Quantification of [68Ga]PSMA-11 PET

One of most used PET tracers for imaging PSMA expressing tumours is [68Ga]PSMA-11 ([68Ga]PSMA-HBED-CC, [68Ga]DKFZ-PSMA-11, [68Ga]Glu-CO-Lys(Ahx)-HBED-CC, Glu-NH-CO-NH-Lys-(Ahx)-[68Ga(HBED-CC)]). This tracer binds to the extracellular domain of PSMA, and is then slowly internalized. [68Ga]PSMA-11 is used for initial staging and detecting residual and recurrent prostate cancer (Eiber et al., 2015; Verburg et al., 2016; Rauscher et al., 2016 and 2018; Rahbar et al., 2016; Bailey & Piert, 2017; Einspieler et al., 2017; Uprimny et al., 2017a); Hicks et al., 2018; Emmett et al., 2019; Farolfi et al., 2019; Hope et al., 2019; Jilg et al., 2019; Müller et al., 2019; Farolfi et al., 2019 and 2020; Kranzbühler et al., 2020), and can be useful for individual dosimetry planning for PSMA-ligand based radiotherapy (Scarpa et al., 2017), adjuvant and salvage radiotherapy (ART and SRT) planning (Sterzing et al., 2016; Calais et al., 2018), and treatment response assessment (Seitz et al., 2018; Grubmüller et al., 2019; Plouznikoff et al., 2019). Bone metastases can be found even in patients with low PSA level (Pomykala et al., 2020). In a matched-pair analysis the detection rates of recurrent tumours were similar between [68Ga]PSMA-11 and [18F]PSMA-1007 (Kroenke et al., 2019).


[68Ga]PSMA-11 PET can be used for detecting breast cancer and its metastases (Kasoha et al., 2017; Sathekge et al., 2017; Morgenroth et al., 2019), glioblastoma (Schwenck et al., 2015; Unterrainer et al., 2017), and certain types of renal carcinomas. Lung [68Ga]PSMA-11 PET could not reliably discriminate between primary lung cancer, prostate cancer metastases, and tuberculous lesions (Pyka et al., 2016).

Voxel-based analysis of ex vivo samples has shown high agreement between radioligand uptake and histopathology (Zamboglou et al., 2016), and lesion SUVmax correlates with immunohistochemically determined PSMA expression (Woythal et al., 2018). Tumour detection is positively associated with prostate-specific antigen (PSA) and androgen deprivation therapy (ADT) (Afshar-Oromieh et al, 2015).

The optimal acquisition protocol and timing remain under debate for [68Ga]PSMA-11 and other PSMA tracers (Afshar-Oromieh et al., 2016a and 2017; Alberts et al., 2020b). The uptake of [68Ga]PSMA-11 in prostate cancer cells increases with time, unlike in benign tissues, leading to continuously increasing tumour-to-background ratio and increased image contrast (Sahlmann et al., 2016). Benign lymph nodes (LNs) and ganglia are commonly visible in [68Ga]PSMA-11 PET, albeit uptake is usually lower than in metastatic LNs, with remarkable overlap (Alberts et al., 2020b). Comparison of PET scans at 1 h and 3 hr has suggested that in benign LNs the uptake is often decreasing and in metastatic LNs increasing with time (Afshar-Oromieh et al., 2018; Alberts et al., 2020a). In another comparison of PET scans at 1.5 h and 3.5 h, ∼1/3 of ganglia exhibited increased SUV (Alberts et al., 2020b). Currently, the recommended scan time is 60 min after administration, with acceptable range of 50-100 min; in case of indeterminate scan at 60 min, late imaging is recommended in EANM/SNMMI guidelines (Fendler et al., 2017). Recommended tumour uptake metrics is SUVmax (Fendler et al., 2017). Image interpretation must also be standardized (Fanti et al., 2017).

In healthy subjects, [68Ga]PSMA-11 is taken up in the salivary glands, liver, spleen, small bowel, kidneys, and urinary tract (mainly bladder) (Afshar-Oromieh et al., 2013; Prasad et al., 2016; Malaspina et al., 2018). The high [68Ga]PSMA-11 uptake in salivary glands is mostly non-specific: uptake does not correlate with PSMA expression in submandibular glands, and PSMA inhibitors do not markedly reduce the uptake there like in tissues with high PSMA expression (Rupp et al., 2019). In benign prostate tissue the accumulation of [68Ga]PSMA-11 is higher in the central zone than in the transition or peripheral zone (Pizzuto et al., 2018).

[68Ga]PSMA-11 is rapidly cleared from circulation and non-target tissues, and then excreted into urine. High urine concentration may hinder the evaluation of the prostate bed and pelvic lymph nodes (Malaspina et al., 2018). Forced diuresis and delayed PET imaging can markedly improve image quality and reduce scatter artefacts (Derlin et al., 2016; Lawhn-Heath et al., 2018). With PET-MR the scatter correction method should be optimized to improve the prostate imaging (Lindemann et al., 2019).

Internalization rate of PSMA radioligands has been assessed in vitro in PSMA-expressing cell cultures. Internalization of [68Ga]PSMA-11 is relatively slow: Lütje et al (2019) measured ∼10% internalization after 60 min, about the same percentage that was membrane bound.


Input function

For precise input function determination, automatic blood sampling system has been used to collect blood data during the first 10 min, followed by manual arterial sampling (Ringheim et al., 2020). Plasma-to-blood ratio was constant during 60 study, with mean 1.62, and no radioactive metabolites were present in plasma (Ringheim et al., 2020).

In PET imaging of the pelvic area, abdominal aorta or iliac arteries are visible in the image and can be used to derive image-derived input function (Sachpekidis et al., 2016a; Ringheim et al., 2020).

Compartmental model

Reversible two-tissue compartmental model can be used to analyze tissue time-activity curves (TACs), using plasma TAC as input function. The first tissue compartment represents the free and non-specifically bound tracer in the interstitial space, and the second compartment the tracer bound to PSMA on cell surfaces or the internalized tracer-PSMA complex. Rate constants K1 and k2 reflect the forward and reverse transport between plasma and the first tissue compartment; k3 represents the binding of tracer to PSMA and its internalization, and k4 represents the dissociation of tracer from PSMA and externalization (Sachpekidis et al., 2016a). Vascular volume fraction (VB) is additionally fitted as model parameter (Sachpekidis et al., 2016a). This model is used primary and recurrent prostate cancer, and in bone metastases of prostate cancer (Sachpekidis et al., 2016a, 2016b, 2018a, and 2018b). With good-quality arterial input function, and based on AIC analysis, Ringheim et al (2020) concluded that irreversible two-tissue compartmental model without VB best described the 60-min lesion data. While even this relatively simple model did not provide good parameter estimates in all lesions, robust values for K1 and Ki, calculated from model parameters, were robust in all cases Ringheim et al., 2020).

Patlak plot

Parametric Ki images calculated from dynamic 60 min scan using Patlak plot, and the intercept images from the Patlak plot, are helpful in detection of local recurrence of prostate cancer (Sachpekidis et al., 2018b). Patlak plot became linear 15 min after [68Ga]PSMA-11 administration (Ringheim et al., 2020).

SUV and tumour-to-background ratio

[68Ga]PSMA-11 SUV correlates well with Ki (Sachpekidis et al., 2016b; Ringheim et al., 2020). For [68Ga]PSMA-11 SUVLBM has been recommended over SUVBW (Gafita et al., 2019). For [18F]DCFPyL, SUV had lower interindividual variance than SUL (SUVLBM) (Li et al., 2017).

SUV at 3 h p.i. is significantly higher than 1 h SUV in most lesions, but some lesions were better detected in the PET scan at 1 h (Hohberg et al., 2019; Alberts et al., 2020a). Oral hydration and diuretics may further improve detection of tumours adjacent to urinary bladder (Haupt et al., 2020).

Very early images (5-12 min p.i.) can help to distinguish between lesions and urinary bladder (Kabasakal et al., 2015; Uprimny et al., 2017b and 2017c; Sachpekidis et al., 2018b).

Blood pool is the preferred reference for [68Ga]PSMA-11 for calculating ratios (Jansen et al., 2019a). For [18F]DCFPyL the tumour-to-blood ratio was found to correlate well with Ki calculated from reversible two-tissue compartmental model (Jansen et al., 2019b). For [18F]DCFPyL the “sink effect” of increased tumour burden is generally low (Werner et al., 2020), but at least in some cases marked “sink effect” can be observed in SUVs, but avoided in tumour-to-blood ratio (Cysouw et al., 2020).

Tumour-to-background ratio (TBR) has been calculated using liver as reference region (Hohberg et al., 2019). SUV in liver after 1 h is 4.6±1.0 and in gluteus muscle 0.36±0.08 (Hammes et al., 2018). With another PSMA radioligand, [18F]DCFPyL, liver uptake was less variable between subjects than the uptake of FDG (Li et al., 2017), supporting its use as reference region.

Fractal analysis

Fractal analysis has been applied to [68Ga]PSMA-11 PET data to assess the heterogeneity of PSMA expression inside the lesions (Sachpekidis et al., 2016a, 2016b, and 2018).

Machine learning

Deep neural network, mimicking the work flow of physicians, has potential for assessing tumour burden and optimizing PSMA-targeted radiotherapy (Zhao et al., 2020).

See also:


Ringheim A, Campos Neto GC, Anazodo U, Cui L, da Cunha ML, Vitor T, Martins KM, Miranda ACC, de Barboza MF, Fuscaldi LL, Lemos GC, Colombo Jr JR, Baroni RH. Kinetic modeling of 68Ga-PSMA-11 and validation of simplified methods for quantification in primary prostate cancer patients. EJNMMI Res. 2020; 10(1):12. doi: 10.1186/s13550-020-0594-6.

Sachpekidis C, Eder M, Kopka K, Mier W, Hadaschik BA, Haberkorn U, Dimitrakopoulou-Strauss A. 68Ga-PSMA-11 dynamic PET/CT imaging in biochemical relapse of prostate cancer. Eur J Nucl Med Mol Imaging 2016a; 43(7): 1288-1299. doi: 10.1007/s00259-015-3302-4.

Sachpekidis C, Kopka K, Eder M, Hadaschik BA, Freitag MT, Pan L, Haberkorn U, Dimitrakopoulou-Strauss A. 68Ga-PSMA-11 dynamic PET/CT imaging in primary prostate cancer. Clin Nucl Med. 2016b; 41(11): e473-e479. doi: 10.1097/RLU.0000000000001349.

Sachpekidis C, Bäumer P, Kopka K, Hadaschik BA, Hohenfellner M, Kopp-Schneider A, Haberkorn U, Dimitrakopoulou-Strauss A. 68Ga-PSMA PET/CT in the evaluation of bone metastases in prostate cancer. Eur J Nucl Med Mol Imaging 2018; 45(6): 904-912. doi: 10.1007/s00259-018-3936-0.

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Updated at: 2020-04-27
Created at: 2015-03-25
Written by: Vesa Oikonen, Anne Roivainen