Table 1.

Overview of the basic classes of quantitative PET imaging methods applicable to clinical oncology

Quantitative method	Metric	Definition/Scope	Advantages	Limitation	Accuracy	Precision	Oncology applications	Implementation status
First-order static	SUVmax SUVpeak	Time-averaged uptake account for dose and (lean) body mass	Simplified acquisition, established in the clinic, applicable to low counts	Semi-quantitative, SUVmax can be highly sensitive to noise	Post-injection time-dependent, neglecting blood plasma activity & tumour metabolic volume	SUVmax: low in regions of low uptake	Standard-of-care & research clinical scan protocols	Completed, widely employed and supported
Second-order (post region delineation) static	SUVmean MTV TLG	Time- and space-averaged uptake, accounts for dose, body mass and metabolic tumour volume	Simplified, adoptable, applicable to low counts, relevant to metabolic tumour volume	Semi-quantitative, highly dependent on tumour delineation and PVE	Time- and segmentation-dependent, neglecting blood plasma activity	Moderate due to high dependence on tumour delineation and PVE	Widely employed in clinical research, but not yet standard-of-care, promising for tumour staging	Ongoing development of automated robust tumour delineation & segmentation methods
Third-order (radiomics) static	Texture uptake features	Low- and high-order texture and heterogeneity uptake features	Quite large variety of features high predictive power for malignancy capable of assessing heterogeneous uptake features	Potentially large intercorrelations between a wide set of features requires sufficiently high uptake contrast and number of tumour voxels	Limited accuracy depending on resolution, number of intratumour voxels and number of counts/voxels, sensitive to PVE and tumour delineation	Highly variable precision depending on the order of the evaluated texture feature	Demonstrated correlation between therapy outcome and certain texture features, enhanced prognosis of malignant tumours	Recently emerged, extensive validation and machine/deep learning developments required
Simplified kinetic analysis	Dual-time point: SUVratio or simplified Ki assuming known V macro-parameter	Relevant SUV difference based on two dynamic frames Ki evaluation on ROIs only after taking plenty of kinetic assumptions	Simplified, adoptable, robust (linear models) applicable even for very few (two) dynamic frames per bed or in the absence of input function	Relies on too many kinetic assumptions, ROI-based analysis only, requires a priori knowledge of macro-parameters, very limited clinical usefulness	Quite limited accuracy due to too many assumptions, dual-time image analysis highly dependent on the selected acquisition time window	Robust precise ROI-based macro-parameter estimates thanks to linear modelling	Very limited applications in 18F-FDG and 18F-NaF (sodium fluoride) tumour imaging	Completed for dynamic ROI-based analysis of specific types of malignancies, extensive clinical validation required for each of the taken kinetic assumptions
Dynamic (low-order) graphical analysis	Kinetic macro-parameters Patlak (Ki, V, kloss) Logan (DV) accounting for the input function	Kinetic macro-parameters imaging (net uptake rate, distribution volume, etc.) taking only few assumptions, may offer highly quantitative imaging of a summary of kinetic uptake attributes at high precision, beyond SUV, and across WB FOVs	Adoptable even for WB dynamic scans, linearized robust voxel-based imaging of clinically relevant macro-parameters, Patlak class suitable for WB parametric imaging direct-4D graphical analysis associated with high precision linear methods applicable in theory even for two dynamic frames, most preferred parametric imaging class of methods in clinical practice	Model assumptions valid only after a certain post-injection time, robust linear sPatlak does not account for uptake reversibility (k4 > 0), linearized gPatlak accounts for mild reversibility as well (small positive k4) but not very robust	Highly accurate provided reasonable temporal sampling rate and validity of model assumptions, Logan DV accuracy relies on fine temporal sampling of early uptake, sPatlak under-estimates Ki if true k4 > 0 especially at later times, gPatlak may achieve high Ki accuracy regardless of zero or mildly positive k4 and scan time window, potential errors in parameter estimates may propagate beyond their origin with direct 4D imaging	Single-bed dynamic: high precision in parametric images, WB dynamic: moderate (indirect) to high (direct-4D) precision, sPatlak achieved higher precision than gPatlak when applied indirectly, sPatlak and gPatlak direct-4D algorithms exhibited similar precision levels, in terms of CNR detectability, gPatlak achieved higher scores for tumours with non-negligible uptake reversibility	Applicable to both single-bed but most importantly WB dynamic PET imaging protocols (the latter with Patlak methods), WB Patlak 18F-FDG imaging demonstrated improved tumour detectability in the liver and thorax, WB gPatlak 18F-FDG imaging exhibited further detectability enhancement in tumours with mild uptake reversibility (hepato-cellular carcinomas), clinical application of combined SUV/Patlak imaging demonstrated potential of multiparametric 18F-FDG PET in oncology	Completed for both indirect and direct 4D imaging for most graphical analysis methods employed in oncology research studies, clinically adoptable indirect and 4D WB Patlak imaging algorithms implemented in clinical setting in academic and corporate settings, ongoing clinical validation of specific features of hybrid SUV/Patlak 18F-FDG imaging algorithms: population-based image-derived input function & synthesis of SUV PET images from the dynamic WB PET data
Dynamic compartment (high-order) full kinetic modelling	Kinetic microparameters (K1, k2, k3 and k4) accounting for the input function	Kinetic microparameter imaging (uptake rate constants) taking minimum kinetic assumptions, may offer highly quantitative detailed information, in addition to SUV, but only for a selected single bed position	Highly accurate estimation of the fine kinetic features of tracers’ dynamic uptake, most accurate parametric imaging methods (specialists best friend)	Not very robust to high PET data noise levels due to non-linear estimation of a large number of kinetic parameters, sensitive to noise-induced bias, not possible for multibed kinetic analysis, nevertheless single-bed fully kinetic modelling can be combined with multibed Patlak imaging	Highly accurate at moderate noise levels, potential noise-induced bias at PET data high noise levels, potential errors in parameter estimates may propagate beyond their origin with direct 4D imaging	Moderate to low precision in parametric images even with direct-4D imaging due to non-linear modelling	Full kinetic modelling applied in clinical research studies for specific type of tumours and tracers (primarily 18F-FDG), demonstrated improved tumour detectability, potential utilization for the identification of benign and malignant tumours	Basic implementation of variants of compartmental kinetic modelling analysis (with 1 or 2-tissue compartments) have been completed but with very limited clinical validation for only specific types of malignancy and tracers (primarily 18F-FDG)

CNR, contrast-to-noise ratio; 4D, four-dimensional; FOV, field-of-view; MTV, metabolic active volume; PET, positron emission tomography; PVE, partial volume effect; ROI, region of interest; SUV, standardized uptake value; TLG, total lesion glycolysis; WB, whole -body.