Comparison of choline influx from dynamic 18F-Choline PET/CT and clinicopathological parameters in prostate cancer initial assessment

Impact of point-spread function reconstruction on dynamic and static 18F-DOPA PET/CT quantitative parameters in glioma

BACKGROUND

Quantification of dynamic and static parameters extracted from 3,4-dihydroxy-6-[¹⁸F]-fluoro-L-phenylalanine (¹⁸F-DOPA, FDOPA) positron emission tomography (PET)/computed tomography (CT) plays a critical role for glioma assessment. The objective of the present study was to investigate the impact of point-spread function (PSF) reconstruction on these quantitative parameters.

METHODS

Fourteen patients with untreated gliomas and investigated with FDOPA PET/CT were analyzed. The distribution of the 14 cases was as follows: 6 astrocytomas-isocitrate dehydrogenase-mutant; 2 oligodendrogliomas/1p19q-codeleted-isocitrate dehydrogenase-mutant; and 6 isocitrate dehydrogenase-wild-type glioblastomas. A 0-20-min dynamic images (8×15, 2×30, 2×60, and 3×300 s post-injection) and a 0-20-min static image were reconstructed with and without PSF. Tumoral volumes-of-interest were generated on all of the PET series and the background volumes-of-interest were generated on the 0-20-min static image with and without PSF. Static parameters (SUVmax and SUVmean) of the tumoral and the background volumes-of-interest and kinetic parameters (K1 and k2) of the tumoral volumes-of-interest extracted from using full kinetic analysis were provided. PSF and non-PSF quantitative parameters values were compared.

RESULTS

Thirty-three tumor volumes-of-interest and 14 background volumes-of-interest were analyzed. PSF images provided higher tumor SUVmax than non-PSF images for 23/33 VOIs [median SUVmax =3.0 (range, 1.4-10.2) with PSF vs. 2.7 (range, 1.4-9.1) without PSF; P<0.001] and higher tumor SUVmean for 13/33 volumes-of-interest [median SUVmean =2.0 (range, 0.8-7.6) with PSF vs. 2.0 (range, 0.8-7.4) without PSF; P=0.002]. K1 and k2 were significantly lower with PSF than without PSF [respectively median K1 =0.077 mL/ccm/min (range, 0.043-0.445 mL/ccm/min) with PSF vs. 0.101 mL/ccm/min (range, 0.055-0.578 mL/ccm/min) without PSF; P<0.001 and median k2 =0.070 min^-1 (range, 0.025-0.146 min^-1) with PSF vs. 0.081 min^-1 (range, 0.027-0.180 min^-1) without PSF; P<0.001]. Background SUVmax and SUVmean were statistically unaffected [respectively median SUVmax =1.7 (range, 1.3-2.0) with PSF vs. 1.7 (range, 1.3-1.9) without PSF; P=0.346 and median SUVmean =1.5 (range, 1.0-1.8) with PSF vs. 1.5 (range, 1.0-1.7) without PSF; P=0.371].

CONCLUSIONS

The present study confirms that PSF significantly increases tumor activity concentrations measured on PET images. PSF algorithms for quantitative PET/CT analysis should be used with caution, especially for quantification of kinetic parameters.

Molecular Imaging in Primary Staging of Prostate Cancer Patients: Current Aspects and Future Trends

Accurate primary staging is the cornerstone in all malignancies. Different morphological imaging modalities are employed in the evaluation of prostate cancer (PCa). Regardless of all developments in imaging, invasive histopathologic evaluation is still the standard method for the detection and staging of the primary PCa. Magnetic resonance imaging (MRI) and computed tomography (CT) play crucial roles; however, functional imaging provides additional valuable information, and it is gaining ever-growing acceptance in the management of PCa. Targeted imaging with different radiotracers has remarkably evolved in the past two decades. [111In]In-capromab pendetide scintigraphy was a new approach in the management of PCa. Afterwards, positron emission tomography (PET) tracers such as [11C/18F]choline and [11C]acetate were developed. Nevertheless, none found a role in the primary staging. By introduction of the highly sensitive small molecule prostate-specific membrane antigen (PSMA) PET/CT, as well as recent developments in MRI and hybrid PET/MRI systems, non-invasive staging of PCa is being contemplated. Several studies investigated the role of these sophisticated modalities in the primary staging of PCa, showing promising results. Here, we recapitulate the role of targeted functional imaging. We briefly mention the most popular radiotracers, their diagnostic accuracy in the primary staging of PCa, and impact on patient management.

Additive Value of Dynamic FDOPA PET/CT for Glioma Grading

Purpose: The aim of this study was to assess the value of the FDOPA PET kinetic parameters extracted using full kinetic analysis for tumor grading with neuronavigation-guided biopsies as reference in patients with newly-diagnosed gliomas.

Methods: Fourteen patients with untreated gliomas were investigated. Twenty minutes of dynamic positron-emission tomography (PET) imaging and a 20-min static image 10 min after injection were reconstructed from a 40-min list-mode acquisition immediately after FDOPA injection. Tumors volume-of-interest (VOI) were generated based on the MRI-guided brain biopsies. Static parameters (TBRmax and TBRmean) and kinetic parameters [K1 and k2 using full kinetic analysis with the reversible single-tissue compartment model with blood volume parameter and the time-to-peak (TTP)] were extracted. Performances of each parameter for differentiating low-grade gliomas (LGG) from high-grade gliomas (HGG) were evaluated by receiver-operating characteristic analyses (area under the curve; AUC).

Results: Thirty-two tumoral VOI were analyzed. K1, k2, and TTP were significantly higher for HGG than for LGG (median K1-value = 0.124 vs. 0.074 ml/ccm/min, p = 0.025, median k2-value = 0.093 vs. 0.063 min⁻¹, p = 0.025, and median TTP-value = 10.0 vs. 15.0 min, p = 0.025). No significant difference was observed for the static parameters. The AUC for the kinetic parameters was higher than the AUC for the static parameters (respectively, AUC_K1 = 0.787, AUC_k2 = 0.785, AUC_TTP = 0.775, AUC_TBRmax = 0.551, AUC_TBRmean = 0.575), significantly compared to TBRmax (respectively, p = 0.001 for K1, p = 0.031 for k2, and p = 0.029 for TTP).

Conclusion: The present study suggests an additive value of FDOPA PET/CT kinetic parameters for newly-diagnosed gliomas grading.

Potential synergy between PSMA uptake and tumour blood flow for prediction of human prostate cancer aggressiveness

Background

Both prostate-specific membrane antigen (PSMA) uptake and tumour blood flow (TBF) correlate with International Society of Urological Pathology (ISUP) Grade Group (GG) and hence prostate cancer (PCa) aggressiveness. The aim of the present study was to evaluate the potential synergistic benefit of combining the two physiologic parameters for separating significant PCa from insignificant findings.

Methods

From previous studies of [⁸²Rb]Rb positron emission tomography (PET) TBF in PCa, the 43 patients that underwent clinical [⁶⁸Ga]Ga-PSMA-11 PET were selected for this retrospective study. Tumours were delineated on [⁶⁸Ga]Ga-PSMA-11 PET or magnetic resonance imaging. ISUP GG was recorded from 52 lesions.

Results

[⁶⁸Ga]Ga-PSMA-11 maximum standardized uptake value (SUVmax) and [⁸²Rb]Rb SUVmax correlated moderately with ISUP GG (rho = 0.59 and rho = 0.56, both p < 0.001) and with each other (r = 0.65, p < 0.001). A combined model of [⁶⁸Ga]Ga-PSMA-11 and [⁸²Rb]Rb SUVmax separated ISUP GG > 2 from ISUP GG 1–2 and benign with an area-under-the-curve of 0.85, 96% sensitivity, 74% specificity, and 95% negative predictive value. The combined model performed significantly better than either tracer alone did (p < 0.001), primarily by reducing false negatives from five or six to one (p ≤ 0.025).

Conclusion

PSMA uptake and TBF provide complementary information about tumour aggressiveness. We suggest that a combined analysis of PSMA uptake and TBF could significantly improve the negative predictive value and allow non-invasive separation of significant from insignificant PCa.

Tumour blood flow for prediction of human prostate cancer aggressiveness: a study with Rubidium-82 PET, MRI and Na+/K+-ATPase-density

Purpose

Tumour blood flow (TBF) is a crucial determinant of cancer growth. Recently, we validated Rubidium-82 (⁸²Rb) positron emission tomography (PET) for TBF measurement in prostate cancer (PCa) and found TBF and cancer aggressiveness positively correlated. The aims of the present study were to determine the ability of TBF for separating significant from insignificant PCa and to examine the relation to underlying Na⁺/K⁺-ATPase density, which is relevant as ⁸²Rb is transported intracellularly via the Na⁺/K⁺-ATPase.

Methods

One hundred and two patients were included for pelvic ⁸²Rb PET scan prior to magnetic resonance imaging (MRI)-guided prostate biopsy. Findings constituted 100 PCa lesions (86 patients) and 25 benign lesions (16 patients). Tumours were defined on MRI and transferred to ⁸²Rb PET for TBF measurement. Immunohistochemical Na⁺/K⁺-ATPase staining was subsequently performed on biopsies.

Results

TBF was the superior predictor (rho = 0.68, p < 0.0001, inflammatory lesions excluded) of MRI-guided biopsy grade group (GG) over lowest apparent diffusion coefficient (ADC) value (rho = −0.23, p = 0.01), independent of ADC value and tumour volume (p < 0.0001). PET could separate GG-2-5 from GG-1 and benign lesions with an area under the curve (AUC), sensitivity, and specificity of 0.79, 96%, and 59%, respectively. For separating GG-3-5 from GG-1-2 and benign lesions the AUC, sensitivity, and specificity were 0.82, 95%, and 63%, respectively. Na⁺/K⁺-ATPase density per PCa cell profile was 38% lower compared with that of the benign prostate cell profiles. Neither cell density nor Na⁺/K⁺-ATPase density determined tumour ⁸²Rb uptake.

Conclusion

TBF is an independent predictor of PCa aggressiveness and deserves more attention, as it may be valuable in separating clinically significant from insignificant PCa.

Electronic supplementary material

The online version of this article (10.1007/s00259-020-04998-2) contains supplementary material, which is available to authorized users.

Verification of image quality and quantification in whole-body positron emission tomography with continuous bed motion

OBJECTIVE

Whole-body dynamic imaging using positron emission tomography (PET) facilitates the quantification of tracer kinetics. It is potentially valuable for the differential diagnosis of tumors and for the evaluation of therapeutic efficacy. In whole-body dynamic PET with continuous bed motion (CBM) (WBDCBM-PET), the pass number and bed velocity are key considerations. In the present study, we aimed to investigate the effect of a combination of pass number and bed velocity on the quantitative accuracy and quality of WBDCBM-PET images.

METHODS

In this study, WBDCBM-PET imaging was performed at a body phantom using seven bed velocity settings in combination with pass numbers. The resulting image quality was evaluated. For comparing different acquisition settings, the dynamic index (DI) was obtained using the following formula: [P/S], where P represents the pass number, and S represents the bed velocity (mm/s). The following physical parameters were evaluated: noise equivalent count at phantom (NEC_phantom), percent background variability (N_10 mm), percent contrast of the 10 mm hot sphere (Q_{H, 10 mm}), the Q_{H, 10 mm}/N_10 mm ratio, and the maximum standardized uptake value (SUV_max). Furthermore, visual evaluation was performed.

RESULTS

The NEC_phantom was equivalent for the same DI settings regardless of the bed velocity. The N_10 mm exhibited an inverse correlation (r < - 0.89) with the DI. Q_H,10 mm was not affected by DI, and a correlation between Q_H,10 mm/N_10 mm ratio and DI was found at all the velocities (r > 0.93). The SUV_max of the spheres was not influenced by the DI. The coefficient of variations caused by bed velocity decreased in larger spheres. There was no significant difference between the bed velocities on visual evaluation.

CONCLUSION

The quantitative accuracy and image quality achieved with WBDCBM-PET was comparable to that achieved with non-dynamic CBM, regardless of the pass number and bed velocity used during imaging for a given acquisition time.

Comparison of 18F-Choline PET/CT and MRI functional parameters in prostate cancer

AIM

¹⁸F-Choline (FCH) uptake parameters are strong indicators of aggressive disease in prostate cancer. Functional parameters derived by magnetic resonance imaging (MRI) are also correlated to aggressive disease. The aim of this work was to evaluate the relationship between metabolic parameters derived by FCH PET/CT and functional parameters derived by MRI.

MATERIALS AND METHODS

Fourteen patients with proven prostate cancer who underwent FCH PET/CT and multiparametric MRI were enrolled. FCH PET/CT consisted in a dual phase: early pelvic list-mode acquisition and late whole-body acquisition. FCH PET/CT and multiparametric MRI examinations were registered and tumoral volume-of-interest were drawn on the largest lesion visualized on the apparent diffusion coefficient (ADC) map and projected onto the different multiparametric MR images and FCH PET/CT images. Concerning the FCH uptake, kinetic parameters were extracted with the best model selected using the Akaike information criterion between the one- and two-tissue compartment models with an imaging-derived plasma input function. Other FCH uptake parameters (early SUVmean and late SUVmean) were extracted. Concerning functional parameters derived by MRI scan, cell density (ADC from diffusion weighting imaging) and vessel permeability (K_trans and V_e using the Tofts pharmakinetic model from dynamic contrast-enhanced imaging) parameters were extracted. Spearman's correlation coefficients were calculated to compare parameters.

RESULTS

The one-tissue compartment model for kinetic analysis of PET images was selected. Concerning correlation analysis between PET parameters, K₁ was highly correlated with early SUVmean (r = 0.83, p < 0.001) and moderately correlated with late SUVmean (r = 0.66, p = 0.010) and early SUVmean was highly correlated with late SUVmean (r = 0.90, p < 0.001). No significant correlation was found between functional MRI parameters. Concerning correlation analysis between PET and functional MRI parameters, K₁ (from FCH PET/CT imaging) was moderately correlated with K_trans (from perfusion MR imaging) (r = 0.55, p = 0.041).

CONCLUSIONS

No significant correlation was found between FCH PET/CT and multiparametric MRI metrics except FCH influx which is moderately linked to the vessel permeability in prostate cancer.

Optimization of temporal sampling for 18F-choline uptake quantification in prostate cancer assessment

Background

Suboptimal temporal sampling of time-activity curves (TAC) from dynamic ¹⁸F-fluoromethylcholine (FCH) PET images may introduce bias in quantification of FCH uptake in prostate cancer assessment. We sought to define an optimal temporal sampling protocol for dynamic FCH PET imaging.

Seven different time samplings were tested: 5 × 60″, 10 × 30″, 15 × 15″–1 × 75″, 6 × 10″–8 × 30″, 12 × 5″–8 × 30″; 10 × 5″–4 × 10″–3 × 20″–5 × 30″, and 8 × 3″–8 × 12″–6 × 30″. First, the irreversible and reversible one-tissue compartment model with blood volume parameter (VB) (respectively, 1T1K+VB and 1T2k+VB, with K1 = transfer coefficient from the arterial blood to the tissue compartment and k2 = transfer coefficient from the tissue compartment to the arterial blood) were compared for 37 lesions from 32 patients who underwent FCH PET imaging for initial or recurrence assessment of prostate cancer, and the model was selected using the Akaike information criterion. To determine the optimal time sampling, K1 values extracted from 1000 noisy-simulated TAC using Monte Carlo method from the seven different time samplings were compared to a target K1 value which is the average of the K1 values extracted from the 37 lesions using an imaging-derived input function for each patient. K1 values extracted with the optimal time sampling for each tumoral lesion were compared to K1 values extracted from each of the other time samplings for the 37 lesions.

Results

The 1T2k + VB model was selected. The target K1 value as the objective was 0.506 mL/ccm/min (range 0.216–1.246). Results showed a significant difference between K1 values from the simulated TAC with the seven different time samplings analyzed. The closest K1 value from the simulated TAC to the target K1 value was obtained by the 12 × 5″–8 × 30″ time sampling. Concerning the clinical validation, K1 values extracted from the optimal time sampling (12 × 5″–8 × 30″) were significantly different with K1 values extracted from the other time samplings, except for the comparison with K1 values extracted from the 10 × 5″–4 × 10″–3 × 20″–5 × 30″ time sampling.

Conclusions

A two-phase framing of dynamic PET reconstruction with frame durations of 5 s (blood phase) and 30 s (tissue phase) could be used to sample the TAC for uptake quantification in prostate cancer assessment.