Short-duration dynamic [18F]DCFPyL PET and CT perfusion imaging to localize dominant intraprostatic lesions in prostate cancer: validation against digital histopathology and comparison to [18F]DCFPyL PET/MR at 120 minutes.

PURPOSE: Localized prostate cancer (PCa) in patients is characterized by a dominant focus in the gland (dominant intraprostatic lesion, DIL). Accurate DIL identification may enable more accurate diagnosis and therapy through more precise targeting of biopsy, radiotherapy and focal ablative therapies. The goal of this study is to validate the performance of [18F]DCFPyL PET and CT perfusion (CTP) for detecting and localizing DIL against digital histopathological images. METHODS: Multi-modality image sets: in vivo T2-weighted (T2w)-MRI, 22-min dynamic [18F]DCFPyL PET/CT, CTP, and 2-h post-injection PET/MR were acquired in patients prior to radical prostatectomy. The explanted gland with implanted fiducial markers was imaged with T2w-MRI. All images were co-registered to the pathologist-annotated digital images of whole-mount mid-gland histology sections using fiducial markers and anatomical landmarks. Regions of interest encompassing DIL and non-DIL tissue were drawn on the digital histopathological images and superimposed on PET and CTP parametric maps. Logistic regression with backward elimination of parameters was used to select the most sensitive parameter set to distinguish DIL from non-DIL voxels. Leave-one-patient-out cross-validation was performed to determine diagnostic performance. RESULTS: [18F]DCFPyL PET and CTP parametric maps of 15 patients were analyzed. SUVLate and a model combining Ki and k4 of [18F]DCFPyL achieved the most accurate performance distinguishing DIL from non-DIL voxels. Both detection models achieved an AUC of 0.90 and an error rate of < 10%. Compared to digital histopathology, the detected DILs had a mean dice similarity coefficient of 0.8 for the Ki and k4 model and 0.7 for SUVLate. CONCLUSIONS: We have validated using co-registered digital histopathological images that parameters from kinetic analysis of 22-min dynamic [18F]DCFPyL PET can accurately localize DILs in PCa for targeting of biopsy, radiotherapy, and focal ablative therapies. Short-duration dynamic [18F]DCFPyL PET was not inferior to SUVLate in this diagnostic task. CLINICAL TRIAL REGISTRATION NUMBER: NCT04009174 (ClinicalTrials.gov).

Evaluation of pharmacokinetic models for perfusion imaging with dynamic contrast-enhanced magnetic resonance imaging in porcine skeletal muscle using low-molecular-weight contrast agents.

PURPOSE: Pharmacokinetic models for perfusion quantification with a low-molecular-weight contrast agent (LMCA) in skeletal muscle using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) were evaluated. METHODS: Tissue perfusion was measured in seven regions of interest (ROIs) placed in the total hind leg supplied by the femoral artery in seven female pigs. DCE-MRI was performed using a 3D gradient echo sequence with k-space sharing. The sequence was acquired twice, first after LMCA and then after blood pool contrast agent injection. Blood flow was augmented by continuous infusion of the vasodilator adenosine into the femoral artery, resulting in up to four times increased blood flow. The results obtained with several LMCA models were compared with those of a two-compartment blood pool model (2CBPM) consisting of a capillary and an arteriolar compartment. Measurements performed with a Doppler flow probe placed at the femoral artery served as ground truth. RESULTS: The two-compartment exchange model extended by an arteriolar compartment (E2CXM) showed the highest fit quality of all LMCA models and the most significant correlation with the Doppler measurements, r = 0.78 (P < 0.001). The best correspondence between the capillary perfusion measurements of the LMCA models and those of the 2CBPM was found with the E2CXM (slope of the regression line equal to 1, r = 0.85, P < 0.001). The results for the clinical patient data corresponded very well with the results obtained in the animal experiments. CONCLUSIONS: Double-contrast agent DCE-MRI in combination with the E2CXM yields the most reliable results and can be used in clinical routine. Magn Reson Med 79:3154-3162, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Impact of tissue transport on PET hypoxia quantification in pancreatic tumours.

BACKGROUND: The clinical impact of hypoxia in solid tumours is indisputable and yet questions about the sensitivity of hypoxia-PET imaging have impeded its uptake into routine clinical practice. Notably, the binding rate of hypoxia-sensitive PET tracers is slow, comparable to the rate of diffusive equilibration in some tissue types, including mucinous and necrotic tissue. This means that tracer uptake on the scale of a PET imaging voxel-large enough to include such tissue and hypoxic cells-can be as much determined by tissue transport properties as it is by hypoxia. Dynamic PET imaging of 20 patients with pancreatic ductal adenocarcinoma was used to assess the impact of transport on surrogate metrics of hypoxia: the tumour-to-blood ratio [TBR(t)] at time t post-tracer injection and the trapping rate k 3 inferred from a two-tissue compartment model. Transport quantities obtained from this model included the vascular influx and efflux rate coefficients, k 1 and k 2, and the distribution volume v d ≡k 1/(k 2+k 3). RESULTS: Correlations between voxel- and whole tumour-scale k 3 and TBR values were weak to modest: the population average of the Pearson correlation coefficients (r) between voxel-scale k 3 and TBR (1 h) [TBR(2 h)] values was 0.10 [0.01] in the 20 patients, while the correlation between tumour-scale k 3 and TBR(2 h) values was 0.58. Using Patlak's formula to correct uptake for the distribution volume, correlations became strong (r=0.80[0.52] and r=0.93, respectively). The distribution volume was substantially below unity for a large fraction of tumours studied, with v d ranging from 0.68 to 1 (population average, 0.85). Surprisingly, k 3 values were strongly correlated with v d in all patients. A model was proposed to explain this in which k 3 is a combination of the hypoxia-sensitive tracer binding rate k b and the rate k eq of equilibration in slow-equilibrating regions occupying a volume fraction 1-v d of the imaged tissue. This model was used to calculate the proposed hypoxia surrogate marker k b. CONCLUSIONS: Hypoxia-sensitive PET tracers are slow to reach diffusive equilibrium in a substantial fraction of pancreatic tumours, confounding quantification of hypoxia using both static (TBR) and dynamic (k 3) PET imaging. TBR is reduced by distribution volume effects and k 3 is enhanced by slow equilibration. We proposed a novel model to quantify tissue transport properties and hypoxia-sensitive tracer binding in order to improve the sensitivity of hypoxia-PET imaging.

Tracer kinetic modelling for DCE-MRI quantification of subtle blood-brain barrier permeability.

There is evidence that subtle breakdown of the blood-brain barrier (BBB) is a pathophysiological component of several diseases, including cerebral small vessel disease and some dementias. Dynamic contrast-enhanced MRI (DCE-MRI) combined with tracer kinetic modelling is widely used for assessing permeability and perfusion in brain tumours and body tissues where contrast agents readily accumulate in the extracellular space. However, in diseases where leakage is subtle, the optimal approach for measuring BBB integrity is likely to differ since the magnitude and rate of enhancement caused by leakage are extremely low; several methods have been reported in the literature, yielding a wide range of parameters even in healthy subjects. We hypothesised that the Patlak model is a suitable approach for measuring low-level BBB permeability with low temporal resolution and high spatial resolution and brain coverage, and that normal levels of scanner instability would influence permeability measurements. DCE-MRI was performed in a cohort of mild stroke patients (n=201) with a range of cerebral small vessel disease severity. We fitted these data to a set of nested tracer kinetic models, ranking their performance according to the Akaike information criterion. To assess the influence of scanner drift, we scanned 15 healthy volunteers that underwent a "sham" DCE-MRI procedure without administration of contrast agent. Numerical simulations were performed to investigate model validity and the effect of scanner drift. The Patlak model was found to be most appropriate for fitting low-permeability data, and the simulations showed vp and K(Trans) estimates to be reasonably robust to the model assumptions. However, signal drift (measured at approximately 0.1% per minute and comparable to literature reports in other settings) led to systematic errors in calculated tracer kinetic parameters, particularly at low permeabilities. Our findings justify the growing use of the Patlak model in low-permeability states, which has the potential to provide valuable information regarding BBB integrity in a range of diseases. However, absolute values of the resulting tracer kinetic parameters should be interpreted with extreme caution, and the size and influence of signal drift should be measured where possible.