Population-based Arterial Input Function Research Articles

Quantitative analysis from ultrafast dynamic contrast-enhanced breast MRI using population-based versus individual arterial input functions, and comparison with semi-quantitative analysis

PurposeTo evaluate the value of inline quantitative analysis of ultrafast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) using a population-based arterial input function (P-AIF) compared with offline quantitative analysis with an individual AIF (I-AIF) and semi-quantitative analysis for diagnosing breast cancer. MethodsThis prospective study included 99 consecutive patients with 109 lesions (85 malignant and 24 benign). Model-based parameters (Ktrans, kep, and ve) and model-free parameters (washin and washout) were derived from CAIPIRINHA-Dixon-TWIST-VIBE (CDTV) DCE-MRI. Univariate analysis and multivariate logistic regression analysis with forward stepwise covariate selection were performed to identify significant variables. The AUC and F1 score were assessed for semi-quantitative and two quantitative analyses. Resultskep from inline quantitative analysis with P-AIF for diagnosing breast cancer provided an AUC similar to kep from offline quantitative analysis with I-AIF (0.782 vs 0.779, p = 0.954), higher compared to washin from semi-quantitative analysis (0.782 vs 0.630, p = 0.034). Furthermore, the inline quantitative analysis with P-AIF achieved the larger F1 score (0.920) compared with offline quantitative analysis with I-AIF (0.780) and semi-quantitative analysis (0.480). There were no statistically significant differences for kep values between the two quantitative analysis schemes (p = 0.944). ConclusionThe inline quantitative analysis with P-AIF from CDTV in characterizing breast lesions could offer similar diagnostic accuracy to offline quantitative analysis with I-AIF, and higher diagnostic accuracy to semi-quantitative analysis.

Short-time-window Patlak imaging using a population-based arterial input function and optimized Bayesian penalized likelihood reconstruction: a feasibility study

BackgroundTo explore the feasibility of short-time-window Ki imaging using a population-based arterial input function (IF) and optimized Bayesian penalized likelihood (BPL) reconstruction as a practical alternative to long-time-window Ki imaging with an individual patient-based IF. Myocardial Ki images were generated from 73 dynamic 18F-FDG-PET/CT scans of 30 patients with cardiac sarcoidosis. For each dynamic scan, the Ki images were obtained using the IF from each individual patient and a long time window (10–60 min). In addition, Ki images were obtained using the normalized averaged population-based IF and BPL algorithms with different beta values (350, 700, and 1000) with a short time window (40–60 min). The visual quality of each image was visually rated using a 4-point scale (0, not visible; 1, poor; 2, moderate; and 3, good), and the Ki parameters (Ki-max, Ki-mean, Ki-volume) of positive myocardial lesions were measured independently by two readers. Wilcoxon’s rank sum test, McNemar’s test, or linear regression analysis were performed to assess the differences or relationships between two quantitative variables.ResultsBoth readers similarly rated 51 scans as positive (scores = 1–3) and 22 scans as negative (score = 0) for all four Ki images. Among the three types of population-based IF Ki images, the proportion of images with scores of 3 was highest with a beta of 1000 (78.4 and 72.5%, respectively) and lowest with a beta of 350 (33.3 and 23.5%) for both readers (all p < 0.001). The coefficients of determination between the Ki parameters obtained with the individual patient-based IF and those obtained with the population-based IF were highest with a beta of 1000 for both readers (Ki-max, 0.91 and 0.92, respectively; Ki-mean, 0.91 and 0.92, respectively; Ki-volume, 0.75 and 0.60, respectively; and all p < 0.001).ConclusionsShort-time-window Ki images with a population-based IF reconstructed using the BPL algorithm and a high beta value were closely correlated with long-time-window Ki images generated with an individual patient-based IF. Short-time-window Ki images using a population-based IF and BPL reconstruction might represent practical alternatives to long-time-window Ki images generated using an individual patient-based IF.

Arterial input function for quantitative dynamic contrast-enhanced MRI to diagnose prostate cancer.

PURPOSE This study aims to analyze the ability of quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to distinguish between prostate cancer (PCa) and benign lesions in transition zone (TZ) and peripheral zone (PZ) using different methods for arterial input function (AIF) determination. Study endpoints are identification of a standard AIF method and optimal quantitative perfusion parameters for PCa detection. METHODS DCE image data of 50 consecutive patients with PCa who underwent multiparametric MRI were analyzed retrospectively with three different methods of AIF acquisition. First, a region of interest was manually defined in an artery (AIFm); second, an automated algorithm was used (AIFa); and third, a population-based AIF (AIFp) was applied. Values of quantitative parameters after Tofts (Ktrans, ve, and kep) in PCa, PZ, and TZ in the three different AIFs were analyzed. RESULTS Ktrans and kep were significantly higher in PCa than in benign tissue independent from the AIF method. Whereas in PZ, Ktrans and kep could differentiate PCa (P < .001), in TZ only kep using AIFpdemonstrated a significant difference (P = .039). The correlations of the perfusion parameters that resulted from AIFm and AIFa were higher than those that resulted from AIFp, and the absolute values of Ktrans, kep, and ve were significantly lower when using AIFp. The values of quantitative perfusion parameters for PCa were similar regardless of whether PCa was located in PZ or TZ. CONCLUSION Ktrans and kep were able to differentiate PCa from benign PZ independent of the AIF method. AIFaseems to be the most feasible method of AIF determination in clinical routine. For TZ, none of the quantitative perfusion parameters provided satisfying results.

DCE-MRI of Tumor Hypoxia and Hypoxia-Associated Aggressiveness.

Tumor hypoxia is associated with resistance to treatment, aggressive growth, metastatic dissemination, and poor clinical outcome in many cancer types. The potential of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to assess the extent of hypoxia in tumors has been investigated in several studies in our laboratory. Cervical carcinoma, melanoma, and pancreatic ductal adenocarcinoma (PDAC) xenografts have been used as models of human cancer, and the transfer rate constant (Ktrans) and the extravascular extracellular volume fraction (ve) have been derived from DCE-MRI data by using Tofts standard pharmacokinetic model and a population-based arterial input function. Ktrans was found to reflect naturally occurring and treatment-induced hypoxia when hypoxia was caused by low blood perfusion, radiation responsiveness when radiation resistance was due to hypoxia, and metastatic potential when metastasis was hypoxia-induced. Ktrans was also associated with outcome for patients with locally-advanced cervical carcinoma treated with cisplatin-based chemoradiotherapy. Together, the studies imply that DCE-MRI can provide valuable information on the hypoxic status of cervical carcinoma, melanoma, and PDAC. In this communication, we review and discuss the studies and provide some recommendations as to how DCE-MRI data can be analyzed and interpreted to assess tumor hypoxia.

Evaluating the Relationship Between Dynamic Contrast-Enhanced MRI (DCE-MRI) Parameters and Pathological Characteristics in Breast Cancer.

Dynamic contrast-enhanced MRI (DCE-MRI) is used to evaluate tumor microvasculature. However, studies demonstrating an association between perfusion parameters derived from DCE-MRI and histopathologic characteristics are limited to a small set of histopathologic factors, and the results are inconsistent. To evaluate the relationship between DCE-MRI perfusion parameters and common histopathologic tumor characteristics used to predict angiogenesis and determine prognosis in breast cancer. Retrospective. In all, 105 breast cancer patients with invasive ductal carcinoma (122 lesions). 3.0T, turbo spin-echo (TSE) T1 -weighted, fat-suppressed T2 -weighted, TSE T2 -weighted, and dynamic unenhanced and contrast-enhanced 3D T1 high-resolution isotropic volume examination. One reviewer obtained perfusion parameters (Ktrans , kep , ve , and vp ) of each breast cancer from DCE MRI using the extended Tofts model with a fixed baseline T1 value and a population-based arterial input function. The relationship between DCE-MRI perfusion parameters and histopathologic tumor characteristics used to predict angiogenesis and determine prognosis was evaluated. Student's t-test, Mann-Whitney U-test, analysis of variance (ANOVA), and Kruskal-Wallis test were used. Triple-negative breast cancers exhibited higher Ktrans and kep than luminal cancers (P < 0.05). Estrogen receptor (ER)-negative tumors showed higher Ktrans than ER-positive tumors (P < 0.05). Progesterone receptor (PR)-negative tumors presented higher ve than PR-positive tumors (P < 0.05). Tumors with higher Ki-67 showed higher kep than tumors with lower Ki-67 (P < 0.05). P53-positive tumors exhibited higher Ktrans and kep than p53-negative tumors (P < 0.05). Higher histologic grade tumors (grade II/III) presented higher Ktrans , kep , vp (P < 0.05) than grade I tumors. Tumors with LVSI presented higher Ktrans and kep than tumors without LVSI (P < 0.05). Breast cancer presenting higher Ktrans and kep on DCE-MRI was associated with poor prognostic histopathologic factors. Therefore, pretreatment DCE-MRI perfusion parameters may be useful imaging biomarkers for the evaluation of tumor prognosis and angiogenesis. 3 TECHNICAL EFFICACY STAGE: 2.

Repeatability of arterial input functions and kinetic parameters in muscle obtained by dynamic contrast enhanced MR imaging of the head and neck

BackgroundQuantification of pharmacokinetic parameters in dynamic contrast enhanced (DCE) MRI is heavily dependent on the arterial input function (AIF). In the present patient study on advanced stage head and neck squamous cell carcinoma (HNSCC) we have acquired DCE-MR images before and during chemo radiotherapy. We determined the repeatability of image-derived AIFs and of the obtained kinetic parameters in muscle and compared the repeatability of muscle kinetic parameters obtained with image-derived AIF's versus a population-based AIF. Materials and methodsWe compared image-derived AIFs obtained from the internal carotid, external carotid and vertebral arteries. Pharmacokinetic parameters (ve, Ktrans, kep) in muscle—located outside the radiation area—were obtained using the Tofts model with the image-derived AIFs and a population averaged AIF. Parameter values and repeatability were compared. Repeatability was calculated with the pre- and post-treatment data with the assumption of no DCE-MRI measurable biological changes between the scans. ResultsSeveral parameters describing magnitude and shape of the image-derived AIFs from the different arteries in the head and neck were significantly different. Use of image-derived AIFs led to higher pharmacokinetic parameters compared to use of a population averaged AIF. Median muscle pharmacokinetic parameters values obtained with AIFs in external carotids, internal carotids, vertebral arteries and with a population averaged AIF were respectively: ve (0.65, 0.74, 0.58, 0.32), Ktrans (0.30, 0.21, 0.13, 0.06), kep (0.41, 0.32, 0.24, 0.18). Repeatability of pharmacokinetic parameters was highest when a population averaged AIF was used; however, this repeatability was not significantly different from image-derived AIFs. ConclusionImage-derived AIFs in the neck region showed significant variations in the AIFs obtained from different arteries, and did not improve repeatability of the resulting pharmacokinetic parameters compared with the use of a population averaged AIF. Therefore, use of a population averaged AIF seems to be preferable for pharmacokinetic analysis using DCE-MRI in the head and neck area.

Dynamic contrast-enhanced magnetic resonance imaging may act as a biomarker for vascular damage in normal appearing brain tissue after radiotherapy in patients with glioblastoma.

BackgroundDynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a promising perfusion method and may be useful in evaluating radiation-induced changes in normal-appearing brain tissue.PurposeTo assess whether radiotherapy induces changes in vascular permeability (Ktrans) and the fractional volume of the extravascular extracellular space (Ve) derived from DCE-MRI in normal-appearing brain tissue and possible relationships to radiation dose given.Material and MethodsSeventeen patients with glioblastoma treated with radiotherapy and chemotherapy were included; five were excluded because of inconsistencies in the radiotherapy protocol or early drop-out. DCE-MRI, contrast-enhanced three-dimensional (3D) T1-weighted (T1W) images and T2-weighted fluid attenuated inversion recovery (T2-FLAIR) images were acquired before and on average 3.3, 30.6, 101.6, and 185.7 days after radiotherapy. Pre-radiotherapy CE T1W and T2-FLAIR images were segmented into white and gray matter, excluding all non-healthy tissue. Ktrans and Ve were calculated using the extended Kety model with the Parker population-based arterial input function. Six radiation dose regions were created for each tissue type, based on each patient’s computed tomography-based dose plan. Mean Ktrans and Ve were calculated over each dose region and tissue type.ResultsGlobal Ktrans and Ve demonstrated mostly non-significant changes with mean values higher for post-radiotherapy examinations in both gray and white matter compared to pre-radiotherapy. No relationship to radiation dose was found.ConclusionAdditional studies are needed to validate if Ktrans and Ve derived from DCE-MRI may act as potential biomarkers for acute and early-delayed radiation-induced vascular damages. No dose-response relationship was found.

Modification of population based arterial input function to incorporate individual variation

This technical note describes how to modify a population-based arterial input function to incorporate variation among the individuals. In DCE-MRI, an arterial input function (AIF) is often distorted by pulsated inflow effect and noise. A population-based AIF (pAIF) has high signal-to-noise ratio (SNR), but cannot incorporate the individual variation. AIF variation is mainly induced by variation in cardiac output and blood volume of the individuals, which can be detected by the full width at half maximum (FWHM) during the first passage and the amplitude of AIF, respectively. Thus pAIF scaled in time and amplitude fitting to the individual AIF may serve as a high SNR AIF incorporating the individual variation. The proposed method was validated using DCE-MRI images of 18 prostate cancer patients. Root mean square error (RMSE) of pAIF from individual AIFs was 0.88±0.48mM (mean±SD), but it was reduced to 0.25±0.11mM after pAIF modification using the proposed method (p<0.0001).

Dynamic Contrast-enhanced MR Imaging in Renal Cell Carcinoma: Reproducibility of Histogram Analysis on Pharmacokinetic Parameters

Pharmacokinetic parameters derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) have been increasingly used to evaluate the permeability of tumor vessel. Histogram metrics are a recognized promising method of quantitative MR imaging that has been recently introduced in analysis of DCE-MRI pharmacokinetic parameters in oncology due to tumor heterogeneity. In this study, 21 patients with renal cell carcinoma (RCC) underwent paired DCE-MRI studies on a 3.0 T MR system. Extended Tofts model and population-based arterial input function were used to calculate kinetic parameters of RCC tumors. Mean value and histogram metrics (Mode, Skewness and Kurtosis) of each pharmacokinetic parameter were generated automatically using ImageJ software. Intra- and inter-observer reproducibility and scan–rescan reproducibility were evaluated using intra-class correlation coefficients (ICCs) and coefficient of variation (CoV). Our results demonstrated that the histogram method (Mode, Skewness and Kurtosis) was not superior to the conventional Mean value method in reproducibility evaluation on DCE-MRI pharmacokinetic parameters (K trans & Ve) in renal cell carcinoma, especially for Skewness and Kurtosis which showed lower intra-, inter-observer and scan-rescan reproducibility than Mean value. Our findings suggest that additional studies are necessary before wide incorporation of histogram metrics in quantitative analysis of DCE-MRI pharmacokinetic parameters.

Reproducibility of Dynamic Contrast-Enhanced MRI in Renal Cell Carcinoma: A Prospective Analysis on Intra- and Interobserver and Scan-Rescan Performance of Pharmacokinetic Parameters.

The objective of this study was to investigate the intra- and interobserver as well as scan-rescan reproducibility of quantitative parameters of renal cell carcinomas (RCCs) with dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). A total of 21 patients with clear cell RCCs (17 men, 4 woman; age 37-69 years, mean age 54.6 years, mean size, 5.0 ± 2.2 cm) were prospectively recruited from September 2012 to November 2012. Patients underwent paired DCE-MRI studies on a 3.0 T MR system with an interval of 48 to 72 hours. The extended-Tofts model and population-based arterial input function were used to calculate kinetic parameters. Three observers defined the 2-dimensional whole-tumor region of interest at the slice with the maximum diameter of the RCC. Intraobserver and scan-rescan differences were assessed using paired t tests, whereas interobserver differences using two-way analysis of variance. Intra- and interobserver reproducibility and scan-rescan reproducibility were evaluated using within-subject coefficient of variation (wCoV) and intraclass correlation coefficient (ICC). There were no significant intra-, interobserver, or scan-rescan differences in parameters (all P > 0.05). All ICCs for intra- and interobserver agreements were >0.75 (P < 0.05), whereas the scan-rescan agreement was moderate to good; V(e) (0.764, 95% confidence interval [CI]: 0.378-0.925) and K(ep) (0.906, 95% CI: 0.710-0.972) had higher ICC than K(trans) (0.686; 95% CI: 0.212-0.898) and V(p) (0.657; 95% CI: 0.164-0.888). In intra- and interobserver variability analyses, all parameters except V(p) had low wCoV values. K(trans) and V(e) had slightly lower intraobserver wCoV (1.2% and 0.9%) compared with K(ep) (3.7%), whereas all 3 of these parameters had similar interobserver wCoV values (2.5%, 3.1%, and 2.9%, respectively). Regarding scan-rescan variability, K(trans) and K(ep) showed slightly higher variation (15.6% and 15.4%) than V(e) (10.1%). V(p) had the largest wCoV in all variability analyses (all >30%). DCE-MRI demonstrated good intra- and interobserver reproducibility and moderate to good scan-rescan performance in the assessment of RCC using K(trans), K(ep), and V(e) as parameters under noncontinuous scanning mode. V(p) showed poor reproducibility, and thus may not be suitable for this scanning protocol.

SU-D-303-02: Impact of Arterial Input Function Selection and T10 Correction On DCE-MRI Tumour Response Prediction Using Compared to Volumetric DCE CT

Purpose: To evaluate the impact of individualized magnitude and phase signal arterial input function (AIF) measurements as well as voxel-based pre-contrast T10 relaxation on tumour perfusion metrics from DCE-MRI compared to DCE-CT using a common 4D temporal dynamic analysis (TDA) method. Methods: Nine patients with 13 brain metastases underwent volumetric DCE-CT (Toshiba, Aquilion ONE) and DCE-MRI (IMRIS 3T Verio) at baseline then 7 and 21 days post-radiosurgery. Voxel-based whole brain TDA was performed on all data using in-house software producing kinetic parameters AUC, Ktrans, Kep, and Vb (using the Modified Tofts model). AIF susceptibility was investigated in DCE-CT by selecting the AIF (from internal carotid artery) or VIF (from Sagittal Sinus). In DCE-MRI an individual Magnitude or Phase-based AIF (Sagittal Sinus) was compared to population-based AIF together with susceptibility to voxel-based T10 maps instead of a constant of 2400 msec. Absolute DCE-MRI values were compared to DCE-CT by Pearson correlation. Results: No significant difference in median Ktrans (0.048 +/−0.03 s-1) or AUC (2785.5 +/−1143.6 HU.s) was found between individual AIF and VIF-based DCE CT analyses. Using individual Magnitude VIF or Phase-based AIF for DCE-MRI (T10 2400 ms) resulted in higher Ktrans values (0.181 +/−0.11 vs. 0.121 +/−0.099 s-1). This is likely resulting from the smaller AIF peak since the population AIF (which more closely resembles CT) correlates better to DCE-CT metrics. Using voxel-based T10 maps caused further statistically-significant increase in Ktrans and AUC (p<0.0006) that could be contributed to a lower median T10 value (1572 +/−594, n=41). Conclusion: This preliminary data highlights the stability of DCE-CT calculations as well as susceptibility of DCE-MRI Ktrans measurements to various imaging factors, including AIF selection and T10 values used for modeling. Efforts to improve voxel-based T10 map calculations are being explored to further explain discrepancies between analysis methods. Brain Tumor Foundation of Canada and Ontario Institute of Cancer Research

Automated reference region extraction and population-based input function for brain [(11)C]TMSX PET image analyses.

[(11)C]TMSX ([7-N-methyl-(11)C]-(E)-8-(3,4,5-trimethoxystyryl)-1,3,7-trimethylxanthine) is a selective adenosine A2A receptor (A2AR) radioligand. In the central nervous system (CNS), A2AR are linked to dopamine D2 receptor function in striatum, but they are also important modulators of inflammation. The golden standard for kinetic modeling of brain [(11)C]TMSX positron emission tomography (PET) is to obtain arterial input function via arterial blood sampling. However, this method is laborious, prone to errors and unpleasant for study subjects. The aim of this work was to evaluate alternative input function acquisition methods for brain [(11)C]TMSX PET imaging. First, a noninvasive, automated method for the extraction of gray matter reference region using supervised clustering (SCgm) was developed. Second, a method for obtaining a population-based arterial input function (PBIF) was implemented. These methods were created using data from 28 study subjects (7 healthy controls, 12 multiple sclerosis patients, and 9 patients with Parkinson's disease). The results with PBIF correlated well with original plasma input, and the SCgm yielded similar results compared with cerebellum as a reference region. The clustering method for extracting reference region and the population-based approach for acquiring input for dynamic [(11)C]TMSX brain PET image analyses appear to be feasible and robust methods, that can be applied in patients with CNS pathology.

A novel AIF tracking method and comparison of DCE-MRI parameters using individual and population-based AIFs in human breast cancer

Quantitative analysis of dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) data requires the accurate determination of the arterial input function (AIF). A novel method for obtaining the AIF is presented here and pharmacokinetic parameters derived from individual and population-based AIFs are then compared. A Philips 3.0 T Achieva MR scanner was used to obtain 20 DCE-MRI data sets from ten breast cancer patients prior to and after one cycle of chemotherapy. Using a semi-automated method to estimate the AIF from the axillary artery, we obtain the AIF for each patient, AIFind, and compute a population-averaged AIF, AIFpop. The extended standard model is used to estimate the physiological parameters using the two types of AIFs. The mean concordance correlation coefficient (CCC) for the AIFs segmented manually and by the proposed AIF tracking approach is 0.96, indicating accurate and automatic tracking of an AIF in DCE-MRI data of the breast is possible. Regarding the kinetic parameters, the CCC values for Ktrans, vp and ve as estimated by AIFind and AIFpop are 0.65, 0.74 and 0.31, respectively, based on the region of interest analysis. The average CCC values for the voxel-by-voxel analysis are 0.76, 0.84 and 0.68 for Ktrans, vp and ve, respectively. This work indicates that Ktrans and vp show good agreement between AIFpop and AIFind while there is a weak agreement on ve.

Abstract P2-02-07: Predicting Response to Bevacizumab Therapy in Primary Breast Cancer Using Dynamic Contrast-Enhanced Magnetic Resonance Imaging

Abstract Background: Anti-angiogenic therapy holds much promise for the treatment of breast cancer. In practice however, only a subset of patients who receive these drugs demonstrate a significant response to therapy. A key challenge therefore is to elucidate markers that are predictive of response to anti-angiogenic agents such as bevacizumab, and which would enable the selection of patients who would get the most benefit from these expensive therapies. Materials and Methods: We used high temporal resolution dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to assess tumor vascularity in 20 patients with primary breast cancer. Patients were imaged both before and two weeks after single dose Bevacizumab therapy (15mg/kg). Pharmacokinetic modelling techniques were used to quantify the volume transfer constant Ktrans, the rate constant kep, and the fractional volume of the extra-vascular extracellular space ve. Specifically, we used Tofts model with a population-based arterial input function (modified Fritz-Hansen) to model the contrast agent concentration time courses on a voxel-wise basis. Non-enhancing voxels were detected automatically with the use of a Bayesian noise model, and the corresponding pharmacokinetic parameter values for these voxels were set to zero. The median pharmacokinetic parameter values over the tumor volumes of interest were then computed both pre-and post Bevacizumab. Results: We found marked variation across patients in the baseline level and percentage change in median Ktrans, kep and ve following Bevacizumab. In particular, median Ktrans at baseline ranged form 0.12 to 0.88. Changes in median Ktrans varied from −97% to +19% across all patients, with an average change of −49%. Notably, we found a highly significant negative correlation (r = −0.92, P = 1e-08) between the absolute change in median Ktrans and the median Ktrans at baseline. In particular, tumors with a high median Ktrans at baseline demonstrated the greatest change in Ktrans following Bevacizumab therapy, whereas tumors with low median Ktrans at baseline demonstrated relatively little change in Ktrans. Discussion: Although Ktrans is a complex function of vessel permeability, surface area, and tumor blood flow, it has previously been demonstrated to be a reliable biomarker of response to anti-angiogenic therapy in a number of different cancers. Our results illustrate that therapy-induced changes in Ktrans can be predicted from the value of Ktrans at baseline, and hence DCE-MRI scans may enable the selection of primary breast cancer patients who show the greatest response to single-dose Bevacizumab therapy. Whether this will translate into longer term benefit and improvements in outcome for patients remains to be shown. The relationship between baseline and pre-/post-therapy change in Ktrans with the corresponding changes in gene expression is currently under study in a larger number of patients. Figure available in online version. Citation Information: Cancer Res 2010;70(24 Suppl):Abstract nr P2-02-07.