Associations between tumor vascularization assessed by in vivo DCE-MRI and the presence of disseminated tumor cells in bone marrow in breast cancer patients at the time of diagnosis.

PURPOSE: To explore possible associations between in vivo pharmacokinetic dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters and the presence of disseminated tumor cells (DTCs) in bone marrow in breast cancer patients at the time of diagnosis. MATERIALS AND METHODS: Thirty-seven women with breast cancer (stage T2-4N0-1M0) were included. Patients were classified as DTC+ if one or more DTCs were detected by immunocytochemistry. DCE-MRI was acquired with a radial 3D T1 -weighted spoiled gradient echo sequence with k-space weighted image contrast. K(trans), kep, and ve were calculated using the extended Tofts model and a population-derived arterial input function. The nonparametric Mann-Whitney U-test was used to compare the histogram distributions of the pharmacokinetic parameters for the DTC+ and the DTC- patients. RESULTS: DTCs were detected in 7 of the 37 patients (19%). In DTC+ patients, the distribution of tumor K(trans) and kep were significantly (P < 0.01) more shifted towards lower values than in DTC- patients. CONCLUSION: An association between vascular dependent pharmacokinetic DCE-MRI parameters and the presence of DTCs were found. Compared to DTC- patients, DTC+ patients had poorer perfusion and permeability, indicative of hypoxia. Thus, pharmacokinetic parameters might be surrogate biomarkers of metastatic potential and future relapse.

Quantitative analysis of diffusion-weighted magnetic resonance imaging in malignant breast lesions using different b value combinations.

OBJECTIVES: To explore how apparent diffusion coefficients (ADCs) in malignant breast lesions are affected by selection of b values in the monoexponential model and to compare ADCs with diffusion coefficients (Ds) obtained from the biexponential model. METHODS: Twenty-four women (mean age 51.3 years) with locally advanced breast cancer were included in this study. Pre-treatment diffusion-weighted magnetic resonance imaging was performed using a 1.5-T system with b values of 0, 50, 100, 250 and 800 s/mm(2). Thirteen different b value combinations were used to derive individual monoexponential ADC maps. All b values were used in the biexponential model. RESULTS: Median ADC (including all b values) and D were 1.04 × 10(-3) mm(2)/s (range 0.82-1.61 × 10(-3) mm(2)/s) and 0.84 × 10(-3) mm(2)/s (range 0.17-1.56 × 10(-3) mm(2)/s), respectively. There was a strong positive correlation between ADCs and Ds. For clinically relevant b value combinations, maximum deviation between ADCs including and excluding low b values (<100 s/mm(2)) was 11.8 %. CONCLUSION: Selection of b values strongly affects ADCs of malignant breast lesions. However, by excluding low b values, ADCs approach biexponential Ds, demonstrating that microperfusion influences the diffusion signal. Thus, care should be taken when ADC calculation includes low b values.

Preclinical dynamic 18F-FDG PET - tumor characterization and radiotherapy response assessment by kinetic compartment analysis.

BACKGROUND: Non-invasive visualization of tumor biological and molecular processes of importance to diagnosis and treatment response is likely to be critical in individualized cancer therapy. Since conventional static (18)F-FDG PET with calculation of the semi-quantitative parameter standardized uptake value (SUV) may be subject to many sources of variability, we here present an approach of quantifying the (18)F-FDG uptake by analytic two-tissue compartment modeling, extracting kinetic tumor parameters from dynamic (18)F-FDG PET. Further, we evaluate the potential of such parameters in radiotherapy response assessment. MATERIAL AND METHODS: Male, athymic mice with prostate carcinoma xenografts were subjected to dynamic PET either untreated (n=8) or 24 h post-irradiation (7.5 Gy single dose, n=8). After 10 h of fasting, intravenous bolus injections of 10-15 MBq (18)F-FDG were administered and a 1 h dynamic PET scan was performed. 4D emission data were reconstructed using OSEM-MAP, before remote post-processing. Individual arterial input functions were extracted from the image series. Subsequently, tumor (18)F-FDG uptake was fitted voxel-by-voxel to a compartment model, producing kinetic parameter maps. RESULTS: The kinetic model separated the (18)F-FDG uptake into free and bound tracer and quantified three parameters; forward tracer diffusion (k(1)), backward tracer diffusion (k(2)), and rate of (18)F-FDG phosphorylation, i.e. the glucose metabolism (k(3)). The fitted kinetic model gave a goodness of fit (r(2)) to the observed data ranging from 0.91 to 0.99, and produced parametrical images of all tumors included in the study. Untreated tumors showed homogeneous intra-group median values of all three parameters (k(1), k(2) and k(3)), whereas the parameters significantly increased in the tumors irradiated 24 h prior to (18)F-FDG PET. CONCLUSIONS: This study demonstrates the feasibility of a two-tissue compartment kinetic analysis of dynamic (18)F-FDG PET images. If validated, extracted parametrical maps might contribute to tumor biological characterization and radiotherapy response assessment.

Noise dependency in vascular parameters from combined gradient-echo and spin-echo DSC MRI.

Dynamic susceptibility contrast (DSC) imaging is a widely used technique for assessment of cerebral blood volume (CBV). With combined gradient-echo and spin-echo DSC techniques, measures of the underlying vessel size and vessel architecture can be obtained from the vessel size index (VSI) and vortex area, respectively. However, how noise, and specifically the contrast-to-noise ratio (CNR), affect the estimations of these parameters has largely been overlooked. In order to address this issue, we have performed simulations to generate DSC signals with varying levels of CNR, defined by the peak of relaxation rate curve divided by the standard deviation of the baseline. Moreover, DSC data from 59 brain cancer patients were acquired at two different 3 T-scanners (N = 29 and N = 30, respectively), where CNR and relative parameter maps were obtained. Our simulations showed that the measured parameters were affected by CNR in different ways, where low CNR led to overestimations of CBV and underestimations of VSI and vortex area. In addition, a higher noise-sensitivity was found in vortex area than in CBV and VSI. Results from clinical data were consistent with simulations, and indicated that CNR < 4 gives highly unreliable measurements. Moreover, we have shown that the distribution of values in the tumour regions could change considerably when voxels with CNR below a given cut off are excluded when generating the relative parameter maps. The widespread use of CBV and attractive potential of VSI and vortex area, makes the noise-sensitivity of these parameters found in our study relevant for further use and development of the DSC imaging technique. Our results suggest that the CNR has considerable impact on the measured parameters, with the potential to affect the clinical interpretation of DSC-MRI, and should therefore be taken into account in the clinical decision-making process.

Brain metastases with poor vascular function are susceptible to pseudoprogression after stereotactic radiation surgery.

PURPOSE: This study aimed to investigate the hemodynamic status of cerebral metastases prior to and after stereotactic radiation surgery (SRS) and to identify the vascular characteristics that are associated with the development of pseudoprogression from radiation-induced damage with and without a radionecrotic component. METHODS AND MATERIALS: Twenty-four patients with 29 metastases from non-small cell lung cancer or malignant melanoma received SRS with dose of 15 Gy to 25 Gy. Magnetic resonance imaging (MRI) scans were acquired prior to SRS, every 3 months during the first year after SRS, and every 6 months thereafter. On the basis of the follow-up MRI scans or histology after SRS, metastases were classified as having response, tumor progression, or pseudoprogression. Advanced perfusion MRI enabled the estimation of vascular status in tumor regions including fractions of abnormal vessel architecture, underperfused tissue, and vessel pruning. RESULTS: Prior to SRS, metastases that later developed pseudoprogression had a distinct poor vascular function in the peritumoral zone compared with responding metastases (P < .05; number of metastases = 15). In addition, differences were found between the peritumoral zone of pseudoprogressing metastases and normal-appearing brain tissue (P < .05). In contrast, for responding metastases, no differences in vascular status between peritumoral and normal-appearing brain tissue were observed. The dysfunctional peritumoral vasculature persisted in pseudoprogressing metastases after SRS. CONCLUSIONS: Our results suggest that the vascular status of peritumoral tissue prior to SRS plays a defining role in the development of pseudoprogression and that advanced perfusion MRI may provide new insights into patients' susceptibility to radiation-induced effects.

Dynamic (18) F-FDG PET for Assessment of Tumor Physiology in Two Breast Carcinoma Xenografts.

PURPOSE: To compare dynamic 2-deoxy-2-[(18) F]fluoro-D-glucose positron emission tomography ((18) F-FDG PET) parameters in two selected human breast cancer xenografts and to evaluate associations with immunohistochemistry and histology. PROCEDURES: Dynamic (18) F-FDG PET of luminal-like MAS98.06 and basal-like MAS98.12 xenografts was performed, and the compartmental transfer rates (k 1 ,k 2 ,k 3 ), blood volume fraction (v B ) and metabolic rate of (18) F-FDG(MR FDG ) were estimated from pharmacokinetic model analysis. After sacrifice, analyses of hypoxia (pimonidazole), proliferation (Ki-67), vascularization (CD31), glucose transport receptor (GLUT1) and necrosis (HE) was performed. The level of hexokinase 2 (HK2) was estimated from Western blot analysis. RESULTS: The (18) F-FDG uptake curves for the two xenografts were significantly different (p < 0.05). k 1 and v B were higher for MAS98.12 (p < 0.01), while k 3 was higher for MAS98.06 (p < 0.01). MAS98.12 had a higher fraction of stromal tissue and higher microvessel density (MVD), and it was less necrotic and hypoxic than MAS98.06. MAS98.12 had stronger positive GLUT1 staining and lower Ki-67 than MAS98.06. In both models significant correlations were found between k 1 and the GLUT1 score, between k 3 and the level of HK2, and between v B and MVD. CONCLUSIONS: Significant differences in dynamic (18) F-FDG parameters between the two human breast cancer xenografts were found. The differences could be explained by underlying histological and physiological characteristics.