Postcontrast Series Research Articles

Does MRI help in the assessment of inflammatory breast disorders?

ObjectiveThe aim of work is to evaluate the role of magnetic resonance imaging in the assessment of different entities of inflammatory breast disorders. Materials and methodsEighty-one non-lactating patients with mastitis had been evaluated by post contrast MR imaging; their ages ranged from 22 to 70years (Average age: 41.6years). MRI data analysis was performed regarding qualitative criteria and parametric color maps for image post processing. Diagnosis was made up regarding resolution in follow up sessions for simple mastitis and biopsy were done for cases with suspicious imaging findings. ResultsMammary interstitial edema with no masses or abnormal enhancement was elicited in 28.4% (n=23/81). T2-weighed and post contrast series were the sequences of choice to rely upon in evaluation. T2 had demonstrated lesions in 64.2% (n=52/81) and contrast uptake was detected in 68% (n=55/81). Quantitative analysis was overlapping.MR imaging had displayed sensitivity, specificity and accuracy of 87.5%, 72.3%, and 75.4% respectively in differentiating benign forms of mastitis from malignant ones. ConclusionIn inflammatory breast disorders, MR imaging can precisely delineate disease extent and monitor response to therapy. Immediate distinction between infectious/non-infectious and malignant mastitis is difficult to be obtained.

Virtual unenhanced second generation dual-source CT of the liver: Is it time to discard the conventional unenhanced phase?

Dual-energy dual source CT can almost simultaneously image patients using two different tube potentials, allowing material decomposition and creation of 'virtual unenhanced' (VU) images from post-contrast series. 75 patients undergoing triple-phase liver CT examinations were imaged using a second generation dual-source CT machine with tube potentials 140/100 kVp. Post-processing VU series were derived from arterial and portal phases. Regions-of-interest from liver parenchyma and within fat ('noise' assessment) were drawn to compare VU series to conventional unenhanced (CU) series. Subjective analysis assessed image quality and the suitability of VU to replace CU series. Mean Hounsfield unit (HU) values of liver were higher in the VU series: portal 51.9 (SD = 10.29), arterial 51.1 (SD = 10.05), compared to the CU series 49.2 (SD = 9.11); P<0.001. However, Pearson's correlation of the VU and CU series remained excellent: 0.838 (portal), 0.831 (arterial). Bland-Altman plots also showed good agreement between both VU and the CU datasets. Noise measurements were significantly lower in both VU series (P<0.001). For subjective analysis, image quality was rated as very good/excellent in 100% of CU images, 93.3% of portal VU and 88.7% of arterial VU series. Overall, portal VU and arterial VU images were acceptable replacements for the CU series in 97.4% and 96.1%, respectively. Post-processing was noted to create a number of artefacts in VU images--knowledge of these is essential for interpretation. Portal and arterial-derived VU images objectively correlate to CU images and demonstrate good image quality and acceptability. VU image sets could replace the conventional unenhanced images in the vast majority of cases, significantly reducing radiation dose.

Dynamic Breast MRI in the Course of Neoadjuvant Chemotherapy: Standardized Evaluation of Tumor Size and Enhancement Parameters in Correlation to Different Histopathologic Characteristics

<h3>Rationale and Objectives</h3> Basic exploratory data analysis to evaluate enhancement and tumor size (SIZE) in contrast-enhanced breast magnetic resonance imaging (CE-MRI) during chemotherapy. Correlation with histopathology (human epidermal growth factor receptor (HER2/neu) status and estrogen receptor (ER) score). <h3>Materials and Methods</h3> Sixty-five women (mean age 47 ± 10 years) with locally advanced breast cancer (mean SIZE 25 mL) had CE-MRI (three-dimensional fast low angle shot (FLASH); repetition time=9.1 ms, echo time=4.8 ms, flip angle (FA) 25°, matrix size 256 × 256 pixels, field of view 350 mm, slice thickness 2 mm, number of slices=32, one precontrast and five postcontrast series) before and after chemotherapy. Lesion segmentation and subsequent SIZE and enhancement analysis including maximum enhancement (MAX), area under the curve (AUC), time-to-peak (TTP), and maximum upslope (MUS) were performed. Correlation with histopathology (ER score and HER2/neu status). <h3>Results</h3> SIZE reduced significantly during therapy (25 mL vs. 5 mL, <i>P</i> < .0001). AUC, MAX, MUS decreased (<i>P</i> < .0001), TTP increased (<i>P</i> < .0001). SIZE and MAX were independent parameters (<i>r²</i> = .22). No correlation (<i>P</i> > .01) in any of the parameters with either ER score or HER2/neu status was found. HER2/neu score equal 2+pos. or 3+ showed significantly stronger changes in SIZE (<i>P</i> < .01), MAX (<i>P</i> < .01) and AUC (<i>P</i> < .05) compared to lower HER2/neu score (0 to 2+neg.). <h3>Conclusions</h3> From routine MRI protocol and semiquantitative analysis of signal enhancement curves, information about size, and hemodynamic status of tumors under treatment may be extracted. Reduction in size and maximum enhancement were complementary parameters. In the course of therapy, size and enhancement may develop differently in clinically relevant histopathological subgroups.

WE‐D‐304A‐05: Analysis of Tumor Grade Breast Carcinoma Using Computer‐Extracted Morphological and Kinetic Features in DCE‐MRI

Purpose: An important prognostic marker in breast cancer is the histological grade of the breast tumor. Thus, the purpose of our study is to investigate the performance of computer‐extracted morphological and kinetic features in DCE‐MRI in distinguishing breast tumors with varying histological grades: Grade 1, Grade 2, and Grade 3. Materials and Methods: Breast MR images were obtained with a T1‐weighted SPGR sequence using Gd‐DTPA on a 1.5T GE MRI scanner. Each case has one precontrast and three to five postcontrast series at intervals of 68 seconds, and each series contains 60 coronal slices. The database contains 135 invasive ductal carcinoma (IDC) breast lesions including 21 Grade 1 lesions, 72 Grade 2 lesions, 42 Grade 3 lesions, and 133 benign lesions (no cysts). All lesions were verified pathologically. Each lesion was segmented and its characteristic kinetic curve was extracted using the fuzzy c‐means method. Textural, morphological, kinetic, and spatial enhancement variance features were extracted, and stepwise linear discriminant analysis using a Wilks lambda cost function in a round‐robin fashion was used for feature selection. The selected features were merged using Bayesian neural network, and the classification performance was evaluated using receiver‐operating characteristics (ROC) analysis. Results: We achieved AUC values of 0.80±0.05, 0.85±0.02, 0.75±0.06, 0.74±0.05, 0.75±0.06, and 0.60±0.05 for the classification tasks of Grade 1 vs. benign, malignant (Grades 1–3 lesions) vs. benign, Grade 1 vs. Grade 3 lesions, Grade 1 vs. Grades 2 and 3 lesions, Grade 1 vs. Grade 2, and Grade 2 vs. Grade 3, respectively. Conclusions: Computerized analyses of breast MR images have the potential to yield image‐based prognostic markers for breast carcinoma, specifically in distinguishing different histological grades of breast tumors.

SU‐HH‐AUD C‐07: Classification of Breast Carcinoma Subtypes Using Computer‐Extracted Morphological and Kinetic Features in DCE‐MRI

Purpose: To assess the performance of computer‐extracted morphological and kinetic features in differentiating different types of breast lesions, specifically ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC), in DCE‐MRI. Method and Materials: Breast MR images were obtained with a T1‐weighted SPGR sequence using Gd‐DTPA on a 1.5T GE MRI scanner. Each case has one precontrast and five postcontrast series at intervals of 68 seconds, and each series contains 60 coronal slices. The database contains 83 benign breast lesions and 98 malignant breast lesions including 24 DCIS, 32 IDC, 18 combined DCIS/IDC, 10 invasive lobular carcinoma (ILC) and 14 non‐categorizable lesions. All lesions were verified pathologically. Only pure DCIS and IDC lesions were used. Each lesion was segmented and its characteristic kinetic curve was extracted using the fuzzy c‐means method. Twenty‐nine features including textural, morphological, kinetic, and variance kinetic features were extracted and stepwise linear discriminant analysis using a Wilks lambda cost function was used for feature selection. The selected features were merged using round‐robin Bayesian neural network and the classification performance was evaluated using receiver‐operating characteristics (ROC) analysis. Results: Distinguishing DCIS lesions from IDC lesions using selected features gave an AUC value of 0.95. AUC values of 0.85 and 0.87 were obtained in differentiating between DCIS and benign lesions, and between IDC and benign lesions with merged features, respectively. Conclusion: Computer‐aided diagnosis for breast MRI can be extended from malignant vs. benign classification to distinguishing different subcategories of breast carcinomas, in particular DCIS and IDC.

Classification of Small Contrast Enhancing Breast Lesions in Dynamic Magnetic Resonance Imaging Using a Combination of Morphological Criteria and Dynamic Analysis Based on Unsupervised Vector-Quantization

To evaluate the diagnostic value of breast magnetic resonance imaging (MRI) in small focal lesions using dynamic analysis based on unsupervised vector quantization in combination with a score for morphologic criteria. We examined 85 mammographically indetermintate lesions (BIRADS 3-4; 47 malignant, mean lesion size 1.2 cm; 38 benign, mean lesion size 1.1 cm). MRI was performed with a dynamic T1-weighted gradient echo sequence (1 precontrast and 5 postcontrast series). Lesions with an initial contrast enhancement >/=50% were selected with semiautomatic segmentation. For conventional dynamic analysis, we calculated the mean initial signal increase and postinitial course of all voxels included in a lesion. Secondly, all voxels within the lesions were assigned to 4 clusters using minimal-free-energy vector quantization. Dynamic and morphologic criteria were summarized in a diagnostic score and evaluated by receiver operating characteristic analysis. In the present collection of small lesions, morphologic criteria [area under the curve (AUC) = 0.610] were inferior to dynamic criteria in the detection of breast cancer. Dynamic analysis with vector quantization (AUC = 0.760) presented slightly better results compared with standard dynamic analysis (AUC = 0.693). There was no benefit for combined morphologic and dynamic analysis. In small MR-mammographic lesions, dynamic analysis with vector quantization alone tends to result in a higher diagnostic accuracy compared with combined morphologic and dynamic analysis.

Prostate Cancer: Added Value of Subtraction Dynamic Imaging in 3T Magnetic Resonance Imaging with a Phased-array Body Coil

PurposeTo determine the added value of dynamic subtraction magnetic resonance (MR) imaging for the localization of prostate cancer.Materials and MethodsWe examined 21 consecutive patients who underwent MR imaging in 3T unit with a phased-array body coil and then had radical prostatectomy. After T2-weighted fast spin-echo imaging, we performed a contrast-enhanced dynamic 3D gradient-echo imaging consisting of pre-contrast, 2 successive early-phased (first imaging was started just after the appearance of contrast material in the aortic bifurcation followed by second imaging 35 seconds after the initiation of first imaging) and one 5-minute delayed post-contrast series. Subtraction of pre-contrast images from corresponding post-contrast images of each phase was performed on the console.ResultsOn ROC analysis, the overall accuracy (Az value) of dynamic imaging combined with subtraction imaging was higher than T2-weighted imaging (p = 0.001) or conventional dynamic imaging alone (p = 0.074) for localization of cancer foci regardless of their zonal locations. Among pathologically verified 81 lesions, the mean volume of detected lesions with the subtraction images (n = 49, 0.69 cm3) was smaller than with T2-weighted images (n = 14, 1.05 cm3) or conventional dynamic images (n = 43, 0.71 cm3).ConclusionFor localization of small prostate cancer, additional subtraction for the dynamic imaging could be superior to both T2-weighted imaging and un-subtracted dynamic imaging.

1135: Development of a Three-Dimensional Anisotropic Pelvic Lymph Node Clinical Target Volume and Planning Target Volume Using Magnetic Resonance Lymphadenography With Ferumoxtran-10

1135: Development of a Three-Dimensional Anisotropic Pelvic Lymph Node Clinical Target Volume and Planning Target Volume Using Magnetic Resonance Lymphadenography With Ferumoxtran-10

Pulmonary nodule characterization: A comparison of conventional with quantitative and visual semi-quantitative analyses using contrast enhancement maps

Purpose To determine whether conventional nodule densitometry or analysis based on contrast enhancement maps of indeterminate lung nodules imaged with contrast-enhanced CT can distinguish benign from malignant lung nodules. Materials and method Thin section, contrast-enhanced CT (baseline, and post-contrast series acquired at 45, 90,180, and 360 s) was performed on 29 patients with indeterminate lung nodules (14 benign, 15 malignant). A thoracic radiologist identified the boundary of each nodule using semi-automated contouring to form a 3D region-of-interest (ROI) on each image series. The post-contrast series having the maximum mean enhancement was then volumetrically registered to the baseline series. The two series were subtracted volumetrically and the subtracted voxels were quantized into seven color-coded bins, forming a contrast enhancement map (CEM). Conventional nodule densitometry was performed to obtain the maximum difference in mean enhancement values for each nodule from a circular ROI. Three thoracic radiologists performed visual semi-quantitative analysis of each nodule, scoring each map for: (a) magnitude and (b) heterogeneity of enhancement throughout the entire volume of the nodule on a five-point scale. Receiver operator characteristic (ROC) analysis was conducted on these features to evaluate their diagnostic efficacy. Finally, 14 quantitative texture features were calculated for each map. A statistical analysis was performed to combine the 14 texture features to a single factor. ROC analysis of the derived aggregate factor was done as an indicator of malignancy. All features were analyzed for differences between benign and malignant nodules. Results Using 15 HU as a threshold, 93% (14/15) of malignant and 79% (11/14) of benign nodules demonstrated enhancement. The ROC curve when higher values of enhancement indicate malignancy was generated and area under the curve (AUC) was 0.76. The visually scored magnitude of enhancement was found to be less effective in distinguishing malignant from benign lesions, with an average AUC of 0.62. The visually scored pattern of enhancement was found to be more effective with an average AUC of 0.79. From the statistical analysis performed to combine the texture features to a single factor, the area under the ROC curve was 0.84. Conclusion The present study suggests that visual semi-quantitative and quantitative characterization of contrast enhancement patterns may potentially enhance the discrimination between benign and malignant nodules. Further studies and correlation with pathologic material will be important to better understand the potential interplay between CT enhancement features, host stromal elements, and neovascularity that may contribute to these patterns.

Cluster analysis of signal-intensity time course in dynamic breast MRI: does unsupervised vector quantization help to evaluate small mammographic lesions?

We examined whether neural network clustering could support the characterization of diagnostically challenging breast lesions in dynamic magnetic resonance imaging (MRI). We examined 88 patients with 92 breast lesions (51 malignant, 41 benign). Lesions were detected by mammography and classified Breast Imaging and Reporting Data System (BIRADS) III (median diameter 14 mm). MRI was performed with a dynamic T1-weighted gradient echo sequence (one precontrast and five postcontrast series). Lesions with an initial contrast enhancement >or=50% were selected with semiautomatic segmentation. For conventional analysis, we calculated the mean initial signal increase and postinitial course of all voxels included in a lesion. Secondly, all voxels within the lesions were divided into four clusters using minimal-free-energy vector quantization (VQ). With conventional analysis, maximum accuracy in detecting breast cancer was 71%. With VQ, a maximum accuracy of 75% was observed. The slight improvement using VQ was mainly achieved by an increase of sensitivity, especially in invasive lobular carcinoma and ductal carcinoma in situ (DCIS). For lesion size, a high correlation between different observers was found (R(2) = 0.98). VQ slightly improved the discrimination between malignant and benign indeterminate lesions (BIRADS III) in comparison with a standard evaluation method.

Optimizing brain MRI protocols in the follow-up of patients with multiple sclerosis

The aim of our study was to determine whether T2-weighted (T2w) MRI of the brain could be performed immediately after the administration of gadopentetate dimeglumine (gadolinium DTPA) in patients with multiple sclerosis (MS) without a loss in image quality or diagnostic reliability. Sixteen patients with clinically diagnosed MS were included in the study. Twenty-four patients with various cerebral pathologies (14 patients with multiple lacunar lesions) were examined in order to exclude masking of T2 hyperintense lesions other than MS lesions. Images of 10 patients without pathological changes served as a control condition for the qualitative analysis. In these 50 patients, T1w and T2w MRI was performed before and after the administration of gadolinium DTPA. Signal intensities were measured within T2 hyperintense cerebral lesions, in T1-enhancing lesions and in normal appearing brain tissue on T2w turbo spin-echo (TSE) sequences. Both quantitative and qualitative analysis did not show significant differences between T2w pre- and postcontrast series. T2w MRI performed prior to and after the administration of gadolinium DTPA provides similar information in patients with MS. With a TR of 3.2 s, not a single lesion was obscured on T2w postcontrast series. Acquisition of T2w MR images immediately after the administration of gadolinium DTPA allows for shorter examination time and assures sufficient time for contrast enhancement in cerebral lesions with a disrupted blood-brain barrier.

Imaging and localization of islet-cell tumours of the pancreas on CT and MRI

Islet-cell tumours are neuroendocrine tumours that arise from the endocrine pancreas. They may be associated with a variety of syndromes and are subclassified into functioning and non-functioning tumours. They range from benign to malignant. They demonstrate characteristic features when imaged with both computed tomography (CT) and magnetic resonance imaging (MRI). Sensitivity and specificity, as well as detection of extrapancreatic extension, are generally superior with MRI. However, CT is currently still more readily available to patients. Multiphase, post-contrast series are commended for the evaluation of islet-cell tumours with either modality.

Dynamic MR Imaging Of The Breast. Analysis of kinetic and morphologic diagnostic criteria

To assess the value of kinetic and architectural diagnostic criteria on dynamic MR breast imaging, and to construct a scoring system for lesion characterization. A total of 92 women with 109 histopathologically verified breast lesions were included in this retrospective study. The patients were examined by a 1.5 T system using a dedicated double breast coil. A dynamic examination with one precontrast and seven postcontrast series was performed, using a T1-weighted 3D FLASH sequence. Thirty lesions (15 malignant and 15 benign) were randomly chosen for the validation set, and the remaining 79 lesions (62 malignant and 17 benign) formed the estimation set, in which multivariate analysis was performed in order to select the most important features. These parameters were then used for constructing the scoring system, which was tested on the validation set. The scoring system was compared with the routine standard evaluation that used all established diagnostic criteria. ROC curves were generated to assess the diagnostic accuracy of different approaches. In the multivariate analysis of the 79 lesions, time-to-peak enhancement and the descriptor of margins were found to be the most important independent factors for distinguishing benign from malignant lesions, and formed the basis of the scoring system. The areas under the ROC curves for the standard evaluation, and the scoring system were 0.813 and 0.880 in the 30 lesions. Time-to-peak enhancement and the descriptor of margins appear to be the most important diagnostic criteria for mass lesions in dynamic breast MR imaging.

Dynamic MR imaging of the breast. Analysis of kinetic and morphologic diagnostic criteria.

To assess the value of kinetic and architectural diagnostic criteria on dynamic MR breast imaging, and to construct a scoring system for lesion characterization. A total of 92 women with 109 histopathologically verified breast lesions were included in this retrospective study. The patients were examined by a 1.5 T system using a dedicated double breast coil. A dynamic examination with one precontrast and seven postcontrast series was performed, using a T1-weighted 3D FLASH sequence. Thirty lesions (15 malignant and 15 benign) were randomly chosen for the validation set, and the remaining 79 lesions (62 malignant and 17 benign) formed the estimation set, in which multivariate analysis was performed in order to select the most important features. These parameters were then used for constructing the scoring system, which was tested on the validation set. The scoring system was compared with the routine standard evaluation that used all established diagnostic criteria. ROC curves were generated to assess the diagnostic accuracy of different approaches. In the multivariate analysis of the 79 lesions, time-to-peak enhancement and the descriptor of margins were found to be the most important independent factors for distinguishing benign from malignant lesions, and formed the basis of the scoring system. The areas under the ROC curves for the standard evaluation, and the scoring system were 0.813 and 0.880 in the 30 lesions. Time-to-peak enhancement and the descriptor of margins appear to be the most important diagnostic criteria for mass lesions in dynamic breast MR imaging.

Intracranial meningiomas

Fifty patients with intracranial meningiomas underwent plain and contrast-enhanced examinations with CT and MRI. Each of the MR studies consisted of three plain (T1, proton density and T2-weighted) and a post-contrast series (0.1 mmol Gd-DTPA/kg body weight). All techniques (plain CT, plain MRI, contrast-enhanced CT, contrast-enhanced MRI) proved to be highly efficient as regards tumour detection: depending on the technique, an intracranial lesion was demonstrated in 47-50 cases. The image contrast was assessed as good or excellent in 21 cases having plain CT and in 33 cases having plain MRI, but in 46 and 50 of the contrast-enhanced CT and MRI studies respectively. Adequate tumour delineation was achieved in 18 cases with plain CT, in 35 cases with plain MRI and in 46 and 50 cases of the contrast-enhanced CT and MRI examinations. The contrast-enhanced studies proved to be superior to the plain CT and MRI studies as regards image contrast and tumor delineation. Because of the methodological advantages of the MRI technique, contrast-enhanced MRI was judged to be slightly superior to contrast-enhanced CT.

Computed Tomography of Invasive Bladder Carcinoma after Diagnostic Transurethral Resection

Fourteen patients with known muscle invasive bladder carcinoma were examined with computed tomography (CT) before and five to seven days after diagnostic transurethral resection (TUR). The CT examinations included pre- and postcontrast series. In order to standardize the technique, we examined on a distended bladder. No significant changes occurred for the whole group after TUR. Individual changes were seen. In six patients the largest measured area of the bladder tumor increased after resection and in four the largest measured thickness increased, and this should be borne in mind when CT is used to evaluate tumor response to treatment. Perivesical changes occurred only after extensive diathermy or perforation. They did not show contrast enhancement and could therefore not be mistaken for tumor growth.

Contrast-Enhanced Computed Tomography to Show Perivesical Extension in Bladder Carcinoma

Fifty-two patients (53 lesions) with muscle invasive carcinoma (T2/T3) of the urinary bladder were examined with computed tomography (CT) before planned total cystectomy. The object of CT was to demonstrate perivesical growth. All patients were examined before and after intravenous injection of contrast medium. The stages obtained from the precontrast and postcontrast scans were compared with the histopathologic stage from the cystectomy specimen. CT staged correctly 35 of the 53 lesions in the precontrast series and 46 of the 53 lesions in the postcontrast series. The improved accuracy from contrast enhancement resulted primarily from fewer cases being overstaged. The use of intravenous contrast medium improved accuracy of CT in evaluation of perivesical tumor growth.