studies. Phys Med Biol 40:629–642CrossRefPubMed
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–E307CrossRefPubMed
18.
Karakatsanis NA, Lodge MA, Tahari AK, Zhou Y, Wahl RL, Rahmim A (2013) Dynamic whole-body PET parametric imaging: I. Concept, acquisition protocol optimization and clinical application. Phys Med Biol 58:7391–7418CrossRefPubMedPubMedCentral
19.
Feng D, Huang SC, Wang X (1993) Models for computer simulation studies of input functions for tracer kinetic modeling with positron emission tomography. Int J Biomed Comput 32:95–110CrossRefPubMed
20.
Le Maitre A, Segars WP, Marache S et al (2009) Incorporating patient-specific variability in the simulation of realistic whole-body F-18-FDG distributions for oncology applications. Proc IEEE 97:2026–2038CrossRef
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 98:117–125CrossRefPubMed
22.
Daisne JF, Duprez T, Weynand B, Lonneux M, Hamoir M, Reychler H, Grégoire V (2004) Tumor volume in pharyngolaryngeal squamous cell carcinoma: comparison at CT, MR imaging, and FDG PET and validation with surgical specimen. Radiology 233:93–100CrossRefPubMed
23.
Loening AM, Gambhir SS (2003) AMIDE: a free software tool for multimodality medical image analysis. Mol Imaging 2:131–137CrossRefPubMed
24.
Thielemans K, Tsoumpas C, Mustafovic S, Beisel T, Aguiar P, Dikaios N, Jacobson MW (2012) STIR: software for tomographic image reconstruction release 2. Phys Med Biol 57:867–883CrossRefPubMed
25.
Patlak CS, Blasberg RG, Fenstermacher JD (1983) Graphical evaluation of blood-to-brain transfer constants from multiple-time uptake data. J Cereb Blood Flow Metab 3:1–7CrossRef
26.
Zaidi H, El Naqa I (2010) PET-guided delineation of radiation therapy treatment volumes: a survey of image segmentation techniques. Eur J Nucl Med Mol Imaging 37:2165–2187CrossRefPubMed
27.
Hatt M, Lee J, Schmidtlein CR et al (2017) Classification and evaluation strategies of auto-segmentation approaches for PET: report of AAPM Task Group No. 211. Med Phys 44:e1–e42CrossRefPubMedPubMedCentral
28.
Zhuang M, Dierckx RA, Zaidi H (2016) Generic and robust method for automatic segmentation of PET images using an active contour model. Med Phys 43:4483–4494CrossRefPubMed
29.
Foster B, Bagci U, Ziyue X et al (2014) Segmentation of PET images for computer-aided functional quantification of tuberculosis in small animal models. IEEE Trans Biomed Eng 61:711–724CrossRefPubMed
30.
Zou KH, Warfield SK, Bharatha A, Tempany CMC, Kaus MR, Haker SJ, Wells WM III, Jolesz FA, Kikinis R (2004) Statistical validation of image segmentation quality based on a spatial overlap index—scientific reports. Acad Radiol 11:178–189CrossRefPubMedPubMedCentral
31.
Hatt M, le Rest CC, Descourt P et al (2010) Accurate automatic delineation of heterogeneous functional volumes in positron emission tomography for oncology applications. Int J Radiat Oncol Biol Phys 77:301–308CrossRefPubMed
32.
33.
Frigge M, Hoaglin DC, Iglewicz B (1989) Some implementations of the boxplot. Am Stat 43:50–54
34.
Karakatsanis NA, Lodge MA, Zhou Y, Wahl RL, Rahmim A (2013) Dynamic whole-body PET parametric imaging: II. Task-oriented statistical estimation. Phys Med Biol 58:7419–7445CrossRefPubMedPubMedCentral
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 38:209–215CrossRefPubMed
36.
Ilan E, Sandstrom M, Velikyan I, Sundin A, Eriksson B, Lubberink M (2017) Parametric net influx rate images of 68Ga-DOTATOC and 68Ga-DOTATATE: quantitative accuracy and improved image contrast. J Nucl Med 58:744–749CrossRefPubMed
37.
Wangerin KA, Muzi M, Peterson LM, Linden HM, Novakova A, Mankoff DA, Kinahan PE (2017) A virtual clinical trial comparing static versus dynamic PET imaging in measuring response to breast cancer therapy. Phys Med Biol 62:3639–3655CrossRefPubMedPubMedCentral
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):367
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):625
Metadata
Title
Quantitative Analysis of Heterogeneous [18F]FDG Static (SUV) vs. Patlak (Ki) Whole-body PET Imaging Using Different Segmentation Methods: a Simulation Study
Authors
Mingzan Zhuang
Nicolas A. Karakatsanis
Rudi A. J. O. Dierckx
Habib Zaidi
Publication date
01-04-2019
Publisher
Springer International Publishing
Published in
Molecular Imaging and Biology / Issue 2/2019
Print ISSN: 1536-1632
Electronic ISSN: 1860-2002
DOI
https://doi.org/10.1007/s11307-018-1241-8

Other articles of this Issue 2/2019

Molecular Imaging and Biology 2/2019 Go to the issue

Research Article

Quantitative Intensity Harmonization of Dopamine Transporter SPECT Images Using Gamma Mixture Models

Research Article

In Vivo Mapping and Quantification of Creatine Using Chemical Exchange Saturation Transfer Imaging in Rat Models of Epileptic Seizure

Research Article

Linked Hexokinase and Glucose-6-Phosphatase Activities Reflect Grade of Ovarian Malignancy

Special Topic

Summary of Imaging in 2020: Visualizing the Future of Healthcare with MR Imaging

OriginalPaper

[68Ga]RGD Versus [18F]FDG PET Imaging in Monitoring Treatment Response of a Mouse Model of Human Glioblastoma Tumor with Bevacizumab and/or Temozolomide

Research Article

Differentiation of Malignant and Benign Head and Neck Tumors with Amide Proton Transfer-Weighted MR Imaging