Dynamic contrast-enhanced MRI with Gd-EOB-DTPA for the quantitative assessment of early-stage liver fibrosis induced by carbon tetrachloride in rabbits

PurposeTo explore quantitative parameters obtained by dynamic contrast-enhanced magnetic resonance imaging (DCE MRI) with Gd-EOB-DTPA in discriminating early-stage liver fibrosis (LF) in a rabbit model.Materials and methodsLF was established in 60 rabbits by the injection of 50% CCl₄ oil solution, whereas 30 rabbits served as the control group. All rabbits underwent pathological examination to determine the LF stage using the METAVIR classification system. DCE MRI was performed, and quantitative parameters, including K^trans, K_ep, V_e, V_p and Re were measured and evaluated among the different LF stages using spearman correlation coefficients and receiver operating characteristic curve.ResultsIn all, 24, 25, and 22 rabbits had stage F0, stage F1, and stage F2 LF, respectively. K^trans (r = 0.803) increased, and K_ep (r = −0.495) and Re (r = −0.701) decreased with LF stage progression (P < 0.001), while no significant correlation was found for V_e or V_p. K^trans and Re were significantly different between all LF stage pairs compared (F0 vs. F1, F0 vs. F2, F1 vs. F2, F0 vs. F1-F2, P < 0.05). With the exception of F0 vs. F1, K_ep differed significantly between stages (P < 0.05). The AUC of K^trans was higher than that of other quantitative parameters, with an AUC of 0.92, 0.99, 0.94 and 0.92 for staging F0 vs. F1, F0 vs. F2, F1 vs. F2, and F0 vs. F1-F2, respectively.ConclusionAmong quantitative parameters of Gd-EOB-DTPA DCE MRI, K^trans was the best predictor for quantitatively differentiating early-stage LF.     

Free-breathing radial volumetric interpolated breath-hold examination sequence and dynamic contrast-enhanced MRI combined with diffusion-weighted imaging for assessment of solitary pulmonary nodules

ObjectiveTo test the performance of free-breathing Dynamic Contrast-Enhanced MRI (DCE-MRI) using a radial volumetric interpolated breath-hold examination (VIBE) sequence combined with diffusion-weighted imaging (DWI) for quantitative solitary pulmonary nodule (SPN) assessment.MethodsA total of 67 SPN cases receiving routine MRI routine scans, DWI, and dynamic-enhanced MRI in our hospital from May 2017 to November 2018 were collected. These cases were divided into a malignant group and a benign group according to the characteristics of the SPNs. The quantitative DCE-MRI parameters (K^trans, K_ep, V_e) and apparent diffusion coefficient (ADC) values of the nodules were measured.ResultsThe K^trans and K_ep values in the malignant group were higher than those in the benign group, while the ADC values in the malignant group were lower than those in the benign group. Furthermore, the K^trans value of adenocarcinoma was higher than that of squamous cell carcinoma and small cell carcinoma (P < 0.05). The V_e value was significantly different between non-small cell carcinoma and small cell carcinoma (P < 0.05). With an ADC value of 0.98 × 10⁻³ mm²/s as the threshold, the specificity and sensitivity to diagnose benign and malignant nodules was 90.6% and 80%, respectively.ConclusionHigh-temporal-resolution DCE-MRI using the r-VIBE technique in combination with DWI could contribute to pulmonary nodule analysis and possibly serve as a potential alternative to distinguish malignant from benign nodules as well as differentiate different types of malignancies.     

Assessment of early response to concurrent chemoradiotherapy in cervical cancer: value of diffusion-weighted and dynamic contrast-enhanced MR imaging

Purpose

To investigate diffusion-weighted (DWI) and dynamic contrast-enhanced MR imaging (DCE-MRI) as early response predictors in cervical cancer patients who received concurrent chemoradiotherapy (CCRT).

Materials and methods

Sixteen patients with cervical cancer underwent DWI and DCE-MRI before CCRT (preTx), at 1 week (postT1) and 4 weeks (postT2) after initiating treatment, and 1 month after the end of treatment (postT3). At each point, apparent diffusion coefficient (ADC) and DCE-MRI parameters were measured in tumors and gluteus muscles (GM). Tumor response was correlated with imaging parameters or changes in imaging parameters at each point.

Results

At each point, ADC, K^trans and V_e in tumors showed significant changes (P < 0.05), as compared with those of GM (P > 0.05). PostT1 tumor ADCs showed a significant correlation with tumor size response at postT2 (P = 0.041), and changes in tumor ADCs at postT1 had a significant correlation with tumor size (P = 0.04) and volume response (P = 0.003) at postT2. In tumors, preTx K^trans and V_e showed significant correlations with tumor size at postT3 (P = 0.011) and tumor size response at postT2 (P = 0.019), respectively.

Conclusion

DWI and DCE-MRI, as early biomarkers, have the potential to evaluate therapeutic responses to CCRT in cervical cancers.     

Differentiation of bone metastases from prostate cancer and benign red marrow depositions of the pelvic bone with multiparametric MRI

PurposeTo investigate the diagnostic utilities of imaging parameters derived from T1-weighted imaging (T1WI), diffusion-weighted imaging (DWI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to differentiate bone metastases from prostate cancer and benign red marrow depositions of the pelvic bone.Materials and methodsThirty-six lesions from 36 patients with prostate cancer were analyzed with T1WI, DWI, and DCE-MRI. The lesions were classified in the bone metastases (n = 22) and benign red marrow depositions (n = 14). Lesion-muscle ratio (LMR), apparent diffusion coefficient (ADC), volume transfer constant (K^trans), reflux rate (Kep), and volume fraction of the extravascular extracellular matrix (Ve) values were obtained from the lesions. The imaging parameters of the both groups were compared using the Mann-Whitney U test, receiver operating characteristics (ROC) curves were analyzed. For the ROC curves, area under the curves (AUCs) were compared.ResultsThe ADC, K^trans, K_ep, and V_e values of bone metastases were significantly higher than those of benign red marrow depositions (Mann-Whitney U test, p < 0.05). However, there was no significant difference in LMR between the two groups (Mann-Whitney U test, p = 0.360). The AUCs of K^trans_, K_ep, ADC, V_e, and LMR were 0.896, 0.844, 0.812, 0.724, and 0.448, respectively. In the pairwise comparison of ROC curves, the AUCs of K^trans and K_ep was significantly higher than LMR.ConclusionsK^trans, K_ep, V_e, and ADC values can be used as imaging tools to differentiate bone metastases from prostate cancer and benign red marrow depositions of the pelvic bone.     

Dynamic contrast-enhanced MRI of gastric cancer: Correlations of the pharmacokinetic parameters with histological type,Lauren classification,and angiogenesis

PurposeTo compare the pharmacokinetic parameters derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in gastric cancers of different histological type and Lauren classification, and to investigate whether DCE-MRI parameters correlate with vascular endothelial growth factor (VEGF) expression levels in gastric cancer.MethodsIncluded were 32 patients with gastric cancer who underwent DCE-MRI of the upper abdomen before tumor resection. DCE-MRI parameters including the volume transfer coefficient (K^trans), reverse reflux rate constant (K_ep), and extracellular extravascular volume fraction (V_e) were calculated from the tumor region. Post-operative specimens were used for determination of histological differentiation (i.e., non-mucinous, mucinous, or signet-ring-cell adenocarcinoma) as well as Lauren classification (intestinal type or diffuse type). VEGF expression was examined for assessing angiogenesis. DCE-MRI parameters with different histological type and Lauren classification were compared using independent samples t-test and analysis of variance, respectively. Correlations between DCE-MRI parameters and VEGF expression grades were tested using Spearman correlation analysis.ResultsAmong gastric adenocarcinomas of three different histological types, mucinous adenocarcinomas showed a higher V_e and lower K^trans than the others (P < 0.01). Between the two Lauren classifications, the diffuse type showed a higher V_e than the intestinal type (P < 0.001). The mean K^trans showed a significantly positive correlation with VEGF (r = 0.762, P < 0.001).ConclusionDCE-MRI permits noninvasive prediction of tumor histological type and Lauren classification and estimation of tumor angiogenesis in gastric cancer. DCE-MRI parameters can be used as imaging biomarkers to predict the biologic aggressiveness of a tumor as well as patient prognosis.     

A comprehensive evaluation and impact of normalization of generalized tracer kinetic model parameters to characterize blood-brain-barrier permeability in normal-appearing and tumor tissue regions of patients with glioma

Rationale and objectivesTo comprehensively evaluate robustness and variations of DCE-MRI derived generalized-tracer-kinetic-model (GTKM) parameters in healthy and tumor tissues and impact of normalization in mitigating these variations on application to glioma.Materials (patients) and methodsA retrospective study included pre-operative 31 high-grade-glioma(HGG), 22 low-grade-glioma(LGG) and 33 follow-up data from 10 patients a prospective study with 4 HGG subjects. Voxel-wise GTKM was fitted to DCE-MRI data to estimate K^trans, v_e, v_b. Simulations were used to evaluate noise sensitivity. Variation of parameters with-respect-to arterial-input-function (AIF) variation and data length were studied. Normalization of parameters with-respect-to mean values in gray-matter (GM) and white-matter (WM) regions (GM-Type-2, WM-Type-2) and mean curves (GM-Type-1, WM-Type-1) were also evaluated. Co-efficient-of-variation(CoV), relative-percentage-error (RPE), Box-Whisker plots, bar graphs and t-test were used for comparison.ResultsGTKM was fitted well in all tissue regions. K^trans and v_e in contrast-enhancing (CE) has shown improved noise sensitivity in longer data. v_b was reliable in all tissues. Mean AIF and C(t) peaks showed ~38% and ~35% variations. During simulation, normalizations have mitigated variations due to changes in AIF amplitude in K^trans and v_b.. v_e was less sensitive to normalizations. CoV of K^trans and v_b has reduced ~70% after GM-Type-1 normalization and ~80% after GM-Type-2 normalization, respectively. GM-Type-1 (p = 0.003) and GM-Type-2 (p = 0.006) normalizations have significantly improved differentiation of HGG and LGG using K^trans.ConclusionK^trans and v_b can be reliably estimated in normal-appearing brain tissues and can be used for normalization of corresponding parameters in tumor tissues for mitigating inter-subject variability due to errors in AIF. Normalized K^trans and v_b provided improved differentiation of HGG and LGG.     

Dynamic contrast-enhanced magnetic resonance imaging parameters independent of baseline T10 values

A baseline T₁₀ value is typically needed for dynamic contrast-enhanced (DCE-) MRI studies. However, an assumed baseline T₁₀ has to be used when T₁₀ measurements for patients are not available. In this work, we systematically investigate the dependence on T₁₀ of the commonly used DCE-MRI parameters (K^trans, k_ep, v_e and IAUC) as well as several newly defined parameters [the normalized ratios (NRs) of k_ep, K^trans and v_e, which are measures of relative changes in these parameters between two time points] for a spoiled gradient-echo pulse sequence using simulations and in vivo studies. Effects of various factors on the T₁₀ dependence, such as the true T₁₀ value, flip angle and the potential changes in T₁₀ due to treatment, were also assessed using simulations. We found that DCE-MRI parameters k_ep and the NR of k_ep are largely independent of T₁₀, especially when larger flip angles are used (e.g., 30–40°). Their estimations therefore do not require any knowledge of T₁₀. The NRs of K^trans, v_e and IAUC also exhibit independence to T₁₀, but only when T₁₀ remains constant between pre- and posttreatment. The estimation of parameters themselves (K^trans, v_e and IAUC) is highly dependent on the T₁₀ value.     

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 (v_e, K^trans, k_ep) 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: v_e (0.65, 0.74, 0.58, 0.32), K^trans (0.30, 0.21, 0.13, 0.06), k_ep (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.     

Prediction of survival and progression in glioblastoma patients using temporal perfusion changes during radiochemotherapy

BackgroundThe aim of this study was to investigate changes in structural magnetic resonance imaging (MRI) according to the RANO criteria and perfusion- and permeability related metrics derived from dynamic contrast-enhanced MRI (DCE) and dynamic susceptibility contrast MRI (DSC) during radiochemotherapy for prediction of progression and survival in glioblastoma.MethodsTwenty-three glioblastoma patients underwent biweekly structural and perfusion MRI before, during, and two weeks after a six weeks course of radiochemotherapy. Temporal trends of tumor volume and the perfusion-derived parameters cerebral blood volume (CBV) and blood flow (CBF) from DSC and DCE, in addition to contrast agent capillary transfer constant (K^trans) from DCE, were assessed. The patients were separated in two groups by median survival and differences between the two groups explored. Clinical- and MRI metrics were investigated using univariate and multivariate survival analysis and a predictive survival index was generated.ResultsMedian survival was 19.2 months. A significant decrease in contrast-enhancing tumor size and CBV and CBF in both DCE- and DSC-derived parameters was seen during and two weeks past radiochemotherapy (p < 0.05). A 10%/30% increase in K^trans/CBF two weeks after finishing radiochemotherapy resulted in significant shorter survival (13.9/16.8 vs. 31.5/33.1 months; p < 0.05). Multivariate analysis revealed an index using change in K^trans and relative CBV from DSC significantly corresponding with survival time in months (r² = 0.843; p < 0.001).ConclusionsSignificant temporal changes are evident during radiochemotherapy in tumor size (after two weeks) and perfusion-weighted MRI-derived parameters (after four weeks) in glioblastoma patients. While DCE-based metrics showed most promise for early survival prediction, a multiparametric combination of both DCE- and DSC-derived metrics gave additional information.     

Comparison of tumor histology to dynamic contrast enhanced magnetic resonance imaging-based physiological estimates

Purpose

The purpose of this study was to compare histologically determined cellularity and extracellular space to dynamic contrast-enhanced magnetic resonance imaging (DCE MRI)-based maps of a two-compartment model's parameters describing tumor contrast agent extravasation, specifically tumor extravascular extracellular space (EES) volume fraction (v_e), tumor plasma volume fraction (v_p) and volume-normalized contrast agent transfer rate between tumor plasma and interstitium (K_TRANS/V_T).

Materials and Methods

Obtained v_e, v_p and K_TRANS/V_T maps were estimated from gadolinium diethylenetriamine penta-acetic acid DCE T₁-weighted gradient-echo images at resolutions of 469, 938 and 2500 μm. These parameter maps were compared at each resolution to histologically determined tumor type, and the high-resolution 469-μm maps were compared with automated cell counting using Otsu's method and a color-thresholding method for estimated intracellular (V_{intracellular}) and extracellular (V_{extracellular}) space fractions.

Results

The top five K_TRANS/V_T values obtained from each tumor at 469 and 938 μm resolutions are significantly different from those obtained at 2500 μm (P<.0001) and from one another (P=.0014). Using these top five K_TRANS/V_T values and the corresponding tumor EES volume fractions v_e, we can statistically differentiate invasive ductal carcinomas from noninvasive papillary carcinomas for the 469- and 938-μm resolutions (P=.0017 and P=.0047, respectively), but not for the 2500-μm resolution (P=.9008). The color-thresholding method demonstrated that v_e measured by DCE MRI is statistically similar to histologically determined EES. The V_{extracellular} obtained from the color-thresholding method was statistically similar to the v_e measured with DCE MRI for the top 10 K_TRANS/V_T values (P>.05). DCE MRI-based K_TRANS/V_T estimates are not statistically correlated with histologically determined cellularity.

Conclusion

DCE MRI estimates of tumor physiology are a limited representation of tumor histological features. Extracellular spaces measured by both DCE MRI and microscopic analysis are statistically similar. Tumor typing by DCE MRI is spatial resolution dependent, as lower resolutions average out contributions to voxel-based estimates of K_TRANS/V_T. Thus, an appropriate resolution window is essential for DCE MRI tumor diagnosis. Within this resolution window, the top K_TRANS/V_T values with corresponding v_e are diagnostic for the tumor types analyzed in this study.     

Flow versus permeability weighting in estimating the forward volumetric transfer constant (Ktrans) obtained by DCE-MRI with contrast agents of differing molecular sizes

PurposeTo quantify the differential plasma flow- (F_p-) and permeability surface area product per unit mass of tissue- (PS-) weighting in forward volumetric transfer constant (K^trans) estimates by using a low molecular (Gd-DTPA) versus high molecular (Gadomer) weight contrast agent in dynamic contrast enhanced (DCE) MRI.Materials and methodsDCE MRI was performed using a 7T animal scanner in 14 C57BL/6J mice syngeneic for TRAMP tumors, by administering Gd-DTPA (0.9 kD) in eight mice and Gadomer (35 kD) in the remainder. The acquisition time was 10 min with a sampling rate of one image every 2 s. Pharmacokinetic modeling was performed to obtain K^trans by using Extended Tofts model (ETM). In addition, the adiabatic approximation to the tissue homogeneity (AATH) model was employed to obtain the relative contributions of F_p and PS.ResultsThe K^trans values derived from DCE-MRI with Gd-DTPA showed significant correlations with both PS (r² = 0.64, p = 0.009) and F_p (r² = 0.57, p = 0.016), whereas those with Gadomer were found only significantly correlated with PS (r² = 0.96, p = 0.0003) but not with F_p (r² = 0.34, p = 0.111). A voxel-based analysis showed that K^trans approximated PS (< 30% difference) in 78.3% of perfused tumor volume for Gadomer, but only 37.3% for Gd-DTPA.ConclusionsThe differential contributions of F_p and PS in estimating K^trans values vary with the molecular weight of the contrast agent used. The macromolecular contrast agent resulted in K^trans values that were much less dependent on flow. These findings support the use of macromolecular contrast agents for estimating tumor vessel permeability with DCE-MRI.     

An analysis of the pharmacokinetic parameter ratios in DCE-MRI using the reference region model

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is performed by obtaining sequential MRI images, before, during and after the injection of a contrast agent. It is usually used to observe the exchange of contrast agent between the vascular space and extravascular extracellular space (EES), and provide information about blood volume and microvascular permeability. To estimate the kinetic parameters derived from the pharmacokinetic model, accurate knowledge of the arterial input function (AIF) is very important. However, the AIF is usually unknown, and it remains very difficult to obtain such information noninvasively. In this article, without knowledge of the AIF, we applied a reference region (RR) model to analyze the kinetic parameters. The RR model usually depends on kinetic parameters found in previous studies of a reference region. However, both the assignment of reference region parameters (intersubject variation) and the selection of the reference region itself (intrasubject variation) may confound the results obtained by RR methods. Instead of using literature values for those pharmacokinetic parameters of the reference region, we proposed to use two pharmacokinetic parameter ratios between the tissue of interest (TOI) and the reference region. Specifically, one parameter K_R is calculated as the ratio between the volume transfer constant K^trans of the TOI and RR. Similarly, another parameter V_R is calculated as the ratio between the extravascular extracellular volume fraction v_e of the TOI and RR. To investigate the consistency of the two ratios, the K^trans of the RR was varied ranging from 0.1 to 1.0 min⁻¹, covering the cited literature values. A simulated dataset with different levels of Gaussian noises and an in vivo dataset acquired from five canine brains with spontaneous occurring brain tumors were used to study the proposed ratios. It is shown from both datasets that these ratios are independent of K^trans of the RR, implying that there is potentially no need to obtain information about literature values from the reference region for future pharmacokinetic modeling and analysis.     

Feasibility of using limited-population-based average R10 for pharmacokinetic modeling of osteosarcoma dynamic contrast-enhanced magnetic resonance imaging data

Retrospective analyses of clinical dynamic contrast-enhanced (DCE) MRI studies may be limited by failure to measure the longitudinal relaxation rate constant (R₁) initially, which is necessary for quantitative analysis. In addition, errors in R₁ estimation in each individual experiment can cause inconsistent results in derivations of pharmacokinetic parameters, K^trans and v_e, by kinetic modeling of the DCE-MRI time course data. A total of 18 patients with lower extremity osteosarcomas underwent multislice DCE-MRI prior to surgery. For the individual R₁ measurement approach, the R₁ time course was obtained using the two-point R₁ determination method. For the average R₁₀ (precontrast R₁) approach, the R₁ time course was derived using the DCE-MRI pulse sequence signal intensity equation and the average R₁₀ value of this population. The whole tumor and histogram median K^trans (0.57±0.37 and 0.45±0.32 min⁻¹) and v_e (0.59±0.20 and 0.56±0.17) obtained with the individual R₁ measurement approach are not significantly different (paired t test) from those (K^trans: 0.61±0.46 and 0.44±0.33 min⁻¹; v_e: 0.61±0.19 and 0.55±0.14) obtained with the average R₁₀ approach. The results suggest that it is feasible, as well as practical, to use a limited-population-based average R₁₀ for pharmacokinetic modeling of osteosarcoma DCE-MRI data.     

Effects of arterial input function selection on kinetic parameters in brain dynamic contrast-enhanced MRI

PurposeKinetic parameters derived from dynamic contrast-enhanced MRI (DCE-MRI) were suggested as a possible instrument for multi-parametric lesion characterization, but have not found their way into clinical practice yet due to inconsistent results. The quantification is heavily influenced by the definition of an appropriate arterial input functions (AIF). Regarding brain tumor DCE-MRI, there are currently several co-existing methods to determine the AIF frequently including different brain vessels as sources. This study quantitatively and qualitatively analyzes the impact of AIF source selection on kinetic parameters derived from commonly selected AIF source vessels compared to a population-based AIF model.Material and methods74 patients with brain lesions underwent 3D DCE-MRI. Kinetic parameters [transfer constants of contrast agent efflux and reflux K^trans and k_ep and, their ratio, v_e, that is used to measure extravascular-extracellular volume fraction and plasma volume fraction v_p] were determined using extended Tofts model in 821 ROI from 4 AIF sources [the internal carotid artery (ICA), the closest artery to the lesion, the superior sagittal sinus (SSS), the population-based Parker model]. The effect of AIF source alteration on kinetic parameters was evaluated by tissue type selective intra-class correlation (ICC) and capacity to differentiate gliomas by WHO grade [area under the curve analysis (AUC)].ResultsArterial AIF more often led to implausible v_e > 100% values (p < 0.0001). AIF source alteration rendered different absolute kinetic parameters (p < 0.0001), except for k_ep. ICC between kinetic parameters of different AIF sources and tissues were variable (0.08–0.87) and only consistent > 0.5 between arterial AIF derived kinetic parameters. Differentiation between WHO III and II glioma was exclusively possible with v_p derived from an AIF in the SSS (p = 0.03; AUC 0.74).ConclusionThe AIF source has a significant impact on absolute kinetic parameters in DCE-MRI, which limits the comparability of kinetic parameters derived from different AIF sources. The effect is also tissue-dependent. The SSS appears to be the best choice for AIF source vessel selection in brain tumor DCE-MRI as it exclusively allowed for WHO grades II/III and III/IV glioma distinction (by v_p) and showed the least number of implausible v_e values.     

Early responses assessment of neoadjuvant chemotherapy in nasopharyngeal carcinoma by serial dynamic contrast-enhanced MR imaging

PurposeTo evaluate the feasibility of utilizing serial dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) prospectively for early prediction of neoadjuvant chemotherapy (NAC) response in nasopharyngeal carcinoma (NPC) patients.Materials and methodsSixty-three advanced NPC patients were recruited and received three DCE-MRI exams before treatment (Pre-Tx), 3 days (Day3-Tx) and 20 days (Day20-Tx) after initiation of chemotherapy (one NAC cycle). Early response to NAC was determined based on the third MRI scan and classified partial response (PR) as responders and stable disease (SD) as non-responders. After intensity-modulated radiotherapy (IMRT), complete response (CR) patients were classified as responders. The kinetic parameters (K^trans, K_ep, v_e, and v_p) derived from extended Tofts' model analysis and their corresponding changes ΔMetrics_(0–X) (X = 3 or 20 days) were compared between the responders and non-responders using the Student's T-test or Mann–Whitney U test.ResultsCompared to the SD group, the PR group after one NAC cycle presented significantly higher mean K^trans values at baseline (P = 0.011) and larger ΔK^trans_(0–3) and ΔK_ep(0–3) values (P = 0.003 and 0.031). For the above parameters, we gained acceptable sensitivity (range: 66.8–75.0%) and specificity (range: 60.0–66.7%) to distinguish the non-responders from the responders and their corresponding diagnosis efficacy (range: 0.703–0.767). The PR group patients after one NAC cycle showed persistent inhibition of tumor perfusion by NAC as explored by DCE-MRI parameters comparing to the SD group (P < 0.05) and presented a higher cure ratio after IMRT than those who did not (83.3% vs. 73.8%).ConclusionsThis primarily DCE-MRI based study showed that the early changes of the kinetic parameters during therapy were potential imaging markers to predicting response right after one NAC cycle for NPC patients.     

A new upper limit for the branching ratio of the decay K0S → e+e−

A new value of the upper limit for the branching ratio of the decay of K⁰_S into e⁺e⁻ has been experimentally obtained using a lead glass detector. The value is BR (K⁰_S → e⁺e⁻) < 1.1 × 10⁻⁴ (90% CL).     

Measurement of charged kaon semileptonic decay branching fraction K^ - \to e^ - \bar \nu _e \pi ^0 using ISTRA+ detector

The ratio of branching fractions for \(K^ - \to e^ - \bar \nu _e \pi ^0\) andK ^? → π^?π⁰ decays has been measured using the ISTRA+ spectrometer. The result of our measurement is the following: $$\mathcal{R}_{Ke_3 /K_{2\pi } } = 0.2423 \pm 0.0015(stat.) \pm 0.0037(syst.).$$ Using the current PDG value for the K _2π branching fraction, this result leads to the measured K _e3 branching fraction of Br(K _e3) = 0.0501 ± 0.0009 and to the value of |V _us |f ₊(0) = 0.2115 ± 0.0021.     

Quantitative diffusion-weighted imaging and dynamic contrast-enhanced MR imaging for assessment of tumor aggressiveness in prostate cancer at 3T

PurposeTo compare diffusion-weighted imaging (DWI) and dynamic contrast-enhanced MR imaging (DCE-MRI) for characterization of prostate cancer (PC).Methods104 PC patients who underwent prostate multiparametric MRI at 3T including DWI and DCE-MRI before MRI-guided biopsy or radical prostatectomy. Apparent diffusion coefficient (ADC) with histogram analysis (mean, 0–25th percentile, skewness, and kurtosis), intravoxel incoherent motion model including D and f; stretched exponential model including distributed diffusion coefficient (DDC) and a; and permeability parameters including K^trans, K_ep, and V_e were obtained from a region of interest placed on the dominant tumor of each patient.ResultsADC_mean, ADC_0–25, D, DDC, and V_e were significantly lower and K_ep was significantly higher in GS ≥ 3 + 4 tumors (n = 89) than in GS = 3 + 3 tumors (n = 15), and also in GS ≥ 4 + 3 tumors (n = 57) than in GS ≤ 3 + 4 tumors (n = 47) (P < 0.001 to P = 0.040). f was significantly lower in GS ≥ 4 + 3 tumors than in GS ≤ 3 + 4 tumors (P = 0.022), but there was no significant difference between GS = 3 + 3 tumors and GS ≥ 3 + 4 tumors, or between the remaining metrics in both comparisons. In metrics with area under the curve (AUC) >0.80, there was a significant difference in AUC between ADC_0–25 and D, and DDC for separating GS ≤ 3 + 4 tumors from GS ≥ 4 + 3 tumors (P = 0.040 and P = 0.022, respectively). There were no significant differences between metrics with AUC > 0.80 for separating GS = 3 + 3 tumors from GS ≥ 3 + 4 tumors. ADC_0–25 had the highest correlation with Gleason grade (ρ = −0.625, P < 0.001).ConclusionsDWI and DCE-MRI showed no apparent clinical superiority of non-Gaussian models or permeability MRI over the mono-exponential model for assessment of tumor aggressiveness in PC.