Computerized breast lesions detection using kinetic and morphologic analysis for dynamic contrast-enhanced MRI

To facilitate rapid and accurate assessment, this study proposed a novel fully automatic method to detect and identify focal tumor breast lesions using both kinetic and morphologic features from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). After motion registration of all phases of the DCE-MRI study, three automatically generated lines were used to segment the whole breast region of each slice. The kinetic features extracted from the pixel-based time-signal intensity curve (TIC) by a two-stage detection algorithm was first used, and then three-dimensional (3-D) morphologic characteristics of the detected regions were applied to differentiate between tumor and non-tumor regions. In this study, 95 biopsy-confirmed lesions (28 benign and 67 malignant lesions) in 54 women were used to evaluate the detection efficacy of the proposed system. The detection performance was analyzed using the free-response operating characteristics (FROC) curve and detection rate. The proposed computer-aided detection (CADe) system had a detection rate of 92.63% (88/95) of all tumor lesions, with 6.15 false positives per case. Based on the results, kinetic features extracted by TIC can be used to detect tumor lesions and 3-D morphology can effectively reduce the false positives.     

Computer-aided diagnosis of breast DCE-MRI using pharmacokinetic model and 3-D morphology analysis

Three-dimensional (3-D) dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) consists of a large number of images in different enhancement phases which are used to identify and characterize breast lesions. The purpose of this study was to develop a computer-assisted algorithm for tumor segmentation and characterization using both kinetic information and morphological features of 3-D breast DCE-MRI. An integrated color map created by intersecting kinetic and area under the curve (AUC) color maps was used to detect potential breast lesions, followed by the application of a region growing algorithm to segment the tumor. Modified fuzzy c-means clustering was used to identify the most representative kinetic curve of the whole segmented tumor, which was then characterized by using conventional curve analysis or pharmacokinetic model. The 3-D morphological features including shape features (compactness, margin, and ellipsoid fitting) and texture features (based on the grey level co-occurrence matrix) of the segmented tumor were obtained to characterize the lesion. One hundred and thirty-two biopsy-proven lesions (63 benign and 69 malignant) were used to evaluate the performance of the proposed computer-aided system for breast MRI. Five combined features including rate constant (k_ep), volume of plasma (v_p), energy (G₁), entropy (G₂), and compactness (C₁), had the best performance with an accuracy of 91.67% (121/132), sensitivity of 91.30% (63/69), specificity of 92.06% (58/63), and A_z value of 0.9427. Combining the kinetic and morphological features of 3-D breast MRI is a potentially useful and robust algorithm when attempting to differentiate benign and malignant lesions.     

Classification of breast mass lesions using model-based analysis of the characteristic kinetic curve derived from fuzzy c-means clustering

The purpose of this study is to evaluate the diagnostic efficacy of the representative characteristic kinetic curve of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) extracted by fuzzy c-means (FCM) clustering for the discrimination of benign and malignant breast tumors using a novel computer-aided diagnosis (CAD) system. About the research data set, DCE-MRIs of 132 solid breast masses with definite histopathologic diagnosis (63 benign and 69 malignant) were used in this study. At first, the tumor region was automatically segmented using the region growing method based on the integrated color map formed by the combination of kinetic and area under curve color map. Then, the FCM clustering was used to identify the time-signal curve with the larger initial enhancement inside the segmented region as the representative kinetic curve, and then the parameters of the Tofts pharmacokinetic model for the representative kinetic curve were compared with conventional curve analysis (maximal enhancement, time to peak, uptake rate and washout rate) for each mass. The results were analyzed with a receiver operating characteristic curve and Student's t test to evaluate the classification performance. Accuracy, sensitivity, specificity, positive predictive value and negative predictive value of the combined model-based parameters of the extracted kinetic curve from FCM clustering were 86.36% (114/132), 85.51% (59/69), 87.30% (55/63), 88.06% (59/67) and 84.62% (55/65), better than those from a conventional curve analysis. The A(Z) value was 0.9154 for Tofts model-based parametric features, better than that for conventional curve analysis (0.8673), for discriminating malignant and benign lesions. In conclusion, model-based analysis of the characteristic kinetic curve of breast mass derived from FCM clustering provides effective lesion classification. This approach has potential in the development of a CAD system for DCE breast MRI.     

A multiparametric approach to diagnosing breast lesions using diffusion-weighted imaging and ultrafast dynamic contrast-enhanced MRI

PurposeTo evaluate the diagnostic performance of a multiparametric approach to breast lesions including apparent diffusion coefficient (ADC) from diffusion-weighted images (DWI), maximum slope (MS) from ultrafast dynamic contrast enhanced (UF-DCE) MRI, lesion size, and patient's age.Materials and methodsIn total, 96 lesions (73 malignant, 23 benign) were evaluated. UF-DCE MRI was acquired using a prototype 3D-gradient-echo volumetric interpolated breath-hold examination (VIBE) with compressed sensing. Images were obtained up to 1 min after gadolinium injection. MS was calculated as the percentage relative enhancement/s. An ADC map was automatically generated from DWI at b = 0 and b = 1000 s/mm². MS and ADC values were measured by two radiologists independently. Interrater agreement was evaluated using intraclass correlation coefficients. Univariate and multivariate logistic regression analyses were performed using MS, ADC, lesion size, and the patient's age. The parameters of the prediction model were generated from the results of the multivariate logistic regression analysis. Area under the curve (AUC) was used to compare diagnostic performance of the prediction model and each parameter.ResultsInterrater agreements on MS and ADC were excellent (ICC 0.99 and 0.88, respectively). MS, ADC, and patient's age remained as significant parameters after univariate and multivariate logistic regression analysis. The prediction model using these significant parameters yielded an AUC of 0.90, significantly higher than that of MS (AUC 0.74, p = 0.01). The AUCs of ADC, MS, patient's age were 0.87, 0.74 and 0.73, respectively.ConclusionsA multiparametric model using ADC from DWI, MS from UF-DCE MRI, and patient's age showed excellent diagnostic performance, with greater contribution of ADC. Combining DWI and UF-DCE MRI might reduce scanning time while preserving diagnostic performance.     

A variational approach to image registration in dynamic contrast-enhanced MRI of the human kidney

Kidney function can be accessed by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) measurements which yield spatially resolved maps of physiological parameters like perfusion or filtration. The motion of the kidneys during the scan is a dominant limitation of the measurement quality, and image registration is necessary for accurate quantification. We analyzed the feasibility of applying an algorithm, originally developed for multimodal registration, to kidney perfusion time series. The algorithm uses a variational calculation scheme to align the images. In four out of five data sets, kidney motion could be reduced to below the spatial resolution of the images of 1.6 mm while preserving the enhancement pattern of kidney perfusion. Fitting a pharmacokinetic model to the data showed an average reduction of the Akaike fit error of 10% for the registered data, suggesting more stable parameters. We conclude that this image registration algorithm is feasible for correcting kidney motion in renal 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.     

Studies of pathology and VEGF expression in rabbit cerebrospinal fluid metastasis: application of dynamic contrast-enhanced MRI

Objective

The objective was to analyze the correlation of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) with vascular endothelial growth factor (VEGF) protein expression and to assess the potential application of DCE-MRI to the rabbit cerebrospinal fluid (CSF) metastasis model.

Methods

Thirty New Zealand rabbits were divided into experimental and control groups. In the experimental group, VX2 tumor cells were injected into the subarachnoid space at the plane of cisterna magna in 24 rabbits. In the control group, physiological saline was injected into the subarachnoid space at the plane of cisterna magna in six rabbits. DCE-MRI was performed at multiple time points, and several pharmacokinetic parameters, including K^trans, K_ep and V_e, were calculated. Also, VEGF levels in plasma and CSF were evaluated by enzyme-linked immunosorbent assay prior to DCE-MRI examination. After DCE-MRI examination, the rabbits were sacrificed, and the corresponding tumor specimens were harvested. Hematoxylin–eosin staining and VEGF immunohistochemical staining were carried out, and VEGF expression in the specimens was evaluated by the immunohistochemical scoring system.

Results

Vascular endothelial growth factor positive staining was localized in the cytoplasm and cell membranes of tumor cells, as well as in a subset of epithelial cells. Both VEGF immunohistochemical scores and VEGF expression in CSF and plasma exhibited positive correlations with K^trans and K_ep values as demonstrated by rank correlation statistical analysis.

Conclusions

Vascular endothelial growth factor expression in plasma and CSF in the CSF metastasis model was higher than in normal tissues. Therefore, DCE-MRI reliably indicated VEGF expression in the rabbit CSF metastasis model.     

Quantitative correlation between (1)H MRS and dynamic contrast-enhanced MRI of human breast cancer

Proton magnetic resonance spectroscopy (¹H MRS) and dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) provide functional information, including vascular volume, vascular permeability and choline (Cho) metabolism. In this study, we applied these two imaging modalities to quantitatively characterize 36 malignant breast lesions in 32 patients and analyzed the correlation between them. Cho concentration was quantified by single-voxel ¹H MRS using water as an internal reference. The measured Cho levels ranged from 0.32 to 10.47 mmol/kg, consistent with previously reported values. In 25 mass-type lesions, the Cho concentration was significantly correlated with tumor size (r=.69, P<.0002). In addition, the Cho level was found to be significantly higher in lesions presenting as mass-type lesions compared to non-mass-type diffuse enhancements (P=.035). The enhancement kinetics from tissues covered within each MRS voxel were measured and analyzed with a two-compartmental model to obtain pharmacokinetic parameters K^trans and k_ep. A significant correlation was found between the Cho level and the pharmacokinetic parameter k_ep (r=.62, P<.0001), indicating that tissues with a high Cho level have higher wash-out rates in DCE MRI. The results suggest a correlation between Cho metabolism and angiogenesis activity, which might be explained by the association of Cho with cell replication and angiogenesis required to support tumor growth.     

Water-selective spectral-spatial contrast-enhanced breast MRI for cancer detection in patients with extracapsular and injected free silicone

OBJECTIVE: This study investigates the use of contrast-enhanced, T1-weighted, water-selective spectral-spatial 3D gradient echo magnetic resonance imaging (MRI) with magnetization transfer (3DSSMT) for detecting breast cancer in patients with intraparenchymal silicone. CONCLUSION: Water-selective 3DSSMT provides superior fat and silicone suppression in patients with free silicone as compared with conventional fat saturation. It enables direct, high-quality, high-spatial-resolution, T1-weighted breast MRI of contrast enhancement without the need for subtraction processing and aids diagnosis of cancer in the breast with free silicone.     

Examining the acute effects of cediranib (RECENTIN, AZD2171) treatment in tumor models: a dynamic contrast-enhanced MRI study using gadopentate

Cediranib (RECENTIN, AZD2171) is a highly potent inhibitor of the tyrosine kinase activity associated with all three vascular endothelial growth factor (VEGF) receptors and is currently in Phase II/III clinical trials. Preclinically, cediranib inhibits VEGF signaling and angiogenesis in vivo and impedes solid tumor growth significantly. Clinically, changes observed using dynamic contrast-enhanced MRI (DCE-MRI) with gadopentate suggest that acute cediranib treatment compromises tumor hemodynamics. In this study, a DCE-MRI baseline scan using gadopentate was performed in nude rats bearing Lovo (human colorectal carcinoma) or C6 (rat glioma) tumors. Cediranib (3 mg/kg per day) or vehicle was then dosed orally (2, 26 and 50 h after the baseline scan; 12 rats per group) and a second scan acquired 2 h after the final dosing event. Mean values for K(trans) (Tofts and Kermode-derived) [Magn Reson Med 17 (1991) 357-67] and the initial area under the gadolinium concentration curve over the first 60 s (iAUC) were reduced significantly following cediranib treatment: K(trans) by 33% (P<.05) in both tumor models and iAUC by 23% (P>.05) and 33% (P>.005) in Lovo and C6, respectively. This is the first preclinical investigation to examine the effect of cediranib treatment on tumors by DCE-MRI with gadopentate.     

A comparison of two methods for estimating DCE-MRI parameters via individual and cohort based AIFs in prostate cancer: A step towards practical implementation

Multi-parametric Magnetic Resonance Imaging, and specifically Dynamic Contrast Enhanced (DCE) MRI, play increasingly important roles in detection and staging of prostate cancer (PCa). One of the actively investigated approaches to DCE MRI analysis involves pharmacokinetic (PK) modeling to extract quantitative parameters that may be related to microvascular properties of the tissue. It is well-known that the prescribed arterial blood plasma concentration (or Arterial Input Function, AIF) input can have significant effects on the parameters estimated by PK modeling. The purpose of our study was to investigate such effects in DCE MRI data acquired in a typical clinical PCa setting. First, we investigated how the choice of a semi-automated or fully automated image-based individualized AIF (iAIF) estimation method affects the PK parameter values; and second, we examined the use of method-specific averaged AIF (cohort-based, or cAIF) as a means to attenuate the differences between the two AIF estimation methods.     

AG-013736, a novel inhibitor of VEGF receptor tyrosine kinases, inhibits breast cancer growth and decreases vascular permeability as detected by dynamic contrast-enhanced magnetic resonance imaging

Dynamic contrast-enhanced MRI (DCE-MRI) was used to noninvasively evaluate the effects of AG-03736, a novel inhibitor of vascular endothelial growth factor (VEGF) receptor tyrosine kinases, on tumor microvasculature in a breast cancer model. First, a dose response study was undertaken to determine the responsiveness of the BT474 human breast cancer xenograft to AG-013736. Then, DCE-MRI was used to study the effects of a 7-day treatment regimen on tumor growth and microvasculature. Two DCE-MRI protocols were evaluated: (1) a high molecular weight (MW) contrast agent (albumin-(GdDTPA)(30)) with pharmacokinetic analysis of the contrast uptake curve and (2) a low MW contrast agent (GdDTPA) with a clinically utilized empirical parametric analysis of the contrast uptake curve, the signal enhancement ratio (SER). AG-013736 significantly inhibited growth of breast tumors in vivo at all doses studied (10-100 mg/kg) and disrupted tumor microvasculature as assessed by DCE-MRI. Tumor endothelial transfer constant (K(ps)) measured with albumin-(GdDTPA)(30) decreased from 0.034+/-0.005 to 0.003+/-0.001 ml min(-1) 100 ml(-1) tissue (P<.0022) posttreatment. No treatment-related change in tumor fractional plasma volume (fPV) was detected. Similarly, in the group of mice studied with GdDTPA DCE-MRI, AG-013736-induced decreases in tumor SER measures were observed. Additionally, our data suggest that 3D MRI-based volume measurements are more sensitive than caliper measurements for detecting small changes in tumor volume. Histological staining revealed decreases in tumor cellularity and microvessel density with treatment. These data demonstrate that both high and low MW DCE-MRI protocols can detect AG-013736-induced changes in tumor microvasculature. Furthermore, the correlative relationship between microvasculature changes and tumor growth inhibition supports DCE-MRI methods as a biomarker of VEGF receptor target inhibition with potential clinical utility.     

A nonrigid registration algorithm for longitudinal breast MR images and the analysis of breast tumor response

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can estimate parameters relating to blood flow and tissue volume fractions and therefore may be used to characterize the response of breast tumors to treatment. To assess treatment response, values of these DCE-MRI parameters are observed at different time points during the course of treatment. We propose a method whereby DCE-MRI data sets obtained in separate imaging sessions can be co-registered to a common image space, thereby retaining spatial information so that serial DCE-MRI parameter maps can be compared on a voxel-by-voxel basis. In performing inter-session breast registration, one must account for patient repositioning and breast deformation, as well as changes in tumor shape and volume relative to other imaging sessions. One challenge is to optimally register the normal tissues while simultaneously preventing tumor distortion. We accomplish this by extending the adaptive bases algorithm through adding a tumor-volume preserving constraint in the cost function. We also propose a novel method to generate the simulated breast magnetic resonance (MR) images, which can be used to evaluate the proposed registration algorithm quantitatively. The proposed nonrigid registration algorithm is applied to both simulated and real longitudinal 3D high resolution MR images and the obtained transformations are then applied to lower resolution physiological parameter maps obtained via DCE-MRI. The registration results demonstrate the proposed algorithm can successfully register breast MR images acquired at different time points and allow for analysis of the registered parameter maps.     

Intravoxel incoherent motion diffusion-weighted imaging as an adjunct to dynamic contrast-enhanced MRI to improve accuracy of the differential diagnosis of benign and malignant breast lesions

PurposeTo investigate the value of use of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) as an adjunct to dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to distinguish benign from malignant breast lesions.Materials and methodsRetrospective analysis of data pertaining to 117 patients with breast lesions who underwent DCE-MRI and IVIM-DWI examination with 3.0 T MRI was conducted. A total of 128 lesions were pathologically confirmed (47 benign and 81 malignant). Between-group differences in DCE-MRI parameters (Morphology, enhancement pattern, maximum slope of increase (MSI) and time–signal curve (TIC) type) and IVIM-DWI parameters (f value, D value and D* value) were assessed. Multivariate logistic regression was performed to identify variables that distinguished benign from malignant breast lesions. The diagnostic performance of DCE-MRI and DCE-MRI plus IVIM-DWI, to distinguish benign from malignant breast lesions, was evaluated using pathology results as the gold standard.ResultsLesion morphology, MSI, and TIC type (P < 0.05), but not the enhancement pattern (P > 0.05), were significantly different between the benign and malignant groups. The f (8.53 ± 2.14) and D* (7.64 ± 2.07) values in the malignant group were significantly higher than those in the benign group (7.68 ± 1.97 and 6.83 ± 2.13, respectively), while the D value (0.99 ± 0.22) was significantly lower than that (1.34 ± 0.17) in the benign group (P < 0.05 for all). On logistic regression analysis, the sensitivity, specificity and accuracy of DCE-MRI were 90.1%, 70.2% and 82.8% respectively; the corresponding figures for the combination of IVIM-DWI and DCE-MRI were 88.8%, 85.1%, and 87.5%respectively.ConclusionIVIM-DWI method as an adjunct to DCE-MRI can improve the specificity and accuracy in differential diagnosis of benign and malignant lesions of breast.     

Development of an empirical kinetic model for sonocatalytic process using neodymium doped zinc oxide nanoparticles

The degradation of Acid Blue 92 (AB92) solution was investigated using a sonocatalytic process with pure and neodymium (Nd)-doped ZnO nanoparticles. The nanoparticles were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), and X-ray photoelectron spectroscopy (XPS). The 1% Nd-doped ZnO nanoparticles demonstrated the highest sonocatalytic activity for the treatment of AB92 (10 mg/L) with a degradation efficiency (DE%) of 86.20% compared to pure ZnO (62.92%) and sonication (45.73%) after 150 min. The results reveal that the sonocatalytic degradation followed pseudo-first order kinetics. An empirical kinetic model was developed using nonlinear regression analysis to estimate the pseudo-first-order rate constant (k_app) as a function of the operational parameters, including the initial dye concentration (5–25 mg/L), doped-catalyst dosage (0.25–1 g/L), ultrasonic power (150–400 W), and dopant content (1–6% mol). The results from the kinetic model were consistent with the experimental results (R² = 0.990). Moreover, DE% increases with addition of potassium periodate, peroxydisulfate, and hydrogen peroxide as radical enhancers by generating more free radicals. However, the addition of chloride, carbonate, sulfate, and t-butanol as radical scavengers declines DE%. Suitable reusability of the doped sonocatalyst was proven for several consecutive runs. Some of the produced intermediates were also detected by GC–MS analysis. The phytotoxicity test using Lemna minor (L. minor) plant confirmed the considerable toxicity removal of the AB92 solution after treatment process.     

Comparison of analytical and numerical analysis of the reference region model for DCE-MRI

This study compared three methods for analyzing DCE-MRI data with a reference region (RR) model: a linear least-square fitting with numerical analysis (LLSQ-N), a nonlinear least-square fitting with numerical analysis (NLSQ-N), and an analytical analysis (NLSQ-A). The accuracy and precision of estimating the pharmacokinetic parameter ratios K_R and V_R, where K_R is defined as a ratio between the two volume transfer constants, K^trans,TOI and K^trans,RR, and V_R is the ratio between the two extracellular extravascular volumes, v_e,TOI and v_e,RR, were assessed using simulations under various signal-to-noise ratios (SNRs) and temporal resolutions (4, 6, 30, and 60 s). When no noise was added, the simulations showed that the mean percent error (MPE) for the estimated K_R and V_R using the LLSQ-N and NLSQ-N methods ranged from 1.2% to 31.6% with various temporal resolutions while the NLSQ-A method maintained a very high accuracy (< 1.0×10^− 4 %) regardless of the temporal resolution. The simulation also indicated that the LLSQ-N and NLSQ-N methods appear to underestimate the parameter ratios more than the NLSQ-A method. In addition, seven in vivo DCE-MRI datasets from spontaneously occurring canine brain tumors were analyzed with each method. Results for the in vivo study showed that K_R (ranging from 0.63 to 3.11) and V_R (ranging from 2.82 to 19.16) for the NLSQ-A method were both higher than results for the other two methods (K_R ranging from 0.01 to 1.29 and V_R ranging from 1.48 to 19.59). A temporal downsampling experiment showed that the averaged percent error for the NLSQ-A method (8.45%) was lower than the other two methods (22.97% for LLSQ-N and 65.02% for NLSQ-N) for K_R, and the averaged percent error for the NLSQ-A method (6.33%) was lower than the other two methods (6.57% for LLSQ-N and 13.66% for NLSQ-N) for V_R. Using simulations, we showed that the NLSQ-A method can estimate the ratios of pharmacokinetic parameters more accurately and precisely than the NLSQ-N and LLSQ-N methods over various SNRs and temporal resolutions. All simulations were validated with in vivo DCE MRI data.     

Assessment of peripheral tissue perfusion disorder in streptozotocin-induced diabetic rats using dynamic contrast-enhanced MRI

Purpose

To assess peripheral tissue perfusion disorder in streptozotocin (STZ)-induced diabetic rats by using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).

Materials and Methods

A rat diabetes model was produced by intravenous injection of STZ. Diabetic rats were sustainably treated with either saline or insulin using an Alzet osmotic pump. Hind paw tissue perfusion was measured by signal intensity (SI) enhancement after gadolinium diethylenetriaminepentaacetic acid injection in DCE-MRI study and quantified using the initial area under the SI-time curve (IAUC). Peripheral tissue uptake of [¹⁴C]iodoantipyrine (IAP) was also determined as a marker of tissue blood flow for comparison with the IAUC value indicating tissue perfusion.

Results

STZ caused hyperglycemia at 1 and 2 weeks after injection. Treatment with insulin significantly alleviated hyperglycemia. At 2 weeks after STZ injection, peripheral tissue perfusion was clearly reduced in the diabetic rats and its reduction was significantly improved in the insulin-treated diabetic rats. Tissue perfusion evaluated by DCE-MRI was similar to the tissue blood flow measured by [¹⁴C]IAP uptake.

Conclusion

Our findings demonstrated that DCE-MRI can assess peripheral tissue perfusion disorder in diabetes. DCE-MRI could be suitable for noninvasive evaluation of peripheral tissue perfusion in both preclinical and clinical studies. It may also be useful for developing novel drugs to protect against diabetic vascular complications.     

Diagnostic value of breast MRI for predicting metastatic axillary lymph nodes in breast cancer patients: diffusion-weighted MRI and conventional MRI

Purposes

To evaluate the diagnostic value of diffusion-weighted MRI (DWI) and combination of conventional MRI and DWI to predict metastatic axillary lymph nodes in breast cancer.

Materials and methods

Two hundred fifty-two breast cancer patients with 253 axillae were included. The morphological parameters on axial T2-weighted images without fat saturation and apparent diffusion coefficient (ADC) values were retrospectively analyzed. An independent t-test/chi-square test and receiver operating characteristics (ROC) curve analysis were used.

Results

On conventional MRI, short and long axis length, maximal cortical thickness, relative T2 value, loss of fatty hilum (p < 0.001 for each), and eccentric cortical thickening (p < 0.003) were statistically significantly different between the metastatic and nonmetastatic groups. The short axis to long axis ratio was not a statistically significant parameter. The ADC value was significantly different between the 2 groups, with an AUC that was higher than that of conventional MR parameters (AUC, 0.815; threshold, ≤ 0.986 × 10–3 mm²/sec; sensitivity, 75.8%; specificity, 83.9%). Using the adopted thresholds for each parameter, a total number of findings suggesting malignancy of 4 or higher was determined as the threshold, with high specificity (90.1%).

Conclusion

Using conventional MRI and DWI, we can evaluate the axilla in breast cancer with high specificity.     

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.     

Role of apparent diffusion coefficient values for the differentiation of viable and necrotic areas of breast cancer and its potential utility to guide voxel positioning for MRS in the absence of dynamic contrast-enhanced MRI data

We carried out retrospective analysis of apparent diffusion coefficient (ADC) values in 48 infiltrating ductal breast cancer patients who had dynamic contrast-enhanced magnetic resonance imaging (DCEMRI; Group I) and in 53 patients (Group II) for whom DCEMRI data were not available. Twenty-three patients of Group I showed no necrosis (Group Ia), while in 25 patients, both viable (nonnecrotic) and necrotic tumor areas (Group Ib) were observed on DCEMRI. T1-weighted, fat-suppressed and short inversion recovery images were used to identify the viable and necrotic tumor areas in Group II patients, and necrosis was not seen in 11 patients (Group IIa), while 42 (Group IIb) showed both viable and necrotic tumor areas. The ADCs of the necrotic area of Group Ib (1.79±0.30 ×10(-3) mm(2)/s) and Group IIb (1.83±0.40 ×10(-3) mm(2)/s) patients were similar and significantly higher (P<.01) compared to the ADCs of the viable tumor area of Group Ia (0.96±0.21 ×10(-3) mm(2)/s) and Group IIa (0.90±0.17 ×10(-3) mm(2)/s) patients. Proton MR spectroscopy (MRS) data were also available in these patients, and the ADC values were retrospectively determined from the voxel from which MR spectrum was obtained. These values were compared with the ADC obtained for the viable and necrotic areas of the tumor. ADC of the MRS voxel was similar to that obtained for the viable tumor area in patients of both groups. This interesting observation reveals the potential utility of using ADC values to identify viable tumor area for positioning of voxel for MRS in the absence of DCEMRI data.