Quantitative DCE-MRI prediction of breast cancer recurrence following neoadjuvant chemotherapy: a preliminary study.

INTRODUCTION: Breast cancer patients treated with neoadjuvant chemotherapy (NACT) are at risk of recurrence depending on clinicopathological characteristics. This preliminary study aimed to investigate the predictive performances of quantitative dynamic contrast-enhanced (DCE) MRI parameters, alone and in combination with clinicopathological variables, for prediction of recurrence in patients treated with NACT. METHODS: Forty-seven patients underwent pre- and post-NACT MRI exams including high spatiotemporal resolution DCE-MRI. The Shutter-Speed model was employed to perform pharmacokinetic analysis of the DCE-MRI data and estimate the Ktrans, ve, kep, and τi parameters. Univariable logistic regression was used to assess predictive accuracy for recurrence for each MRI metric, while Firth logistic regression was used to evaluate predictive performances for models with multi-clinicopathological variables and in combination with a single MRI metric or the first principal components of all MRI metrics. RESULTS: Pre- and post-NACT DCE-MRI parameters performed better than tumor size measurement in prediction of recurrence, whether alone or in combination with clinicopathological variables. Combining post-NACT Ktrans with residual cancer burden and age showed the best improvement in predictive performance with ROC AUC = 0.965. CONCLUSION: Accurate prediction of recurrence pre- and/or post-NACT through integration of imaging markers and clinicopathological variables may help improve clinical decision making in adjusting NACT and/or adjuvant treatment regimens to reduce the risk of recurrence and improve survival outcome.

Accuracy, repeatability, and interplatform reproducibility of T1 quantification methods used for DCE-MRI: Results from a multicenter phantom study.

PURPOSE: To determine the in vitro accuracy, test-retest repeatability, and interplatform reproducibility of T1 quantification protocols used for dynamic contrast-enhanced MRI at 1.5 and 3 T. METHODS: A T1 phantom with 14 samples was imaged at eight centers with a common inversion-recovery spin-echo (IR-SE) protocol and a variable flip angle (VFA) protocol using seven flip angles, as well as site-specific protocols (VFA with different flip angles, variable repetition time, proton density, and Look-Locker inversion recovery). Factors influencing the accuracy (deviation from reference NMR T1 measurements) and repeatability were assessed using general linear mixed models. Interplatform reproducibility was assessed using coefficients of variation. RESULTS: For the common IR-SE protocol, accuracy (median error across platforms = 1.4-5.5%) was influenced predominantly by T1 sample (P < 10-6 ), whereas test-retest repeatability (median error = 0.2-8.3%) was influenced by the scanner (P < 10-6 ). For the common VFA protocol, accuracy (median error = 5.7-32.2%) was influenced by field strength (P = 0.006), whereas repeatability (median error = 0.7-25.8%) was influenced by the scanner (P < 0.0001). Interplatform reproducibility with the common VFA was lower at 3 T than 1.5 T (P = 0.004), and lower than that of the common IR-SE protocol (coefficient of variation 1.5T: VFA/IR-SE = 11.13%/8.21%, P = 0.028; 3 T: VFA/IR-SE = 22.87%/5.46%, P = 0.001). Among the site-specific protocols, Look-Locker inversion recovery and VFA (2-3 flip angles) protocols showed the best accuracy and repeatability (errors < 15%). CONCLUSIONS: The VFA protocols with 2 to 3 flip angles optimized for different applications achieved acceptable balance of extensive spatial coverage, accuracy, and repeatability in T1 quantification (errors < 15%). Further optimization in terms of flip-angle choice for each tissue application, and the use of B1 correction, are needed to improve the robustness of VFA protocols for T1 mapping. Magn Reson Med 79:2564-2575, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Demonstration of nonlinearity bias in the measurement of the apparent diffusion coefficient in multicenter trials.

PURPOSE: Characterize system-specific bias across common magnetic resonance imaging (MRI) platforms for quantitative diffusion measurements in multicenter trials. METHODS: Diffusion weighted imaging (DWI) was performed on an ice-water phantom along the superior-inferior (SI) and right-left (RL) orientations spanning ± 150 mm. The same scanning protocol was implemented on 14 MRI systems at seven imaging centers. The bias was estimated as a deviation of measured from known apparent diffusion coefficient (ADC) along individual DWI directions. The relative contributions of gradient nonlinearity, shim errors, imaging gradients, and eddy currents were assessed independently. The observed bias errors were compared with numerical models. RESULTS: The measured systematic ADC errors scaled quadratically with offset from isocenter, and ranged between -55% (SI) and 25% (RL). Nonlinearity bias was dependent on system design and diffusion gradient direction. Consistent with numerical models, minor ADC errors (± 5%) due to shim, imaging and eddy currents were mitigated by double echo DWI and image coregistration of individual gradient directions. CONCLUSION: The analysis confirms gradient nonlinearity as a major source of spatial DW bias and variability in off-center ADC measurements across MRI platforms, with minor contributions from shim, imaging gradients and eddy currents. The developed protocol enables empiric description of systematic bias in multicenter quantitative DWI studies.

Using dynamic contrast-enhanced MRI to quantitatively characterize maternal vascular organization in the primate placenta.

PURPOSE: The maternal microvasculature of the primate placenta is organized into 10-20 perfusion domains that are functionally optimized to facilitate nutrient exchange to support fetal growth. This study describes a dynamic contrast-enhanced magnetic resonance imaging method for identifying vascular domains and quantifying maternal blood flow in them. METHODS: A rhesus macaque on the 133rd day of pregnancy (G133, term = 165 days) underwent Doppler ultrasound procedures, dynamic contrast-enhanced magnetic resonance imaging and Cesarean-section delivery. Serial T1 -weighted images acquired throughout intravenous injection of a contrast reagent bolus were analyzed to obtain contrast reagent arrival time maps of the placenta. RESULTS: Watershed segmentation of the arrival time map identified 16 perfusion domains. The number and location of these domains corresponded to anatomical cotyledonary units observed following delivery. Analysis of the contrast reagent wave front through each perfusion domain enabled determination of volumetric flow, which ranged from 9.03 to 44.9 mL/s (25.2 ± 10.3 mL/s). These estimates are supported by Doppler ultrasound results. CONCLUSIONS: The dynamic contrast-enhanced magnetic resonance imaging analysis described here provides quantitative estimates of the number of maternal perfusion domains in a primate placenta and estimates flow within each domain. Anticipated extensions of this technique are to the study placental function in non-human primate models of obstetric complications.

Discrimination of Malignant and Benign Breast Lesions Using Quantitative Multiparametric MRI: A Preliminary Study.

We aimed to compare diagnostic performance in discriminating malignant and benign breast lesions between two intravoxel incoherent motion (IVIM) analysis methods for diffusion-weighted magnetic resonance imaging (DW-MRI) data and between DW- and dynamic contrast-enhanced (DCE)-MRI, and to determine if combining DW- and DCE-MRI further improves diagnostic accuracy. DW-MRI with 12 b-values and DCE-MRI were performed on 26 patients with 28 suspicious breast lesions before biopsies. The traditional biexponential fitting and a 3-b-value method were used for independent IVIM analysis of the DW-MRI data. Simulations were performed to evaluate errors in IVIM parameter estimations by the two methods across a range of signal-to-noise ratio (SNR). Pharmacokinetic modeling of DCE-MRI data was performed. Conventional radiological MRI reading yielded 86% sensitivity and 21% specificity in breast cancer diagnosis. At the same sensitivity, specificity of individual DCE- and DW-MRI markers improved to 36%-57% and that of combined DCE- or combined DW-MRI markers to 57%-71%, with DCE-MRI markers showing better diagnostic performance. The combination of DCE- and DW-MRI markers further improved specificity to 86%-93% and the improvements in diagnostic accuracy were statistically significant (P < .05) when compared with standard clinical MRI reading and most individual markers. At low breast DW-MRI SNR values (<50), like those typically seen in clinical studies, the 3-b-value approach for IVIM analysis generates markers with smaller errors and with comparable or better diagnostic performances compared with biexponential fitting. This suggests that the 3-b-value method could be an optimal IVIM-MRI method to be combined with DCE-MRI for improved diagnostic accuracy.

Predicting breast cancer response to neoadjuvant treatment using multi-feature MRI: results from the I-SPY 2 TRIAL.

Dynamic contrast-enhanced (DCE) MRI provides both morphological and functional information regarding breast tumor response to neoadjuvant chemotherapy (NAC). The purpose of this retrospective study is to test if prediction models combining multiple MRI features outperform models with single features. Four features were quantitatively calculated in each MRI exam: functional tumor volume, longest diameter, sphericity, and contralateral background parenchymal enhancement. Logistic regression analysis was used to study the relationship between MRI variables and pathologic complete response (pCR). Predictive performance was estimated using the area under the receiver operating characteristic curve (AUC). The full cohort was stratified by hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) status (positive or negative). A total of 384 patients (median age: 49 y/o) were included. Results showed analysis with combined features achieved higher AUCs than analysis with any feature alone. AUCs estimated for the combined versus highest AUCs among single features were 0.81 (95% confidence interval [CI]: 0.76, 0.86) versus 0.79 (95% CI: 0.73, 0.85) in the full cohort, 0.83 (95% CI: 0.77, 0.92) versus 0.73 (95% CI: 0.61, 0.84) in HR-positive/HER2-negative, 0.88 (95% CI: 0.79, 0.97) versus 0.78 (95% CI: 0.63, 0.89) in HR-positive/HER2-positive, 0.83 (95% CI not available) versus 0.75 (95% CI: 0.46, 0.81) in HR-negative/HER2-positive, and 0.82 (95% CI: 0.74, 0.91) versus 0.75 (95% CI: 0.64, 0.83) in triple negatives. Multi-feature MRI analysis improved pCR prediction over analysis of any individual feature that we examined. Additionally, the improvements in prediction were more notable when analysis was conducted according to cancer subtype.

Early Prediction of Breast Cancer Therapy Response using Multiresolution Fractal Analysis of DCE-MRI Parametric Maps.

We aimed to determine whether multiresolution fractal analysis of voxel-based dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parametric maps can provide early prediction of breast cancer response to neoadjuvant chemotherapy (NACT). In total, 55 patients underwent 4 DCE-MRI examinations before, during, and after NACT. The shutter-speed model was used to analyze the DCE-MRI data and generate parametric maps within the tumor region of interest. The proposed multiresolution fractal method and the more conventional methods of single-resolution fractal, gray-level co-occurrence matrix, and run-length matrix were used to extract features from the parametric maps. Only the data obtained before and after the first NACT cycle were used to evaluate early prediction of response. With a training (N = 40) and testing (N = 15) data set, support vector machine was used to assess the predictive abilities of the features in classification of pathologic complete response versus non-pathologic complete response. Generally the multiresolution fractal features from individual maps and the concatenated features from all parametric maps showed better predictive performances than conventional features, with receiver operating curve area under the curve (AUC) values of 0.91 (all parameters) and 0.80 (Ktrans), in the training and testing sets, respectively. The differences in AUC were statistically significant (P < .05) for several parametric maps. Thus, multiresolution analysis that decomposes the texture at various spatial-frequency scales may more accurately capture changes in tumor vascular heterogeneity as measured by DCE-MRI, and therefore provide better early prediction of NACT response.

The Impact of Arterial Input Function Determination Variations on Prostate Dynamic Contrast-Enhanced Magnetic Resonance Imaging Pharmacokinetic Modeling: A Multicenter Data Analysis Challenge, Part II.

This multicenter study evaluated the effect of variations in arterial input function (AIF) determination on pharmacokinetic (PK) analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data using the shutter-speed model (SSM). Data acquired from eleven prostate cancer patients were shared among nine centers. Each center used a site-specific method to measure the individual AIF from each data set and submitted the results to the managing center. These AIFs, their reference tissue-adjusted variants, and a literature population-averaged AIF, were used by the managing center to perform SSM PK analysis to estimate Ktrans (volume transfer rate constant), ve (extravascular, extracellular volume fraction), kep (efflux rate constant), and τi (mean intracellular water lifetime). All other variables, including the definition of the tumor region of interest and precontrast T1 values, were kept the same to evaluate parameter variations caused by variations in only the AIF. Considerable PK parameter variations were observed with within-subject coefficient of variation (wCV) values of 0.58, 0.27, 0.42, and 0.24 for Ktrans, ve, kep, and τi, respectively, using the unadjusted AIFs. Use of the reference tissue-adjusted AIFs reduced variations in Ktrans and ve (wCV = 0.50 and 0.10, respectively), but had smaller effects on kep and τi (wCV = 0.39 and 0.22, respectively). kep is less sensitive to AIF variation than Ktrans, suggesting it may be a more robust imaging biomarker of prostate microvasculature. With low sensitivity to AIF uncertainty, the SSM-unique τi parameter may have advantages over the conventional PK parameters in a longitudinal study.

DCE-MRI Texture Features for Early Prediction of Breast Cancer Therapy Response.

This study investigates the effectiveness of hundreds of texture features extracted from voxel-based dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parametric maps for early prediction of breast cancer response to neoadjuvant chemotherapy (NAC). In total, 38 patients with breast cancer underwent DCE-MRI before (baseline) and after the first of the 6-8 NAC cycles. Quantitative pharmacokinetic (PK) parameters and semiquantitative metrics were estimated from DCE-MRI time-course data. The residual cancer burden (RCB) index value was computed based on pathological analysis of surgical specimens after NAC completion. In total, 1043 texture features were extracted from each of the 13 parametric maps of quantitative PK or semiquantitative metric, and their capabilities for early prediction of RCB were examined by correlating feature changes between the 2 MRI studies with RCB. There were 1069 pairs of feature-map combinations that showed effectiveness for response prediction with 4 correlation coefficients >0.7. The 3-dimensional gray-level cooccurrence matrix was the most effective feature extraction method for therapy response prediction, and, in general, the statistical features describing texture heterogeneity were the most effective features. Quantitative PK parameters, particularly those estimated with the shutter-speed model, were more likely to generate effective features for prediction response compared with the semiquantitative metrics. The best feature-map pair could predict pathologic complete response with 100% sensitivity and 100% specificity using our cohort. In conclusion, breast tumor heterogeneity in microvasculature as measured by texture features of voxel-based DCE-MRI parametric maps could be a useful biomarker for early prediction of NAC response.

Evaluation of Soft Tissue Sarcoma Response to Preoperative Chemoradiotherapy Using Dynamic Contrast-Enhanced Magnetic Resonance Imaging.

This study aims to assess the utility of quantitative dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) parameters in comparison with imaging tumor size for early prediction and evaluation of soft tissue sarcoma response to preoperative chemoradiotherapy. In total, 20 patients with intermediate- to high-grade soft tissue sarcomas received either a phase I trial regimen of sorafenib + chemoradiotherapy (n = 8) or chemoradiotherapy only (n = 12), and underwent DCE-MRI at baseline, after 2 weeks of treatment with sorafenib or after the first chemotherapy cycle, and after therapy completion. MRI tumor size in the longest diameter (LD) was measured according to the RECIST (Response Evaluation Criteria In Solid Tumors) guidelines. Pharmacokinetic analyses of DCE-MRI data were performed using the Shutter-Speed model. After only 2 weeks of treatment with sorafenib or after 1 chemotherapy cycle, Ktrans (rate constant for plasma/interstitium contrast agent transfer) and its percent change were good early predictors of optimal versus suboptimal pathological response with univariate logistic regression C statistics values of 0.90 and 0.80, respectively, whereas RECIST LD percent change was only a fair predictor (C = 0.72). Post-therapy Ktrans, ve (extravascular and extracellular volume fraction), and kep (intravasation rate constant), not RECIST LD, were excellent (C > 0.90) markers of therapy response. Several DCE-MRI parameters before, during, and after therapy showed significant (P < .05) correlations with percent necrosis of resected tumor specimens. In conclusion, absolute values and percent changes of quantitative DCE-MRI parameters provide better early prediction and evaluation of the pathological response of soft tissue sarcoma to preoperative chemoradiotherapy than the conventional measurement of imaging tumor size change.

Early Prediction and Evaluation of Breast Cancer Response to Neoadjuvant Chemotherapy Using Quantitative DCE-MRI.

The purpose is to compare quantitative dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) metrics with imaging tumor size for early prediction of breast cancer response to neoadjuvant chemotherapy (NACT) and evaluation of residual cancer burden (RCB). Twenty-eight patients with 29 primary breast tumors underwent DCE-MRI exams before, after one cycle of, at midpoint of, and after NACT. MRI tumor size in the longest diameter (LD) was measured according to the RECIST (Response Evaluation Criteria In Solid Tumors) guidelines. Pharmacokinetic analyses of DCE-MRI data were performed with the standard Tofts and Shutter-Speed models (TM and SSM). After one NACT cycle the percent changes of DCE-MRI parameters K(trans) (contrast agent plasma/interstitium transfer rate constant), ve (extravascular and extracellular volume fraction), kep (intravasation rate constant), and SSM-unique τi (mean intracellular water lifetime) are good to excellent early predictors of pathologic complete response (pCR) vs. non-pCR, with univariate logistic regression C statistics value in the range of 0.804 to 0.967. ve values after one cycle and at NACT midpoint are also good predictors of response, with C ranging 0.845 to 0.897. However, RECIST LD changes are poor predictors with C = 0.609 and 0.673, respectively. Post-NACT K(trans), τi, and RECIST LD show statistically significant (P < .05) correlations with RCB. The performances of TM and SSM analyses for early prediction of response and RCB evaluation are comparable. In conclusion, quantitative DCE-MRI parameters are superior to imaging tumor size for early prediction of therapy response. Both TM and SSM analyses are effective for therapy response evaluation. However, the τi parameter derived only with SSM analysis allows the unique opportunity to potentially quantify therapy-induced changes in tumor energetic metabolism.

The Impact of Arterial Input Function Determination Variations on Prostate Dynamic Contrast-Enhanced Magnetic Resonance Imaging Pharmacokinetic Modeling: A Multicenter Data Analysis Challenge.

Dynamic contrast-enhanced MRI (DCE-MRI) has been widely used in tumor detection and therapy response evaluation. Pharmacokinetic analysis of DCE-MRI time-course data allows estimation of quantitative imaging biomarkers such as Ktrans(rate constant for plasma/interstitium contrast reagent (CR) transfer) and ve (extravascular and extracellular volume fraction). However, the use of quantitative DCE-MRI in clinical prostate imaging islimited, with uncertainty in arterial input function (AIF, i.e., the time rate of change of the concentration of CR in the blood plasma) determination being one of the primary reasons. In this multicenter data analysis challenge to assess the effects of variations in AIF quantification on estimation of DCE-MRI parameters, prostate DCE-MRI data acquired at one center from 11 prostate cancer patients were shared among nine centers. Each center used its site-specific method to determine the individual AIF from each data set and submitted the results to the managing center. Along with a literature population averaged AIF, these AIFs and their reference-tissue-adjusted variants were used by the managing center to perform pharmacokinetic analysis of the DCE-MRI data sets using the Tofts model (TM). All other variables including tumor region of interest (ROI) definition and pre-contrast T1 were kept the same to evaluate parameter variations caused by AIF variations only. Considerable pharmacokinetic parameter variations were observed with the within-subject coefficient of variation (wCV) of Ktrans obtained with unadjusted AIFs as high as 0.74. AIF-caused variations were larger in Ktrans than ve and both were reduced when reference-tissue-adjusted AIFs were used. The parameter variations were largely systematic, resulting in nearly unchanged parametric map patterns. The CR intravasation rate constant, kep (= Ktrans/ve), was less sensitive to AIF variation than Ktrans (wCV for unadjusted AIFs: 0.45 for kepvs. 0.74 for Ktrans), suggesting that it might be a more robust imaging biomarker of prostate microvasculature than Ktrans.

Behavioral and magnetic resonance spectroscopic studies in the rat hyperserotonemic model of autism.

Autism is classified as a pervasive developmental disorder, with several cardinal features including sensory disturbances, obsessive-compulsive-like behavior, lack of bonding to caregivers and motor disturbances. To date, there is a lack of an animal model of the disease. The current work is aimed at producing such a model by treating developing rat pups with a serotonergic agonist, 5-methoxytryptamine (5-MT; 1 mg/kg) during development (from gestational age 12 days to postnatal day 20), thus mimicking one of the hallmark neurochemical features of the illness-increases in the neurotransmitter, serotonin. Animals were then tested in behavioral paradigms that may resemble the human illness. Treated rat pups were found to be overreactive to auditory or tactile sensory stimuli, to display changes in the negative geotaxic test of motor development, to show lack of separation-induced vocalizations when their dam was removed and to show decreased alternation in the spontaneous alternation task. As well, the animals showed metabolic abnormalities in the brain using in vivo proton magnetic resonance spectroscopy, which are consistent with those observed in autistic children. In summary, the model we are proposing shows some of the behavioral and metabolic features of autism, as well as being produced through alteration of a neurochemical system known to be altered in autism.

Errors in Quantitative Image Analysis due to Platform-Dependent Image Scaling.

PURPOSE: To evaluate the ability of various software (SW) tools used for quantitative image analysis to properly account for source-specific image scaling employed by magnetic resonance imaging manufacturers. METHODS: A series of gadoteridol-doped distilled water solutions (0%, 0.5%, 1%, and 2% volume concentrations) was prepared for manual substitution into one (of three) phantom compartments to create "variable signal," whereas the other two compartments (containing mineral oil and 0.25% gadoteriol) were held unchanged. Pseudodynamic images were acquired over multiple series using four scanners such that the histogram of pixel intensities varied enough to provoke variable image scaling from series to series. Additional diffusion-weighted images were acquired of an ice-water phantom to generate scanner-specific apparent diffusion coefficient (ADC) maps. The resulting pseudodynamic images and ADC maps were analyzed by eight centers of the Quantitative Imaging Network using 16 different SW tools to measure compartment-specific region-of-interest intensity. RESULTS: Images generated by one of the scanners appeared to have additional intensity scaling that was not accounted for by the majority of tested quantitative image analysis SW tools. Incorrect image scaling leads to intensity measurement bias near 100%, compared to nonscaled images. CONCLUSION: Corrective actions for image scaling are suggested for manufacturers and quantitative imaging community.

Variations of dynamic contrast-enhanced magnetic resonance imaging in evaluation of breast cancer therapy response: a multicenter data analysis challenge.

Pharmacokinetic analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) time-course data allows estimation of quantitative parameters such as K (trans) (rate constant for plasma/interstitium contrast agent transfer), v e (extravascular extracellular volume fraction), and v p (plasma volume fraction). A plethora of factors in DCE-MRI data acquisition and analysis can affect accuracy and precision of these parameters and, consequently, the utility of quantitative DCE-MRI for assessing therapy response. In this multicenter data analysis challenge, DCE-MRI data acquired at one center from 10 patients with breast cancer before and after the first cycle of neoadjuvant chemotherapy were shared and processed with 12 software tools based on the Tofts model (TM), extended TM, and Shutter-Speed model. Inputs of tumor region of interest definition, pre-contrast T1, and arterial input function were controlled to focus on the variations in parameter value and response prediction capability caused by differences in models and associated algorithms. Considerable parameter variations were observed with the within-subject coefficient of variation (wCV) values for K (trans) and v p being as high as 0.59 and 0.82, respectively. Parameter agreement improved when only algorithms based on the same model were compared, e.g., the K (trans) intraclass correlation coefficient increased to as high as 0.84. Agreement in parameter percentage change was much better than that in absolute parameter value, e.g., the pairwise concordance correlation coefficient improved from 0.047 (for K (trans)) to 0.92 (for K (trans) percentage change) in comparing two TM algorithms. Nearly all algorithms provided good to excellent (univariate logistic regression c-statistic value ranging from 0.8 to 1.0) early prediction of therapy response using the metrics of mean tumor K (trans) and k ep (=K (trans)/v e, intravasation rate constant) after the first therapy cycle and the corresponding percentage changes. The results suggest that the interalgorithm parameter variations are largely systematic, which are not likely to significantly affect the utility of DCE-MRI for assessment of therapy response.

Phase I trial of preoperative chemoradiation plus sorafenib for high-risk extremity soft tissue sarcomas with dynamic contrast-enhanced MRI correlates.

PURPOSE: We conducted a phase I trial of the addition of sorafenib to a chemoradiotherapy regimen in patients with high-risk (intermediate/high grade, >5 cm) extremity soft tissue sarcoma undergoing limb salvage surgery. We conducted a correlative study of quantitative dynamic contrast-enhanced MRI (DCE-MRI) to assess response to treatment. EXPERIMENTAL DESIGN: Patients were treated at increasing dose levels of sorafenib (200 mg daily, 400 mg daily, 400 mg twice daily) initiated 14 days before three preoperative and three postoperative cycles of epirubicin/ifosfamide. Radiation (28 Gy) was administered during cycle 2 with epirubicin omitted. The primary objective was to determine the maximum tolerated dose (MTD) of sorafenib. DCE-MRI was conducted at baseline, after 2 weeks of sorafenib, and before surgery. The imaging data were subjected to quantitative pharmacokinetic analyses. RESULTS: Eighteen subjects were enrolled, of which 16 were evaluable. The MTD of sorafenib was 400 mg daily. Common grade 3-4 adverse events included neutropenia (94%), hypophosphatemia (75%), anemia (69%), thrombocytopenia (50%), and neutropenic fever/infection (50%). Of note, 38% developed wound complications requiring surgical intervention. The rate of ≥95% histopathologic tumor necrosis was 44%. Changes in DCE-MRI biomarker ΔK(trans) after 2 weeks of sorafenib correlated with histologic response (R(2) = 0.67, P = 0.012) at surgery. CONCLUSION: The addition of sorafenib to preoperative chemoradiotherapy is feasible and warrants further investigation in a larger trial. DCE-MRI detected changes in tumor perfusion after 2 weeks of sorafenib and may be a minimally invasive tool for rapid assessment of drug effect in soft tissue sarcoma.

1H-magnetic resonance spectroscopy markers of cognitive and language ability in clinical subtypes of autism spectrum disorders.

This study assessed metabolic functioning of regional brain areas to address whether there is a neurometabolic profile reflecting the underlying neuropathology in individuals with autism spectrum disorders, and if varied profiles correlate with the clinical subtypes. Thirteen children (7-16 years) with autism spectrum disorders and 8 typically developing children were compared on (1)H-magnetic resonance spectroscopy data collected from hippocampus-amygdala and cerebellar regions. The autism spectrum disorder group had significantly lower N-acetyl-aspartate/creatine ratios bilaterally in the hippocampus-amygdala but not cerebellum, whereas myo-inositol/creatine was significantly increased in all measured regions. Choline/creatine was also significantly elevated in the left hippocampus-amygdala and cerebellar regions of children with autism spectrum disorder. Comparisons within the autism spectrum disorder group when clinically subdivided by history of speech delay revealed significant metabolic ratio differences. Magnetic resonance spectroscopy can provide important information regarding abnormal brain metabolism and clinical classification in autism spectrum disorders.

Shutter-speed analysis of contrast reagent bolus-tracking data: Preliminary observations in benign and malignant breast disease.

The standard pharmacokinetic model applied to contrast reagent (CR) bolus-tracking (B-T) MRI (dynamic-contrast-enhanced) data makes the intrinsic assumption that equilibrium transcytolemmal water molecule exchange is effectively infinitely fast. Theory and simulation have suggested that this assumption can lead to significant errors. Recent analyses of animal model experimental data have confirmed two predicted signature inadequacies: a specific temporal mismatch with the B-T time-course and a CR dose-dependent underestimation of model parameters. The most parsimonious adjustment to account for this aspect leads to the "shutter-speed" pharmacokinetic model. Application of the latter to the animal model data mostly eliminates the two signature inadequacies. Here, the standard and shutter-speed models are applied to B-T data obtained from routine human breast examinations. The signature standard model temporal mismatch is found for each of the three invasive ductal carcinoma (IDC) cases and for each of the three fibroadenoma (FA) cases studied. It is effectively eliminated by use of the shutter-speed model. The size of the mismatch is considerably greater for the IDC lesions than for the FA lesions, causing the shutter-speed model to exhibit improved discrimination of malignant IDC tumors from the benign FA lesions compared with the standard model. Furthermore, the shutter-speed model clearly reveals focal "hot spots" of elevated CR perfusion/permeation present in only the malignant tumors.

Evidence for shutter-speed variation in CR bolus-tracking studies of human pathology.

The standard pharmacokinetic model for the analysis of MRI contrast reagent (CR) bolus-tracking (B-T) data assumes that the mean intracellular water molecule lifetime (tau(i)) is effectively zero. This assertion is inconsistent with a considerable body of physiological measurements. Furthermore, theory and simulation show the B-T time-course shape to be very sensitive to the tau(i) magnitude in the physiological range (hundreds of milliseconds to several seconds). Consequently, this standard model aspect can cause significant underestimations (factors of 2 or 3) of the two parameters usually determined: K(trans), the vascular wall CR transfer rate constant, and v(e), the CR distribution volume (the extracellular, extravascular space fraction). Analyses of animal model data confirmed two predicted behaviors indicative of this standard model inadequacy: (1) a specific temporal pattern for the mismatch between the best-fitted curve and data; and (2) an inverse dependence of the curve's K(trans) and v(e) magnitudes on the CR dose. These parameters should be CR dose-independent. The most parsimonious analysis allowing for realistic tau(i) values is the 'shutter-speed' model. Its application to the experimental animal data essentially eliminated the two standard model signature inadequacies. This paper reports the first survey for the extent of this 'shutter-speed effect' in human data. Retrospective analyses are made of clinical data chosen from a range of pathology (the active multiple sclerosis lesion, the invasive ductal carcinoma breast tumor, and osteosarcoma in the leg) that provides a wide variation, particularly of K(trans). The signature temporal mismatch of the standard model is observed in all cases, and is essentially eliminated by use of the shutter-speed model. Pixel-by-pixel maps show that parameter values from the shutter-speed analysis are increased by more than a factor of 3 for some lesion regions. This endows the lesions with very high contrast, and reveals heterogeneities that are often not seen in the standard model maps. Normal muscle regions in the leg allow validation of the shutter-speed model K(trans), v(e), and tau(i) magnitudes, by comparison with results of previous careful rat leg studies not possible for human subjects.