Shutter-speed Model Research Articles

Impact of Arterial Input Function and Pharmacokinetic Models on DCE-MRI Biomarkers for Detection of Vascular Effect Induced by Stroma-Directed Drug in an Orthotopic Mouse Model of Pancreatic Cancer.

We demonstrated earlier in mouse models of pancreatic ductal adenocarcinoma (PDA) that Ktrans derived from dynamic contrast-enhanced (DCE) MRI detected microvascular effect induced by PEGPH20, a hyaluronidase which removes stromal hyaluronan, leading to reduced interstitial fluid pressure in the tumor (Clinical Cancer Res (2019) 25: 2314-2322). How the choice of pharmacokinetic (PK) model and arterial input function (AIF) may impact DCE-derived markers for detecting such an effect is not known. Retrospective analyses of the DCE-MRI of the orthotopic PDA model are performed to examine the impact of individual versus group AIF combined with Tofts model (TM), extended-Tofts model (ETM), or shutter-speed model (SSM) on the ability to detect the microvascular changes induced by PEGPH20 treatment. Individual AIF exhibit a marked difference in peak gadolinium concentration. However, across all three PK models, kep values show a significant correlation between individual versus group-AIF (p < 0.01). Regardless individual or group AIF, when kep is obtained from fitting the DCE-MRI data using the SSM, kep shows a significant increase after PEGPH20 treatment (p < 0.05 compared to the baseline); %change of kep from baseline to post-treatment is also significantly different between PEGPH20 versus vehicle group (p < 0.05). In comparison, when kep is derived from the TM, only the use of individual AIF leads to a significant increase of kep after PEGPH20 treatment, whereas the %change of kep is not different between PEGPH20 versus vehicle group. Group AIF but not individual AIF allows detection of a significant increase of Vp (derived from the ETM) in PEGPH20 versus vehicle group (p < 0.05). Increase of Vp is consistent with a large increase of mean capillary lumen area estimated from immunostaining. Our results suggest that kep derived from SSM and Vp from ETM, both using group AIF, are optimal for the detection of microvascular changes induced by stroma-directed drug PEGPH20. These analyses provide insights in the choice of PK model and AIF for optimal DCE protocol design in mouse pancreatic cancer models.

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

IntroductionBreast 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.MethodsForty-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.ResultsPre- 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.ConclusionAccurate 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.

Consideration of transmembrane water exchange in pharmacokinetic model significantly improves the accuracy of DCE-MRI in estimating cellular density: A pilot study in glioblastoma multiforme

Transmembrane water exchange (TWE) including transcytolemmal water exchange and transvascular water exchange is involved in many in vivo measurements and makes different contributions to the measuring results. In this study, we focus on the potential influence of TWE on the cell density parameter, intracellular water mole fraction pi, derived by dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) which has been reported as a technique to characterize perfusion and vascularization of tissues, but its accuracy in measuring cell density (or interstitial space) has been questioned. Sixteen patients with glioblastoma multiforme (GBM) were enrolled since GBM shows strong intratumor heterogeneity in both cell density and TWE. All the subjects were collected with DCE-MRI and apparent diffusion coefficient (ADC) map. The latter was considered as a valid surrogate of cell density. Extended Tofts (eTofts) model considering TWE as infinitely large variables and shutter-speed model (SSM) considering TWE as finite ones were used to fit DCE-MRI data. Monte Carlo (MC) and finite difference (FD) methods were used to simulate the influence of TWE on DCE-MRI-derived pi and ADC, respectively. The eTofts model shows a significant overestimation of pi in comparison with SSM in GBM (P < 0.001), which is in accordance with MC simulations, and this overestimation shows dependence on the intra-to-extracellular water exchange rate constant (kio). Significant negative correlations between ADC and SSM-derived pi were found in both voxel-wise analyses (t-test P < 0.001, average r = −0.74) and inter-subject comparisons (r = −0.63, P = 0.009). But no consistent voxel-wise correlations (P > 0.05) and a weaker inter-subject negative correlation (r = −0.56, P = 0.02) were found between ADC and eTofts-derived pi. Further experimental and FD results revealed that kio made a limited contribution to ADC values in the physiological kio range in GBM, supporting ADC as a valid biomarker of cell density. These results suggest that the DCE-MRI pharmacokinetic shutter-speed model could significantly improve its accuracy in cell density estimation because of the considering transmembrane water exchange.

Assessing Tumour Haemodynamic Heterogeneity and Response to Choline Kinase Inhibition Using Clustered Dynamic Contrast Enhanced MRI Parameters in Rodent Models of Glioblastoma.

Simple SummaryThis study was designed to monitor changes in DCE-MRI-based parameters in preclinical GBM models in response to choline kinase inhibition using a cluster analysis approach. In terms of therapeutic response in F98 rat GBMs, a sustained decrease in permeability and perfusion and increased necrosis was observed during treatment with JAS239 as compared to control animals. No significant differences in these parameters were found for the GL261 mice GBMs. The study demonstrates that region-based clustered pharmacokinetic parameters obtained using DCE-MRI can be used for detecting and assessing tumour haemodynamic heterogeneity, which may be useful in assessing therapeutic response.To investigate the utility of DCE-MRI derived pharmacokinetic parameters in evaluating tumour haemodynamic heterogeneity and treatment response in rodent models of glioblastoma, imaging was performed on intracranial F98 and GL261 glioblastoma bearing rodents. Clustering of the DCE-MRI-based parametric maps (using Tofts, extended Tofts, shutter speed, two-compartment, and the second generation shutter speed models) was performed using a hierarchical clustering algorithm, resulting in areas with poor fit (reflecting necrosis), low, medium, and high valued pixels representing parameters , , Kep, , and . There was a significant increase in the number of necrotic pixels with increasing tumour volume and a significant correlation between and tumour volume suggesting increased extracellular volume in larger tumours. In terms of therapeutic response in F98 rat GBMs, a sustained decrease in permeability and perfusion and a reduced cell density was observed during treatment with JAS239 based on , and as compared to control animals. No significant differences in these parameters were found for the GL261 tumour, indicating that this model may be less sensitive to JAS239 treatment regarding changes in vascular parameters. This study demonstrates that region-based clustered pharmacokinetic parameters derived from DCE-MRI may be useful in assessing tumour haemodynamic heterogeneity with the potential for assessing therapeutic response.

Disposable point-of-care portable perfusion phantom for quantitative DCE-MRI.

To develop a disposable point-of-care portable perfusion phantom (DP4) and validate its clinical utility in a multi-institutional setting for quantitative dynamic contrast-enhanced magnetic resonance imaging (qDCE-MRI). The DP4 phantom was designed for single-use and imaged concurrently with a human subject so that the phantom data can be utilized as the reference to detect errors in qDCE-MRI measurement of human tissues. The change of contrast-agent concentration in the phantom was measured using liquid chromatography-mass spectrometry. The repeatability of the contrast enhancement curve (CEC) was assessed with five phantoms in a single MRI scanner. Five healthy human subjects were recruited to evaluate the reproducibility of qDCE-MRI measurements. Each subject was imaged concurrently with the DP4 phantom at two institutes using three 3T MRI scanners from three different vendors. Pharmacokinetic (PK) parameters in the regions of liver, spleen, pancreas, and paravertebral muscle were calculated based on the Tofts model (TM), extended Tofts model (ETM), and shutter speed model (SSM). The reproducibility of each PK parameter over three measurements was evaluated with the intraclass correlation coefficient (ICC) and compared before and after DP4-based error correction. The contrast-agent concentration in the DP4 phantom was linearly increased over 10 min (0.17mM/min, measurement accuracy: 96%) after injecting gadoteridol (100mM) at a constant rate (0.24ml/s, 4ml). The repeatability of the CEC within the phantom was 0.997 when assessed by the ICC. The reproducibility of the volume transfer constant, Ktrans , was the highest of the PK parameters regardless of the PK models. The ICCs of Ktrans in the TM, ETM, and SSM before DP4-based error correction were 0.34, 0.39, and 0.72, respectively, while those increased to 0.93, 0.98, and 0.86, respectively, after correction. The DP4 phantom is reliable, portable, and capable of significantly improving the reproducibility of qDCE-MRI measurements.

Diffusion-Weighted and Dynamic Contrast-Enhanced MRI Derived Imaging Metrics for Stereotactic Body Radiotherapy of Pancreatic Ductal Adenocarcinoma: Preliminary Findings.

We aimed to assess longitudinal changes in quantitative imaging metric values obtained from diffusion-weighted (DW-) and dynamic contrast-enhanced magnetic resonance imaging (DCE)-MRI at pre-treatment (TX[0]), immediately after the first fraction of stereotactic body radiotherapy (D1-TX[1]), and 6 weeks post-TX (Post-TX[2]) in patients with pancreatic ductal adenocarcinoma. Ten enrolled patients (n = 10) underwent DW- and DCE-MRI examinations on a 3.0 T scanner. The apparent diffusion coefficient, ADC (mm2/s), was derived from DW imaging data using a monoexponential model. The tissue relaxation rate, R1t, time-course data were fitted with a shutter-speed model, which provides estimates of the volume transfer constant, Ktrans (min−1), extravascular extracellular volume fraction, ve, and mean lifetime of intracellular water protons, τi (seconds). Wilcoxon rank-sum test compared the mean values, standard deviation, skewness, kurtosis, and relative percentage (r, %) changes (Δ) in ADC, Ktrans, ve, and τi values between the magnetic resonance examinations. rADCΔ2–0 values were significantly greater than rADCΔ1-0 values (P = .009). rKtransΔ2–0 values were significantly lower than rKtrans Δ1-0 values (P = .048). rveΔ2-1 and rveΔ2-0 values were significantly different (P = .016). rτiΔ2-1 values were significantly lower than rτiΔ2-0 values (P = .008). For group comparison, the pre-TX mean and kurtosis of ADC (P = .18 and P = .14), skewness and kurtosis of Ktrans values (P = .14 for both) showed a leaning toward significant difference between patients who experienced local control (n = 2) and failed early (n = 4). DW- and DCE-MRI-derived quantitative metrics could be useful biomarkers to evaluate longitudinal changes to stereotactic body radiotherapy in patients with pancreatic ductal adenocarcinoma.

Shutter-Speed DCE-MRI Analyses of Human Glioblastoma Multiforme (GBM) Data.

The shutter-speed model dynamic contrast-enhanced (SSM-DCE) MRI pharmacokinetic analysis adds a metabolic dimension to DCE-MRI. This is of particular interest in cancers, since abnormal metabolic activity might happen. To develop a DCE-MRI SSM analysis framework for glioblastoma multiforme (GBM) cases considering the heterogeneous tissue found in GBM. Prospective. Ten GBM patients. 3T MRI with DCE-MRI. The corrected Akaike information criterion (AICc ) was used to automatically separate DCE-MRI data into proper SSM versions based on the contrast agent (CA) extravasation in each pixel. The supra-intensive parameters, including the vascular water efflux rate constant (kbo ), the cellular efflux rate constant (kio ), and the CA vascular efflux rate constant (kpe ), together with intravascular and extravascular-extracellular water mole fractions (pb and po , respectively) were determined. Further error analyses were also performed to eliminate unreliable estimations on kio and kbo . Student's t-test. For tumor pixels of all subjects, 88% show lower AICc with SSM than with the Tofts model. Compared to normal-appearing white matter (NAWM), tumor tissue showed significantly larger pb (0.045 vs. 0.011, P < 0.001) and higher kpe (3.0 × 10-2 s-1 vs. 6.1 × 10-4 s-1 , P < 0.001). In the contrast, significant kbo reduction was observed from NAWM to GBM tumor tissue (2.8 s-1 vs. 1.0 s-1 , P < 0.001). In addition, kbo is four orders and two orders of magnitude greater than kpe in the NAWM and GBM tumor, respectively. These results indicate that CA and water molecule have different transmembrane pathways. The mean tumor kio of all subjects was 0.57 s-1 . We demonstrate the feasibility of applying SSM models in GBM cases. Within the proposed SSM analysis framework, kio and kbo could be estimated, which might be useful biomarkers for GBM diagnosis and survival prediction in future. 4 TECHNICAL EFFICACY: Stage 1 J. Magn. Reson. Imaging 2020;52:850-863.

Quantification of regional myocardial mean intracellular water lifetime: A nonhuman primate study in myocardial stress.

Heart failure with preserved ejection fraction (HFpEF) is typically associated with early metabolic remodeling. Noninvasive imaging biomarkers that reflect these changes will be crucial in determining responses to early drug interventions in these patients. Mean intracellular water lifetime (τi ) has been shown to be partially inversely related to Na, K-ATPase transporter activity and may thus provide insight into the metabolic status in HFpEF patients. Here, we aim to perform regional quantification of τi using dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) in the nonhuman primate (NHP) heart and evaluate its region-specific variations under conditions of myocardial stress in the context of perturbed myocardial function. Cardiac stress was induced in seven naïve cynomolgus macaques using a dobutamine stepwise infusion protocol. All animals underwent 3 T cardiac dual-bolus DCE and tagging MRI experiments. The shutter-speed model was employed to quantify regional τi from the DCE-MR images. Additionally, τi values were correlated with myocardial strains. During cardiac stress, there was a significant decrease in global τi (192.9 ± 76.3 ms vs 321.6 ± 70 ms at rest, P < 0.05) in the left ventricle, together with an increase in global peak circumferential strain (-15.4% ± 2.7% vs -10.1% ± 2.9% at rest, P < 0.05). Specifically, slice-level analysis further revealed that a greater significant decrease in mean τi was observed in the apical region (ΔτI = 182.4 ms) compared with the basal (Δτi = 113.2 ms) and midventricular regions (Δτi = 108.4 ms). Regional analysis revealed that there was a greater significant decrease in mean τi in the anterior (Δτi = 243.9 ms) and antero-lateral (Δτi = 177.2 ms) regions. In the inferior and infero-septal regions, although a decrease in τi was observed, it was not significant. Whole heart regional quantification of τi is feasible using DCE-MRI. τi is sensitive to regional changes in metabolic state during cardiac stress, and its value correlates with strain.

Reliability of DCE MRI data in primary brain tumours: a comparison of Tofts and Shutter Speed Models

Abstract The accuracy of quantitative measurements of perfusion parameters by DCE-MRI is crucial as it can significantly impact the clinical care of cancer patients. In this study, we provide a method to identify reliable DCE-MRI brain data for perfusion quantification with different pharmacokinetic models. We analysed DCE-MRI data of 14 patients with primary brain tumours using the Tofts model (TM), the extended-Tofts model (ETM), the shutter speed model (SSM) and the extended shutter speed model (ESSM). Due to the presence of the blood brain barrier, which can block the leakage of the CA into the interstitium, we also implemented the no-exchange model (NEM). For each lesion, we produced a 3D model selection map with the evaluation of the Akaike Information Criteria. The variability of each pharmacokinetic parameter extracted from the fitting of the model of choice was assessed with a noise propagation procedure, resulting in distributions of the coefficient of variation (CV). Results showed the NEM to be the most frequent model of choice (35.5%), followed by the ETM (32%), the TM (28.2%), the SSM (4.3%) and the ESSM (&lt;0.1%). In analysing the reliability of Ktrans, when considering regions with a CV&lt;20%, ≈25% of voxels were found to be stable. The remaining 75% of voxels were considered to be unreliable. In conclusion, an appropriate model selection, considering tissue biology and its effects on BBB permeability and exchange conditions, together with an analysis on the reliability and stability of the calculated parameters, is critical in the identification of reliable brain DCE-MRI data.

Reliability of dynamic contrast-enhanced magnetic resonance imaging data in primary brain tumours: a comparison of Tofts and shutter speed models

PurposeThe purpose of this study is to investigate the robustness of pharmacokinetic modelling of DCE-MRI brain tumour data and to ascertain reliable perfusion parameters through a model selection process and a stability test.MethodsDCE-MRI data of 14 patients with primary brain tumours were analysed using the Tofts model (TM), the extended Tofts model (ETM), the shutter speed model (SSM) and the extended shutter speed model (ESSM). A no-effect model (NEM) was implemented to assess overfitting of data by the other models. For each lesion, the Akaike Information Criteria (AIC) was used to build a 3D model selection map. The variability of each pharmacokinetic parameter extracted from this map was assessed with a noise propagation procedure, resulting in voxel-wise distributions of the coefficient of variation (CV).ResultsThe model selection map over all patients showed NEM had the best fit in 35.5% of voxels, followed by ETM (32%), TM (28.2%), SSM (4.3%) and ESSM (< 0.1%). In analysing the reliability of Ktrans, when considering regions with a CV < 20%, ≈ 25% of voxels were found to be stable across all patients. The remaining 75% of voxels were considered unreliable.ConclusionsThe majority of studies quantifying DCE-MRI data in brain tumours only consider a single model and whole tumour statistics for the output parameters. Appropriate model selection, considering tissue biology and its effects on blood brain barrier permeability and exchange conditions, together with an analysis on the reliability and stability of the calculated parameters, is critical in processing robust brain tumour DCE-MRI data.

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.

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.

A multi-parametric PET/MRI study of breast cancer: Evaluation of DCE-MRI pharmacokinetic models and correlation with diffusion and functional parameters.

46 patients with histologically confirmed breast cancer were enrolled and imaged with a 3T hybrid PET/MRI system, at staging. Diffusion, functional and perfusion parameters (measured by Tofts and shutter speed models) were compared. Results showed a good correlation between pharmacokinetic parameters and the SUV.

Multiparametric FDG-PET/MRI of Hepatocellular Carcinoma: Initial Experience.

Purpose To compare multiparametric (mp)FDG-PET/MRI metrics between hepatocellular carcinoma (HCC) and liver parenchyma and to assess the correlation between mpMRI and FDG-PET standard uptake values (SUVs) in liver parenchyma and HCC. Methods This prospective, institutional review board-approved study enrolled 15 patients (M/F 12/3; mean age 61 y) with HCC. mpMRI including blood-oxygen-level-dependent (BOLD) MRI, intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI), and dynamic contrast-enhanced-(DCE-) MRI was performed simultaneously with 18F-FDG-PET on a 3T PET/MRI hybrid system. Quantitative BOLD, IVIM and DCE-MRI parameters (Tofts model (TM) and shutter-speed model (SSM)), and PET parameters (SUVmean and SUVmax) were quantified and compared between HCC lesions and liver parenchyma using Wilcoxon signed-rank tests. SUV ratios between HCCs and liver were also calculated (SUVmean T/L and SUVmax T/L). Diagnostic performance of (combined) mp-PET/MRI parameters for characterization of HCC was assessed using ROC analysis. Spearman correlations between PET and mpMRI parameters in HCC tumors and liver parenchyma were evaluated. Results 21 HCC lesions (mean size 4.0 ± 2.4 cm; range 2–13 cm) were analyzed. HCCs exhibited significantly higher arterial fraction (from DCE-MRI) and lower R2∗ pre-O2 and post-O2 (from BOLD-MRI) versus liver parenchyma (P < 0.032). The highest diagnostic performance for differentiation between HCC and liver parenchyma was achieved for combined ART SSM and R2∗ post-O2 (AUC = 0.91). SUVmax showed reasonable performance for differentiation of HCC versus liver (AUC = 0.75). In HCC, DCE-MRI parameters Ktrans (TM and SSM) and ve TM exhibited significant negative correlations with SUVmax T/L (r ranges from −0.624 to −0.566; FDR-adjusted P < 0.050). Conclusions Despite the observed reasonable diagnostic performance of FDG-PET SUVmax for HCC detection and several significant correlations between FDG-PET SUV and DCE-MRI parameters, FDG-PET did not provide clear additional value for HCC characterization compared to mpMRI in this pilot study.

Shutter-speed dynamic contrast-enhanced MRI: Is it fit for purpose?

To test the ability of shutter-speed dynamic contrast-enhanced (DCE) MRI to estimate water exchange (WX) using simulations and assess its performance in clinical case studies of malignant and benign breast tumors. Data were simulated using a 1-compartment tracer kinetic (TK) model combined with a 2-pool WX model (2PX) and with a 2-compartment TK model. Typical DCE-MRI acquisition parameters were used with both WX-sensitive (8°) and -insensitive (25°) flip angles. Clinical data were obtained from patients with malignant and benign breast tumors. Data were fitted using a 2-compartment TK model and a 1-compartment TK model combined with 4 WX models: fast exchange limit (FXL), no exchange, 2PX, and shutter-speed. Fits to the 1-compartment simulated data were excellent, but estimates of WX obtained using the 2PX and shutter-speed models were poor. One-compartment TK model fits to the clinical malignant tumor data were bad, except for the shutter-speed model. However, that overestimated TK parameters compared to the best-fit 2-compartment TK model, which predicted a significant blood volume and leaky capillaries (1 tracer compartment is insufficient, 2 are necessary). All models produced excellent fits to the clinical benign tumor data with little variation between parameter estimates (1 tracer compartment is sufficient). The 2PX and shutter-speed models were unable to estimate WX from the DCE-MRI data. A good fit to malignant tumor data using the shutter-speed model was not explained by WX, but the choice of an inappropriate TK model leading to distorted parameter estimates.

Dynamic Contrast-Enhanced MRI-Derived Intracellular Water Lifetime (τ i ): A Prognostic Marker for Patients with Head and Neck Squamous Cell Carcinomas.

Shutter-speed model analysis of dynamic contrast-enhanced MR imaging allows estimation of mean intracellular water molecule lifetime (a measure of cellular energy metabolism) and volume transfer constant (a measure of hemodynamics). The purpose of this study was to investigate the prognostic utility of pretreatment mean intracellular water molecule lifetime and volume transfer constant in predicting overall survival in patients with squamous cell carcinomas of the head and neck and to stratify p16-positive patients based upon survival outcome. A cohort of 60 patients underwent dynamic contrast-enhanced MR imaging before treatment. Median, mean intracellular water molecule lifetime and volume transfer constant values from metastatic nodes were computed from each patient. Kaplan-Meier analyses were performed to associate mean intracellular water molecule lifetime and volume transfer constant and their combination with overall survival for the first 2 years, 5 years, and beyond (median duration, >7 years). By the last date of observation, 18 patients had died, and median follow-up for surviving patients (n = 42) was 8.32 years. Patients with high mean intracellular water molecule lifetime (4 deaths) had significantly (P = .01) prolonged overall survival by 5 years compared with those with low mean intracellular water molecule lifetime (13 deaths). Similarly, patients with high mean intracellular water molecule lifetime (4 deaths) had significantly (P = .006) longer overall survival at long-term duration than those with low mean intracellular water molecule lifetime (14 deaths). However, volume transfer constant was a significant predictor for only the 5-year follow-up period. There was some evidence (P < .10) to suggest that mean intracellular water molecule lifetime and volume transfer constant were associated with overall survival for the first 2 years. Patients with high mean intracellular water molecule lifetime and high volume transfer constant were associated with significantly (P < .01) longer overall survival compared with other groups for all follow-up periods. In addition, p16-positive patients with high mean intracellular water molecule lifetime and high volume transfer constant demonstrated a trend toward the longest overall survival. A combined analysis of mean intracellular water molecule lifetime and volume transfer constant provided the best model to predict overall survival in patients with squamous cell carcinomas of the head and neck.

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.

DCE-MRI of the prostate using shutter-speed vs. Tofts model for tumor characterization and assessment of aggressiveness.

To quantify Tofts model (TM) and shutter-speed model (SSM) perfusion parameters in prostate cancer (PCa) and noncancerous peripheral zone (PZ) and to compare the diagnostic performance of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to Prostate Imaging and Reporting and Data System (PI-RADS) classification for the assessment of PCa aggressiveness. Fifty PCa patients (mean age 60 years old) who underwent MRI at 3.0T followed by prostatectomy were included in this Institutional Review Board-approved retrospective study. DCE-MRI parameters (Ktrans , ve , kep [TM&SSM] and intracellular water molecule lifetime τi [SSM]) were determined in PCa and PZ. Differences in DCE-MRI parameters between PCa and PZ, and between models were assessed using Wilcoxon signed-rank tests. Receiver operating characteristic (ROC) analysis for differentiation between PCa and PZ was performed for individual and combined DCE-MRI parameters. Diagnostic performance of DCE-MRI parameters for identification of aggressive PCa (Gleason ≥8, grade group [GG] ≥3 or pathology stage pT3) was assessed using ROC analysis and compared with PI-RADSv2 scores. DCE-MRI parameters were significantly different between TM and SSM and between PZ and PCa (P < 0.037). Diagnostic performances of TM and SSM for differentiation of PCa from PZ were similar (highest AUC TM: Ktrans +kep 0.76, SSM: τi +kep 0.80). PI-RADS outperformed TM and SSM DCE-MRI for identification of Gleason ≥8 lesions (AUC PI-RADS: 0.91, highest AUC DCE-MRI: Ktrans +τi SSM 0.61, P = 0.002). The diagnostic performance of PI-RADS and DCE-MRI for identification of GG ≥3 and pT3 PCa was not significantly different (P > 0.213). SSM DCE-MRI did not increase the diagnostic performance of DCE-MRI for PCa characterization. PI-RADS outperformed both TM and SSM DCE-MRI for identification of aggressive cancer. 3 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:837-849.

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.

Evaluation of the effect of transcytolemmal water exchange analysis for therapeutic response assessment using DCE-MRI: a comparison study

This study compares the shutter-speed (SS) and the Tofts models as used in assessing therapeutic response in a longitudinal DCE-MRI experiment. Sixteen nu/nu mice with implanted colorectal adenocarcinoma cell line (LS-174T) were randomly assigned into treatment/control groups (n = 8/group) and received bevacizumab/saline twice weekly (Day1/Day4/Day8). All mice were scanned at one pre- (Day0) and two post-treatment (Day2/Day9) time points using a high spatiotemporal resolution DCE-MRI pulse sequence. The CA extravasation rate constant from the Tofts/SS model and the mean intracellular water residence time from the SS model were analyzed. A biological subvolume (BV) within the tumor was identified based on the intensity distribution, and the SS model parameters within the BV ( and ) were analyzed. It is found that and have a similar spatial distribution in the tumor volume. The Bayesian information criterion results show that the SS model was a better fit for all scans. At Day9, the treatment group had significantly higher tumor mean (p = 0.021), (p = 0.021) and (p = 0.045). When BV from transcytolemmal water exchange analysis was adopted, the treatment group had higher mean at both Day2 (p = 0.038) and Day9 (p = 0.007). Additionally, at Day9, the treatment group had higher mean (p = 0.045) and higher spatial heterogeneity indices (Rényi dimensions) d1 (p = 0.010) and d2 (p = 0.021). When mean and its coefficient of variation (CV) were used to separate treatment/control group samples using supporting vector machine, the accuracy of treatment/control classification was 68.8% at Day2 and 87.5% at Day9; in contrast, the Day2/Day9 accuracy were 62.5%/87.5% using tumor mean and its CV and were 50.0%/87.5% using tumor mean and its CV, respectively. These results suggest that the SS model parameters outperformed the Tofts model parameters in terms of capturing bevacizumab therapeutic effect in this longitudinal experiment.