Diagnosis of suspicious breast lesions using an empirical mathematical model for dynamic contrast-enhanced MRI

The purpose of this study was to test whether an empirical mathematical model (EMM) of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can distinguish between benign and malignant breast lesions. A modified clinical protocol was used to improve the sampling of contrast medium uptake and washout. T(1)-weighted DCE magnetic resonance images were acquired at 1.5 T for 22 patients before and after injection of Gd-DTPA. Contrast medium concentration as a function of time was calculated over a small region of interest containing the most rapidly enhancing pixels. Then the curves were fitted with the EMM, which accurately described contrast agent uptake and washout. Results demonstrate that benign lesions had uptake (P<2.0 x 10(-5)) and washout (P<.01) rates of contrast agent significantly slower than those of malignant lesions. In addition, secondary diagnostic parameters, such as time to peak of enhancement, enhancement slope at the peak and curvature at the peak of enhancement, were derived mathematically from the EMM and expressed in terms of primary parameters. These diagnostic parameters also effectively differentiated benign from malignant lesions (P<.03). Conventional analysis of contrast medium dynamics, using a subjective classification of contrast medium kinetics in lesions as "washout," "plateau" or "persistent" (sensitivity=83%, specificity=50% and diagnostic accuracy=72%), was less effective than the EMM (sensitivity=100%, specificity=83% and diagnostic accuracy=94%) for the separation of benign and malignant lesions. In summary, the present research suggests that the EMM is a promising alternative method for evaluating DCE-MRI data with improved diagnostic accuracy.     

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.     

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.     

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.     

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.     

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.     

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.     

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.     

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.     

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.     

Assessment of extravascular extracellular space fraction in human melanoma xenografts by DCE-MRI and kinetic modeling

Tumor aggressiveness and response to therapy are influenced by the extravascular extracellular space fraction (EESF) of the malignant tissue. The EESF may, therefore, be an important prognostic parameter for cancer patients. The aim of this study was to investigate whether gadopentetate dimeglumine (Gd-DTPA)-based dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can be used to assess the EESF of tumors. Amelanotic human melanoma xenografts (A-07, R-18) were used as preclinical models of human cancer. Images of E.F (E is the initial extraction fraction of Gd-DTPA and F is perfusion) and lambda (the partition coefficient of Gd-DTPA) were obtained by Kety analysis of DCE-MRI data. Our study was based on the hypothesis that lambda is governed by the EESF and is not influenced significantly by microvascular density (MVD) or blood perfusion. To test this hypothesis, we searched for correlations between lambda and E.F, MVD or EESF by comparing lambda images with E.F images, histological preparations from the imaged tissue and the radial heterogeneity in EESF obtained by invasive imaging. Positive correlations were found between lambda and EESF. Thus, median lambda was larger in A-07 tumors than in R-18 tumors by a factor of 4.2 (P<.00001), consistent with the histological observation that EESF is approximately fourfold larger in A-07 tumors than in R-18 tumors. The radial heterogeneity in lambda in A-07 and R-18 tumors was almost identical to the radial heterogeneity in EESF. Moreover, lambda was larger in tissue regions with high EESF than in tissue regions with low EESF in A-07 tumors (P=.048). On the other hand, significant correlations between lambda and MVD or E.F could not be detected. Consequently, Kety analysis of Gd-DTPA-based DCE-MRI series of xenografted tumors provides lambda images that primarily reflect the EESF of the tissue.     

Differentiation of myeloma and metastatic cancer in the spine using dynamic contrast-enhanced MRI

Spinal myeloma and metastatic cancer cause similar symptoms and show similar imaging presentations, thus making them difficult to differentiate. In this study, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) was performed to differentiate between 9 myelomas and 22 metastatic cancers that present as focal lesions in the spine. The characteristic DCE parameters, including the peak signal enhancement percentage (SE%), the steepest wash-in SE% during the ascending phase and the wash-out SE%, were calculated by normalizing to the precontrast signal intensity. The two-compartmental pharmacokinetic model was used to obtain K^trans and k_ep. All nine myelomas showed the wash-out DCE pattern. Of the 22 metastatic cancers, 12 showed wash-out, 7 showed plateau, and 3 showed persistent enhancing patterns. The fraction of cases that showed the wash-out pattern was significantly higher in the myeloma group than the metastatic cancer group (9/9 = 100% vs. 12/22 = 55%, P = .03). Compared to the metastatic cancer group, the myeloma group had a higher peak SE% (226% ± 72% vs. 165% ± 60%, P = .044), a higher steepest wash-in SE% (169% ± 51% vs. 111% ± 41%, P = .01), a higher K^trans (0.114 ± 0.036 vs. 0.077 ± 0.028 1/min, P = .016) and a higher k_ep (0.88 ± 0.26 vs. 0.49 ± 0.23 1/min, P = .002). The receiver operating characteristic analysis to differentiate between these two groups showed that the area under the curve was 0.798 for K^trans, 0.864 for k_ep and 0.919 for combined K^trans and k_ep. These results show that DCE-MRI may provide additional information for making differential diagnosis to aid in choosing the optimal subsequent procedures or treatments for spinal lesions.     

Microcirculatory fraction (MCF(I)) as a potential imaging marker for tumor heterogeneity in breast cancer

Cancer is a heterogeneous disease by nature. Current imaging studies usually ignore intratumor variability in imaging biomarkers. We postulate that quantifying tumor heterogeneity with imaging techniques can provide useful information about cancer biology and potentially serve as novel imaging biomarkers. In this retrospective study, we identify a potential imaging marker, the microcirculatory fraction (MCF(I)), that quantifies tumor heterogeneity in normoxic/hypoxic cellular composition. We demonstrate its application on a test population of 22 women with stage II/III HER-2 negative breast cancer receiving antiangiogenic-cytotoxic combination neoadjuvant chemotherapy. Early change in MCF(I) (ΔMCF(I)) is assessed with dynamic contrast enhanced magnetic resonance imaging at the end of Cycle 2 and associated with pathologic response. Its performance is compared with other established volumetric imaging biomarkers (initial tumor volume and volume change) by statistical and graphic methods. We demonstrate that a significant (P<.01) difference in ΔMCF(I) can be detected between good (median ΔMCF(I) 0.27) and poor (median ΔMCF(I) -0.12) responders, despite the limited population size. Differences in the volumetric biomarkers are not statistically significant. Receiver operating characteristic analysis also shows that ΔMCF(I) is a good predictor for pathologic response (AUC=0.86, 95% CI 0.69-1.00, P<.01), while predictions made with the established volumetric biomarkers are not significantly better than random guesses. We conclude that ΔMCF(I) has the potential of being a better predictive biomarker for therapeutic response assessment. Our findings support our postulation that quantifying tumor heterogeneity with imaging techniques can provide additional information that can serve as novel biomarkers.     

Two-point method for kinetic analysis of a thermoluminescence glow peak

We present a method for the estimation of defect (trap) physical parameters from thermoluminescence (TL) glow peaks. In this method, the order of kinetics b is determined using two values of TL intensity each of which corresponds to the same temperature (T ₁) on two separate glow peaks of a phosphor. The two glow peaks are obtained from two aliquots of the phosphor irradiated to same dose but read out at different heating rates. The proposed method requires a minimum of only two data points in contrast to standard peak shape (PS) methods that require three points corresponding to three different temperatures on the same glow peak. Once the order of kinetics b is determined, the activation energy E is calculated by taking a second point (T ₂) on any one of the two glow peaks. The values of b and E thus obtained are used to evaluate the frequency factor S ^′′ and the number of trapped electrons before the heating begins n _o. The validity of the method was checked using two numerically generated glow peaks. For the two cases, the method reproduced the input values reasonably well. The method was also used to analyse two experimental glow peaks. The results obtained provide a reasonably good fit to the experimental data. The kinetic parameters calculated using the present technique are comparable to those calculated using PS and initial rise methods. Initial guesses can easily be obtained for E and S ^′′ using the present technique when a glow curve is to be deconvoluted with a model consisting of many unknown parameters with E and S″ inclusive.     

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.     

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.     

Reference region extraction by clustering for the pharmacokinetic analysis of dynamic contrast-enhanced MRI in prostate cancer

PurposeDynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) measures changes in the concentration of an administered contrast agent to quantitatively evaluate blood circulation in a tumor or normal tissues. This method uses a pharmacokinetic analysis based on the time course of a reference region, such as muscle, rather than arterial input function. However, it is difficult to manually define a homogeneous reference region. In the present study, we developed a method for automatic extraction of the reference region using a clustering algorithm based on a time course pattern for DCE-MRI studies of patients with prostate cancer.MethodsTwo feature values related to the shape of the time course were extracted from the time course of all voxels in the DCE-MRI images. Each voxel value of T1-weighted images acquired before administration were also added as anatomical data. Using this three-dimensional feature vector, all voxels were segmented into five clusters by the Gaussian mixture model, and one of these clusters that included the gluteus muscle was selected as the reference region.ResultsEach region of arterial vessel, muscle, and fat was segmented as a different cluster from the tumor and normal tissues in the prostate. In the extracted reference region, other tissue elements including scattered fat and blood vessels were removed from the muscle region.ConclusionsOur proposed method can automatically extract the reference region using the clustering algorithm with three types of features based on the time course pattern and anatomical data. This method may be useful for evaluating tumor circulatory function in DCE-MRI studies.     

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.