Neoangiogenesis in prostate cancer

Determining the optimal pharmacokinetic modelling and simplified quantification method of [18F]AlF-P16-093 for patients with primary prostate cancer (PPCa)

PURPOSE

This paper discusses the optimization of pharmacokinetic modelling and alternate simplified quantification method for [18F]AlF-P16-093, a novel tracer for in vivo imaging of prostate cancer.

METHODS

Dynamic PET/CT scans were conducted on eight primary prostate cancer patients, followed by a whole-body scan at 60 min post-injection. Time-activity curves (TACs) were obtained by drawing volumes of interest for primary prostatic and metastatic lesions. Optimal kinetic modelling involved evaluating three compartmental models (1T2K, 2T3K, and 2T4K) accounting for fractional blood volume (Vb). The simplified quantification method was then determined based on the correlation between the static uptake measure and total distribution volume (Vt) obtained from the optimal pharmacokinetic analysis.

RESULTS

In total, 17 intraprostatic lesions, 10 lymph nodes, and 36 osseous metastases were evaluated. Visually, the contrast of the tumor increased and showed the steepest incline within the first few minutes, whereas background activity decreased over time. Full pharmacokinetic analysis revealed that a reversible two-compartmental (2T4K) model is the preferred kinetic model for the given tracer. The kinetic parameters K1, k3, Vb, and Vt were all significantly higher in lesions when compared with normal tissue (P < 0.01). Several simplified protocols were tested for approximating comprehensive dynamic quantification in tumors, with image-based SURmean (the ratio of tumor SUVmean to blood SUVmean) within the 28-34 min window found to be sufficient for approximating the total distribution Vt values (R2 = 0.949, P < 0.01). Both Vt and SURmean correlated significantly with the total serum prostate-specific antigen (tPSA) levels (P < 0.01).

CONCLUSIONS

This study introduced an optimized pharmacokinetic modelling approach and a simplified acquisition method for [18F]AlF-P16-093, a novel PSMA-targeted radioligand, highlighting the feasibility of utilizing one static PET imaging (between 30 and 60 min) for the diagnosis of prostate cancer. Note that the image-derived input function in this study may not reflect the true corrected plasma input function, therefore the interpretation of the associated kinetic parameter estimates should be done with caution.

Quantitative dynamic contrast-enhance MRI parameters for rectal carcinoma characterization: correlation with tumor tissue composition

OBJECTIVE

To investigate the relationship between dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) measurements and the potential composition of rectal carcinoma.

METHODS

Twenty-four patients provided informed consent for this study. DCE-MRI was performed before total mesorectal excision. Quantitative parameters were calculated based on a modified Tofts model. Whole-mount immunohistochemistry and Masson staining sections were generated and digitized at histological resolution. The percentage of tissue components area was measured. Pearson correlation analysis was used to evaluate the correlations between pathological parameters and DCE-MRI parameters.

RESULTS

On the World Health Organization (WHO) grading scale, there were significant differences in extracellular extravascular space (Ktrans) (F = 9.890, P = 0.001), mean transit time (MTT) (F = 9.890, P = 0.038), CDX-2 (F = 4.935, P = 0.018), and Ki-67 (F = 4.131, P = 0.031) among G1, G2, and G3. ECV showed significant differences in extramural venous invasion (t =  - 2.113, P = 0.046). Ktrans was strongly positively correlated with CD34 (r = 0.708, P = 0.000) and moderately positively correlated with vimentin (r = 0.450, P = 0.027). Interstitial volume (Ve) was moderately positively correlated with Masson's (r = 0.548, P = 0.006) and vimentin (r = 0.417, P = 0.043). There was a moderate negative correlation between Ve and CDX-2 (r =  - 0.441, P = 0.031). The rate constant from extracellular extravascular space to blood plasma (Kep) showed a strong positive correlation with CD34 expression (r = 0.622, P = 0.001). ECV showed a moderate negative correlation with CDX-2 (r =  - 0.472, P = 0.020) and a moderate positive correlation with collagen fibers (r = 0.558, P = 0.005).

CONCLUSION

The dynamic contrast-enhanced MRI-derived parameters measured in rectal cancer were significantly correlated with the proportion of histological components. This may serve as an optimal imaging biomarker to identify tumor tissue components.

Kinetic modeling of 68Ga-PSMA-11 and validation of simplified methods for quantification in primary prostate cancer patients

BACKGROUND

The positron emission tomography (PET) ligand ⁶⁸Ga-Glu-urea-Lys(Ahx)-HBED-CC (⁶⁸Ga-PSMA-11) targets the prostate-specific membrane antigen (PSMA), upregulated in prostate cancer cells. Although ⁶⁸Ga-PSMA-11 PET is widely used in research and clinical practice, full kinetic modeling has not yet been reported nor have simplified methods for quantification been validated. The aims of our study were to quantify ⁶⁸Ga-PSMA-11 uptake in primary prostate cancer patients using compartmental modeling with arterial blood sampling and to validate the use of standardized uptake values (SUV) and image-derived blood for quantification.

RESULTS

Fifteen patients with histologically proven primary prostate cancer underwent a 60-min dynamic ⁶⁸Ga-PSMA-11 PET scan of the pelvis with axial T1 Dixon, T2, and diffusion-weighted magnetic resonance (MR) images acquired simultaneously. Time-activity curves were derived from volumes of interest in lesions, normal prostate, and muscle, and mean SUV calculated. In total, 18 positive lesions were identified on both PET and MR. Arterial blood activity was measured by automatic arterial blood sampling and manual blood samples were collected for plasma-to-blood ratio correction and for metabolite analysis. The analysis showed that ⁶⁸Ga-PSMA-11 was stable in vivo. Based on the Akaike information criterion, ⁶⁸Ga-PSMA-11 kinetics were best described by an irreversible two-tissue compartment model. The rate constants K₁ and k₃ and the net influx rate constants K_i were all significantly higher in lesions compared to normal tissue (p < 0.05). K_i derived using image-derived blood from an MR-guided method showed excellent agreement with K_i derived using arterial blood sampling (intraclass correlation coefficient = 0.99). SUV correlated significantly with K_i with the strongest correlation of scan time-window 30-45 min (rho 0.95, p < 0.001). Both K_i and SUV correlated significantly with serum prostate specific antigen (PSA) level and PSA density.

CONCLUSIONS

⁶⁸Ga-PSMA-11 kinetics can be described by an irreversible two-tissue compartment model. An MR-guided method for image-derived blood provides a non-invasive alternative to blood sampling for kinetic modeling studies. SUV showed strong correlation with K_i and can be used in routine clinical settings to quantify ⁶⁸Ga-PSMA-11 uptake.

A Bayesian Nonparametric Approach for Functional Data Classification with Application to Hepatic Tissue Characterization

Computed tomography perfusion (CTp) is an emerging functional imaging technology that provides a quantitative assessment of the passage of fluid through blood vessels. Tissue perfusion plays a critical role in oncology due to the proliferation of networks of new blood vessels typical of cancer angiogenesis, which triggers modifications to the vasculature of the surrounding host tissue. In this article, we consider a Bayesian semiparametric model for the analysis of functional data. This method is applied to a study of four interdependent hepatic perfusion CT characteristics that were acquired under the administration of contrast using a sequence of repeated scans over a period of 590 seconds. More specifically, our modeling framework facilitates borrowing of information across patients and tissues. Additionally, the approach enables flexible estimation of temporal correlation structures exhibited by mappings of the correlated perfusion biomarkers and thus accounts for the heteroskedasticity typically observed in those measurements, by incorporating change-points in the covariance estimation. This method is applied to measurements obtained from regions of liver surrounding malignant and benign tissues, for each perfusion biomarker. We demonstrate how to cluster the liver regions on the basis of their CTp profiles, which can be used in a prediction context to classify regions of interest provided by future patients, and thereby assist in discriminating malignant from healthy tissue regions in diagnostic settings.

Validation of Perfusion Quantification with 3D Gradient Echo Dynamic Contrast-Enhanced Magnetic Resonance Imaging Using a Blood Pool Contrast Agent in Skeletal Swine Muscle

The purpose of our study was to validate perfusion quantification in a low-perfused tissue by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) with shared k-space sampling using a blood pool contrast agent. Perfusion measurements were performed in a total of seven female pigs. An ultrasonic Doppler probe was attached to the right femoral artery to determine total flow in the hind leg musculature. The femoral artery was catheterized for continuous local administration of adenosine to increase blood flow up to four times the baseline level. Three different stable perfusion levels were induced. The MR protocol included a 3D gradient-echo sequence with a temporal resolution of approximately 1.5 seconds. Before each dynamic sequence, static MR images were acquired with flip angles of 5°, 10°, 20°, and 30°. Both static and dynamic images were used to generate relaxation rate and baseline magnetization maps with a flip angle method. 0.1 mL/kg body weight of blood pool contrast medium was injected via a central venous catheter at a flow rate of 5 mL/s. The right hind leg was segmented in 3D into medial, cranial, lateral, and pelvic thigh muscles, lower leg, bones, skin, and fat. The arterial input function (AIF) was measured in the aorta. Perfusion of the different anatomic regions was calculated using a one- and a two-compartment model with delay- and dispersion-corrected AIFs. The F-test for model comparison was used to decide whether to use the results of the one- or two-compartment model fit. Total flow was calculated by integrating volume-weighted perfusion values over the whole measured region. The resulting values of delay, dispersion, blood volume, mean transit time, and flow were all in physiologically and physically reasonable ranges. In 107 of 160 ROIs, the blood signal was separated, using a two-compartment model, into a capillary and an arteriolar signal contribution, decided by the F-test. Overall flow in hind leg muscles, as measured by the ultrasound probe, highly correlated with total flow determined by MRI, R = 0.89 and P = 10⁻⁷. Linear regression yielded a slope of 1.2 and a y-axis intercept of 259 mL/min. The mean total volume of the investigated muscle tissue corresponds to an offset perfusion of 4.7mL/(min ⋅ 100cm³). The DCE-MRI technique presented here uses a blood pool contrast medium in combination with a two-compartment tracer kinetic model and allows absolute quantification of low-perfused non-cerebral organs such as muscles.

Multimodal approach to assess tumour vasculature and potential treatment effect with DCE-US and DCE-MRI quantification in CWR22 prostate tumour xenografts

The aim of this study was to compare intratumoural heterogeneity and longitudinal changes assessed by dynamic contrast-enhanced ultrasound (DCE-US) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in prostate tumour xenografts. In vivo DCE-US and DCE-MRI were obtained 24 h pre- (day 0) and post- (day 2) radiation treatment with a single dose of 7.5 Gy. Characterization of the tumour vasculature was determined by Brix pharmacokinetic analysis of the time-intensity curves. Histogram analysis of voxels showed significant changes (p < 0.001) from day 0 to day 2 in both modalities for kep , the exchange rate constant from the extracellular extravascular space to the plasma, and kel , the elimination rate constant of the contrast. In addition, kep and kel values from DCE-US were significantly higher than those derived from DCE-MRI at day 0 (p < 0.0001) for both groups. At day 2, kel followed the same tendency for both groups, whereas kep showed this tendency only for the treated group in intermediate-enhancement regions. Regarding kep median values, longitudinal changes were not found for any modality. However, at day 2, kep linked to DCE-US was correlated to MVD in high-enhancement areas for the treated group (p = 0.05). In contrast, correlation to necrosis was detected for the control group in intermediate-enhancement areas (p < 0.1). Intratumoural heterogeneity and longitudinal changes in tumour vasculature were assessed for both modalities. Microvascular parameters derived from DCE-US seem to provide reliable biomarkers during radiotherapy as validated by histology. Furthermore, DCE-US could be a stand-alone or a complementary technique.