The diagnostic value of dynamic contrast-enhanced MRI for thyroid tumors

Background and purpose

The exact place for dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in the diagnosis and management of thyroid tumors is still under debate. We performed the study to analyze and compare the parameters generated from DCE-MRI for thyroid lesions.

Materials and methods

For each thyroid lesion, time intensity curves (TIC), time of peak enhancement (T_peak), maximum enhancement ratio (ER_max) and maximum rise slope (Slope_max) were plotted and calculated. Receiver operator characteristics (ROC) analysis was conducted to assess the diagnostic ability and appropriate cut-off value. The area under the ROC curve (AUC) and the confidence intervals (CIs) were also assessed.

Results

Forty-two patients were consecutively included. All 21 lesions demonstrated the rapid inflow and washout pattern (type-I) were benign. The 12 cases with delayed inflow pattern (type-III) were all malignant. When compared with the benign lesions, the thyroid carcinoma showed significantly lower Slope_max and higher T_peak (P < 0.05). No statistical difference of ER_max was found between malignant and benign ones (P = 0.15). The AUC of ER_max, Slope_max and T_peak in differentiating benign thyroid lesions from malignant ones were 0.63, 0.93and 1, respectively. The ER_max cut-off value of 73.86 (sensitivity, 71.4%; specificity, 64.3%), Slope_max cut-off value of 2.4126 (sensitivity, 92.9%; specificity, 82.1%) and T_peak value of 28 (sensitivity, 100%; specificity, 100%) offered the best diagnostic performances.

Conclusions

DCE-MRI, especially the pattern of TIC and the value of Slope_max and T_peak, could be helpful in differentiating thyroid carcinoma from benign thyroid lesions.     

Assessment of dynamic contrast-enhanced magnetic resonance imaging in the differentiation of malignant from benign orbital masses

Objective

Dynamic contrast enhanced MR imaging (DCE-MRI) allows imaging of the physiology of the microcirculation. The purpose of this study was to determine the diagnostic efficacy of time intensity curve (TIC) and DCE parameters for characterization of orbital masses.

Methods

Fifty-nine patients with untreated orbital lesions underwent DCE-MRI before surgery. For each lesion, peak height (PH), maximum enhancement ratio (ER_max), time of peak enhancement (T_peak) and maximum rise slope (Slope_max) were plotted and calculated. Receiver operator characteristics (ROC) analysis was conducted to assess the appropriate cut-off value.

Results

All 26 lesions that demonstrated persistent pattern (type-I) TICs were benign. Most of the masses with the washout pattern (type-III) TIC were malignant (10/14), including lymphoma (n = 6) and melanoma (n = 4). The Slope_max of benign lesions was statistically lower than malignant ones, while the ER_max and T_peak values of benign lesions were significantly higher. No statistical difference was found in PH (P = 0.121). The AUC for ER_max, T_peak and Slope_max in differentiating benign orbital lesions from malignant ones were 0.683, 0.837 and 0.738, respectively. In the three DCE parameters, Slope_max cut-off value of 1.10 provided the highest sensitivity of 93.8%; however, the corresponding specificity was low (58.1%). The ER_max cut-off value of 1.37 and T_peak cut-off value of 35.14 respectively offered the best diagnostic performances.

Conclusion

DCE-MRI, especially the qualitative TIC pattern and quantitative value of Slope_max, ER_max and T_peak, could be a complementary investigation in distinguishing malignant orbital tumor from benign ones.     

Selection of diagnostic features on breast MRI to differentiate between malignant and benign lesions using computer-aided diagnosis: differences in lesions presenting as mass and non-mass-like enhancement

Purpose

To investigate methods developed for the characterisation of the morphology and enhancement kinetic features of both mass and non-mass lesions, and to determine their diagnostic performance to differentiate between malignant and benign lesions that present as mass versus non-mass types.

Methods

Quantitative analysis of morphological features and enhancement kinetic parameters of breast lesions were used to differentiate among four groups of lesions: 88 malignant (43 mass, 45 non-mass) and 28 benign (19 mass, 9 non-mass). The enhancement kinetics was measured and analysed to obtain transfer constant (K ^trans) and rate constant (k _ep). For each mass eight shape/margin parameters and 10 enhancement texture features were obtained. For the lesions presenting as nonmass-like enhancement, only the texture parameters were obtained. An artificial neural network (ANN) was used to build the diagnostic model.

Results

For lesions presenting as mass, the four selected morphological features could reach an area under the ROC curve (AUC) of 0.87 in differentiating between malignant and benign lesions. The kinetic parameter (k _ep) analysed from the hot spot of the tumour reached a comparable AUC of 0.88. The combined morphological and kinetic features improved the AUC to 0.93, with a sensitivity of 0.97 and a specificity of 0.80. For lesions presenting as non-mass-like enhancement, four texture features were selected by the ANN and achieved an AUC of 0.76. The kinetic parameter k _ep from the hot spot only achieved an AUC of 0.59, with a low added diagnostic value.

Conclusion

The results suggest that the quantitative diagnostic features can be used for developing automated breast CAD (computer-aided diagnosis) for mass lesions to achieve a high diagnostic performance, but more advanced algorithms are needed for diagnosis of lesions presenting as non-mass-like enhancement.     

Morphological manifestations of nonpuerperal mastitis on magnetic resonance imaging

Purpose:

To investigate the morphological features of nonpuerperal mastitis on magnetic resonance imaging (MRI).

Materials and Methods:

MRI results of 27 patients diagnosed with nonpuerperal mastitis were retrospectively analyzed according to the Breast Imaging Reporting and Data System (BI‐RADS).

Results:

According to the enhanced MRI results, 22 cases presented as nonmass‐like enhancements and five cases presented as masses. For the 22 nonmass‐like enhancement lesions, one case showed as a homogeneous enhancement and 21 cases showed as heterogeneous enhancements. In the 21 heterogeneous enhancement cases, 13 contained lesions of rim‐like enhancement. With regard to the number of lesions with rim‐like enhancement, 11 presented as multiple and two as single. In the five mass lesions, three presented as rim enhancements. Indications of rim or rim‐like enhancement accounted for 59% (16/27) of all cases. For the 16 cases that presented with signs of rim or rim‐like enhancement, the central hypointensity area surrounded by enhanced rim or rim‐like entity showed as hyperintensity in 14 cases and hypointensity or heterointensity in two cases on T2‐weighted imaging.

Conclusion:

On contrast‐enhanced MRI, most nonpuerperal mastitis presented as nonmass‐like lesion with heterogeneous signal intensity. The observation of signs of rim or rim‐like enhancement on contrast‐enhanced MRI with central hypointensity areas showing as hyperintensity on T2‐weighted imaging is suggestive of the possibility of nonpuerperal mastitis. J. Magn. Reson. Imaging 2011;33:1369–1374. © 2011 Wiley‐Liss, Inc.     

Semi-quantitative contrast-enhanced MR analysis of indeterminate ovarian tumours: when to say malignancy?

Objective:

To evaluate the ability of dynamic post-contrast sequence to specify indeterminate ovarian masses with inconclusive MR features of malignancy. Since management is dramatically different, special focus on the ability to differentiate borderline from invasive malignancy was considered.

Methods:

150 ovarian masses were detected by pelvic ultrasound in 124 patients. Masses had been considered for dynamic post-contrast MRI. We expressed the kinetic parameters (i.e. enhancement amplitude, time peak of maximal uptake and maximal slope) in the form of maximum relative enhancement percentage (MRE%), time of maximal peak of contrast uptake (T_max) and slope enhancement ratio (SER) curves. Histological findings were the gold standard of reference.

Results:

Malignant ovarian masses showed higher MRE% than benign and borderline masses (p < 0.001). T_max was shorter for malignant than benign (p < 0.01) and borderline (p < 0.001) ovarian masses. SER curves were the most suggestive of malignancy with a specificity and accuracy of 85.7% and 84.7%, respectively.

Conclusion:

Dynamic contrast-enhanced MRI could be a specific sequence to differentiate ovarian masses with indeterminate MR morphology with a special discrimination for low potential from invasive ovarian malignancy.

Advances in knowledge:

The study evaluated the diagnostic performance of the individual parameters of dynamic post-contrast MR sequence in evaluating ovarian masses. Management divert between benign, borderline and invasive malignant masses; our work presented a cut-off value for the peak of contrast uptake of 120%, which helped in the differentiation between benign and malignant tumours; the SER curves with Type III (early washout) pattern that was indicative of invasive malignancy was more specific than borderline malignancy.     

Relationship of temporal resolution to diagnostic performance for dynamic contrast enhanced MRI of the breast

Purpose:

To investigate the relationship between temporal resolution of dynamic contrast‐enhanced (DCE) magnetic resonance imaging (MRI) and classification of breast lesions as benign versus malignant.

Materials and Methods:

Patients underwent T₁‐weighted DCE MRI with 15 s/acquisition temporal resolution using 1.5 Tesla (n = 48) and 3.0T (n = 33) MRI scanners. Seventy‐nine patients had pathologically proven diagnosis and 2 had 2 years follow‐up showing no change in lesion size. The temporal resolution of DCE MRI was systematically reduced as a postprocessing step from 15 to 30, 45, and 60 s/acquisition by eliminating intermediate time points. Average wash‐in and wash‐out slopes, wash‐out percentage changes, and kinetic curve shape (persistently enhancing, plateau, or wash‐out) were compared for each temporal resolution. Logistic regression and receiver operating characteristic (ROC) curve analysis were used to compare kinetic parameters and diagnostic accuracy.

Results:

Sixty patients (74%) had malignant lesions and 21 patients (26%) had benign lesions. All temporal‐resolution parameters significantly predicted benign versus malignant diagnosis (P < 0.05). However, 45 s/acquisition and higher temporal‐resolution datasets showed higher accuracy than the 60 s/acquisition dataset by ROC curve analysis (0.72 versus 0.69 for average wash‐in slope; 0.85 versus 0.82, for average wash‐out slope; and 0.88 versus 0.80 for kinetic curve shape assessment, for 45 s/acquisition versus 60 s/acquisition temporal‐resolution datasets, respectively (P = 0.027).

Conclusion:

DCE MRI data with at least 45‐s temporal resolution maximized the agreement between the kinetic parameters and correct classification of benign versus malignant diagnosis. J. Magn. Reson. Imaging 2009;30:999–1004. © 2009 Wiley‐Liss, Inc.     

Breast lesions detected on MR imaging: features and positive predictive value

OBJECTIVE: The purpose of this study was to analyze features of breast lesions detected on MR imaging that had subsequent biopsy and to determine the positive predictive value (PPV) of these features. MATERIALS AND METHODS: Retrospective review was performed of 100 consecutive solitary MR imaging-detected breast lesions that had MR imaging-guided needle localization and surgical excision. We described lesions, using terms found in a proposed breast MR imaging lexicon. Histologic findings were reviewed. RESULTS: Carcinoma was identified in 25 lesions (25%), including ductal carcinoma in situ (DCIS) in 13 (52%) and infiltrating carcinoma in 12 (48%). Carcinoma was found in 15 (25%) of 60 masses versus 10 (25%) of 40 nonmass lesions; most malignant masses (73%) were infiltrating carcinoma, whereas most malignant nonmass lesions (90%) were DCIS. The features with the highest PPV were spiculated margin (80% carcinoma), rim enhancement (40% carcinoma), and irregular shape (32% carcinoma) for mass lesions and segmental (67% carcinoma) or clumped linear and ductal enhancement (31% carcinoma) for nonmass lesions. Visually assessed kinetic patterns were not significant predictors of carcinoma, but washout was present in 70% of infiltrating carcinomas versus 9% of DCIS lesions (p < 0.01). Carcinoma was present in 17 (19%) of 88 lesions classified as suspicious versus eight (67%) of 12 lesions classified as highly suggestive of malignancy (p = 0.001). CONCLUSION: Among MR imaging-detected breast lesions referred for biopsy, carcinoma was found in 25%, of which half were DCIS. Features with the highest PPV were spiculated margin, rim enhancement, and irregular shape for mass lesions and segmental or clumped linear and ductal enhancement for nonmass lesions. Final assessment categories were significant predictors of carcinoma.     

Dynamic contrast-enhanced and diffusion-weighted MR imaging in the characterisation of small,non-palpable solid testicular tumours

Objectives

To explore the role of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI), using semiquantitative and quantitative parameters, and diffusion-weighted (DW) MRI in differentiating benign from malignant small, non-palpable solid testicular tumours.

Methods

We calculated the following DCE-MRI parameters of 47 small, non-palpable solid testicular tumours: peak enhancement (PE), time to peak (TTP), percentage of peak enhancement (Epeak), wash-in-rate (WIR), signal enhancement ratio (SER), volume transfer constant (K_trans), rate constant (K_ep), extravascular extracellular space volume fraction (V_e) and initial area under the curve (_iAUC). DWI signal intensity and apparent diffusion coefficient (ADC) values were evaluated.

Results

E_peak, WIR, K_trans , K_ep and iAUC were higher and TTP shorter in benign compared to malignant lesions (p?<?0.05). All tumours had similar ADC values (p > 0.07). Subgroup analysis limited to the most frequent histologies – Leydig cell tumours (LCTs) and seminomas – replicated the findings of the entire set. Best diagnostic cutoff value for identification of seminomas: K_trans ≤0.135 min^?1, K_ep ≤0.45 min^?1, iAUC ≤10.96, WIR ≤1.11, Epeak ≤96.72, TTP >99 s.

Conclusions

DCE-MRI parameters are valuable in differentiating between benign and malignant small, non-palpable testicular tumours, especially when characterising LCTs and seminomas.

Key Points

? DCE-MRI may be used to differentiate benign from malignant non-palpable testicular tumours. ? Seminomas show lower Ktrans, Kep and iAUC values. ? ADC values are not valuable in differentiating seminomas from LCTs. ? Semiquantitative DCE-MRI may be used to characterise small, solid testicular tumours.

     

Diagnostic usefulness of water-to-fat ratio and choline concentration in malignant and benign breast lesions and normal breast parenchyma: an in vivo (1) H MRS study

Purpose:

To compare total choline concentrations ([Cho]) and water‐to‐fat (W/F) ratios of subtypes of malignant lesions, benign lesions, and normal breast parenchyma and determine their usefulness in breast cancer diagnosis. Reference standard was histology.

Materials and Methods:

In this HIPPA compliant study, proton MRS was performed on 93 patients with suspicious lesions (>1 cm) who underwent MRI‐guided interventional procedures, and on 27 prospectively accrued women enrolled for screening MRI. (W/F) and [Cho] values were calculated using MRS data.

Results:

Among 88 MRS‐evaluable histologically‐confirmed lesions, 40 invasive ductal carcinoma (IDC); 10 invasive lobular carcinoma (ILC); 4 ductal carcinoma in situ (DCIS); 3 invasive mammary carcinoma (IMC); 31 benign. No significant difference observed in (W/F) between benign lesions and normal breast tissue. The area under curve (AUC) of receiver operating characteristic (ROC) curves for discriminating the malignant group from the benign group were 0.97, 0.72, and 0.99 using [Cho], (W/F) and their combination as biomarkers, respectively. (W/F) performs significantly (P < 0.0001;AUC = 0.96) better than [Cho] (AUC = 0.52) in differentiating IDC and ILC lesions.

Conclusion:

Although [Cho] and (W/F) are good biomarkers for differentiating malignancy, [Cho] is a better marker. Combining both can further improve diagnostic accuracy. IDC and ILC lesions have similar [Cho] levels but are discriminated using (W/F) values. J. Magn. Reson. Imaging 2011;33:855–863. © 2011 Wiley‐Liss, Inc.     

Diagnosis of breast tumors by contrast-enhanced MR imaging: comparison between the diagnostic performance of dynamic enhancement patterns and morphologic features

PURPOSE: To compare the diagnostic performance of breast lesions by the enhancement patterns and morphologic criteria on magnetic resonance imaging (MRI). MATERIALS AND METHODS: Both T1-weighted 3D gradient-echo sequences with high temporal resolution and high-spatial-resolution MRI were performed on 190 patients with a total of 204 enhancing lesions (144 malignant and 60 benign). The enhancement patterns and morphologic features of each mass and nonmass lesion were analyzed, and the diagnostic performance was compared. RESULTS: The sensitivity and specificity of the morphologic criteria were statistically significantly higher than those of the enhancement patterns (sensitivity: P = 0.0012, specificity: P = 0.0003), and the A(z) values for the three observers were 0.900, 0.919, and 0.900. The diagnostic accuracy of the morphologic criteria for both types of lesions was superior, and the differences were statistically significant (mass: P = 0.0001, nonmass: P = 0.0389). CONCLUSION: The analysis of the morphologic features of enhancing breast lesions alone showed higher diagnostic performance; therefore, signal intensity (SI) time-course data may not be needed to diagnose malignant breast lesions.     

Characterization of malignancy of adnexal lesions using ADC entropy: Comparison with mean ADC and qualitative DWI assessment

Purpose:

To establish the utility of apparent diffusion coefficient (ADC) entropy in discrimination of benign and malignant adnexal lesions, using histopathology as the reference standard, via comparison of the diagnostic performance of ADC entropy with mean ADC and with visual assessments of adnexal lesions on conventional and diffusion‐weighted sequences.

Materials and Methods:

In all, 37 adult female patients with an ovarian mass that was resected between June 2006 and January 2011 were included. Volume‐of‐interest was drawn to incorporate all lesion voxels on every slice that included the mass on the ADC map, from which whole‐lesion mean ADC and ADC entropy were calculated. Two independent radiologists also rated each lesion as benign or malignant based on visual assessment of all sequences. The Mann–Whitney test and logistic regression for correlated data were used to compare performance of mean ADC, ADC entropy, and the visual assessments.

Results:

No statistically significant difference was observed in mean ADC between benign and malignant adnexal lesions (P = 0.768). ADC entropy was significantly higher in malignant than in benign lesions (P = 0.009). Accuracy was significantly greater for ADC entropy than for mean ADC (0.018). ADC entropy and visual assessment by the less‐experienced reader showed similar accuracy (P ≥ 0.204). The more experienced reader's accuracy was significantly greater than that of all other assessments (P ≤ 0.039).

Conclusion:

ADC entropy showed significantly greater accuracy than the more traditional metric of mean ADC for distinguishing benign and malignant adnexal lesions. Although whole‐lesion ADC entropy provides a straightforward and objective measurement, its potential benefit decreases with greater reader experience. J. Magn. Reson. Imaging 2013;37:164–171. © 2012 Wiley Periodicals, Inc.     

Differential diagnosis of mammographically and clinically occult breast lesions on diffusion‐weighted MRI

Purpose:

To investigate the diagnostic performance of diffusion‐weighted imaging (DWI) for mammographically and clinically occult breast lesions.

Materials and Methods:

The study included 91 women with 118 breast lesions (91 benign, 12 ductal carcinoma in situ [DCIS], 15 invasive carcinoma) initially detected on dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI) and assigned BI‐RADS category 3, 4, or 5. DWI was acquired with b = 0 and 600 s/mm². Lesion visibility was assessed on DWI. Apparent diffusion coefficient (ADC) values were compared between malignancies, benign lesions, and normal (no abnormal enhancement on DCE‐MRI) breast tissue, and the diagnostic performance of DWI was assessed based on ADC thresholding.

Results:

Twenty‐four of 27 (89%) malignant and 74/91 (81%) benign lesions were hyperintense on the b = 600 s/mm² diffusion‐weighted images. Both DCIS (1.33 ± 0.19 × 10^?3 mm²/s) and invasive carcinomas (1.30 ± 0.27 × 10^?3mm²/s) were lower in ADC than benign lesions (1.71 ± 0.43 × 10^?3mm²/s; P < 0.001), and each lesion type was lower in ADC than normal tissue (1.90 ± 0.38 × 10^?3mm²/s, P ≤ 0.001). Receiver operating curve (ROC) analysis showed an area under the curve (AUC) of 0.77, and sensitivity = 96%, specificity = 55%, positive predictive value (PPV) = 39%, and negative predictive value (NPV) = 98% for an ADC threshold of 1.60 × 10^?3mm²/s.

Conclusion:

Many mammographically and clinically occult breast carcinomas were visibly hyperintense on diffusion‐weighted images, and ADC enabled differentiation from benign lesions. Further studies evaluating DWI while blinded to DCE‐MRI are necessary to assess the potential of DWI as a noncontrast breast screening technique. J. Magn. Reson. Imaging 2010;1:562–570. © 2010 Wiley‐Liss, Inc.

     

Correlation of the apparent diffusion coefficient value and dynamic magnetic resonance imaging findings with prognostic factors in invasive ductal carcinoma

Purpose

To evaluate the correlation of the mean and minimal apparent diffusion coefficient values (ADC_mean, ADC_minimal) and dynamic magnetic resonance imaging (MRI) findings with prognostic factors in invasive ductal carcinoma.

Materials and Methods

A total of 107 women with invasive ductal cancer underwent breast MRI. The ADC_mean and ADC_minimal of the cancers were computed. MRI findings were retrospectively evaluated according to the Breast Imaging Reporting and Data System (BI‐RADS) lexicon: mass or nonmass type, mass shape, mass margin, nonmass distribution, and enhancement pattern. Histological records were reviewed for tumor size, lymph node metastasis, histologic grade, and expression of estrogen receptors (ER), progesterone receptors (PR), c‐erbB‐2(HER2), Ki‐67, and epidermal growth factor receptors (EGFR). Correlations of ADC values and MR findings with prognostic factors were determined using the Mann–Whitney U‐test and the Kruskal–Wallis test.

Results

The mean ADC_minimal was 0.78 ± 0.24 (×10^?3 mm²/s), and the mean ADC_mean was 1.01 ± 0.23 (×10^?3 mm²/s). There was a significant correlation of the ADC_mean value with ER expression (P = 0.027) and HER2 expression (P = 0.018). There was no significant relationship between ADC_minimal and prognostic factors or between ADC_mean and traditional prognostic factors, PR, Ki‐67 and EGFR. The majority of the mass type lesions were less than 5 cm in size and the majority of nonmass type lesions were more than 2 cm in size (P = 0.022). The margin of mass was significantly associated with lymph node metastasis (P = 0.031), ER expression (P = 0.013), PR expression (P = 0.036), HER2 expression (P = 0.019), and EGRF expression (P = 0.041). The rim internal enhancement was significantly correlated with Ki‐67 expression (P = 0.008).

Conclusion

The low ADC_mean value was related to positive expression of ER and negative expression of HER2. A spiculated margin was related to a good prognosis, but rim enhancement was associated with a poor prognosis. J. Magn. Reson. Imaging 2011;33:102–109. © 2010 Wiley‐Liss, Inc.

     

Assessing the degree of collinearity among the lesion features of the MRI BI-RADS lexicon

Purpose

To retrospectively assess collinearity among lesion feature of the MRI BI-RADS lexicon. Collinearity denotes a situation in which two or more (independent) variables are correlated to some degree, thus partly conveying the same information. Collinearity may cause problems in the interpretation of logistic regression models.

Materials and methods

We analysed the BI-RADS features of 351 lesions in 325 consecutive patients. Patients with biopsy proven breast disease or treated with chemotherapy were excluded. All lesion features were dichotomised into “present” or “not present”. Correlation matrices were generated for mass and non-mass lesions separately, focus lesions were omitted. The phi coefficient was used as measure for correlation.

Results

There were 253 mass (175 malignant, 78 benign), 66 non-mass (21 malignant, 45 benign) and 32 focus (5 malignant, 27 benign) lesions among the study population. The strongest inter-subgroup correlations among mass lesion features were: slow initial enhancement with persistent kinetics, phi = 0.64 (0.56–0.71), rapid initial enhancement with washout kinetics, phi = 0.52 (0.43–0.61) and rapid initial enhancement with persistent kinetics, phi = −0.43 (−0.53 to −0.32). The strongest inter-subgroup correlation among non-mass lesion features were: rapid initial enhancement with washout kinetics, phi = 0.51 (0.30–0.67), slow initial enhancement with persistent kinetics, phi = 0.43 (0.21–0.61) and rapid initial enhancement with persistent kinetics, phi = −0.41 (−0.18 to −0.60).

Conclusion

There is a noticeable overlap of information, especially between kinetic features and initial enhancement types for both, mass and non-mass lesions. This should be considered when generating logistic regression models with the MRI BI-RADS lesion features.     

Enhanced mass on contrast-enhanced breast MR imaging: Lesion characterization using combination of dynamic contrast-enhanced and diffusion-weighted MR images

Purpose

To evaluate the diagnostic accuracy of a combination of dynamic contrast‐enhanced MR imaging (DCE‐MRI) and diffusion‐weighted MR imaging (DWI) in characterization of enhanced mass on breast MR imaging and to find the strongest discriminators between carcinoma and benignancy.

Materials and Methods

We analyzed consecutive breast MR images in 270 patients; however, 13 lesions in 93 patients were excluded based on our criteria. We analyzed tumor size, shape, margin, internal mass enhancement, kinetic curve pattern, and apparent diffusion coefficient (ADC) values. We applied univariate and multivariate analyses to find the strongest indicators of malignancy and calculate a predictive probability for malignancy. We added the corresponding categories to these prediction probabilities for malignancy and calculated diagnostic accuracy when we consider category 4b, 4c, and 5 lesions as malignant and category 4a, 3, and 2 lesions as benign. In a validation study, 75 enhancing lesions in 71 patients were examined consecutively.

Results

Irregular margin, heterogeneous internal enhancement, rim enhancement, plateau time–intensity curve (TIC) pattern, and washout TIC pattern were the strongest indicators of malignancy as well as past studies, and ADC values less than 1.1 × 10^?3 mm²/s were also the strongest indicators of malignancy. In a validation study, sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 92% (56/61), 86% (12/14), 97% (56/58), 71% (12/17), and 91% (68/75), respectively.

Conclusion

The combination of DWI and DCE‐MRI could produce high diagnostic accuracy in the characterization of enhanced mass on breast MR imaging. J. Magn. Reson. Imaging 2008;28:1157–1165. © 2008 Wiley‐Liss, Inc.

     

Reproducibility and correlation between quantitative and semiquantitative dynamic and intrinsic susceptibility‐weighted MRI parameters in the benign and malignant human prostate

Purpose:

To assess the reproducibility of relaxivity‐ and susceptibility‐based dynamic contrast‐enhanced magnetic resonance imaging (MRI) in the benign and malignant prostate gland and to correlate the kinetic parameters obtained.

Materials and Methods:

Twenty patients with prostate cancer underwent paired scans before and after androgen deprivation therapy. Quantitative parametric maps for T₁‐ and T₂*‐weighted parameters were calculated (K^trans, k_ep,v_e, IAUC₆₀, rBV, rBF, and R₂*). The reproducibility of and correlation between each parameter were determined using standard methods at both timepoints.

Results:

T₁‐derived parameters are more reproducible than T₂*‐weighted measures, both becoming more variable following androgen deprivation (variance coefficients for prostate K^trans and rBF increased from 13.9%–15.8% and 42.5%–90.8%, respectively). Tumor R₂* reproducibility improved after androgen ablation (23.3%–11.8%). IAUC₆₀ correlated strongly with K^trans, v_e, and k_ep (all P < 0.001). R₂* did not correlate with other parameters.

Conclusion:

This study is the first to document the variability and repeatability of T₁‐ and T₂*‐weighted dynamic MRI and intrinsic susceptibility‐weighted MRI for the various regions of the human prostate gland before and after androgen deprivation. These data provide a valuable source of reference for groups that plan to use dynamic contrast‐enhanced MRI or intrinsic susceptibility‐weighted MRI for the assessment of treatment response in the benign or malignant prostate. J. Magn. Reson. Imaging 2010;32:155–164. © 2010 Wiley‐Liss, Inc.     

Multimodality imaging features of idiopathic granulomatous mastitis: outcome of 12 years of experience

Purpose

This study was done to determine mammographic, sonographic and magnetic resonance imaging (MRI) appearances of idiopathic granulomatous mastitis, an entity clinically and radiographically resembling breast carcinoma.

Materials and methods

A total of 36 women (mean age 37 years, range 21?C51 years) with histopathological diagnosis of idiopathic granulomatous mastitis were enrolled in the study. The Breast Imaging Reporting and Data System (BI-RADS) was used to categorise the levels of suspicion of malignancy on mammography. Mammography findings were classified also according to density, margin, architectural distortion and number of lesions. Lesions were classified according to number, heterogeneity and echogenic features on sonography. Dynamic MRI findings were categorised as enhancing mass lesion, nonmass lesion or both mass lesions and nonmass lesions together. Subclassification criteria for MRI included lesion shape, margin, border and internal enhancement pattern.

Results

The most common mammographic finding was either focal or diffuse asymmetric density (n=15, 44%). The most common sonographic findings were solitary or multiple circumscribed heterogeneous hypoechoic masses (n=19, 52%). Among other sonographic findings were diffuse abscess formation with fistulae and massive parenchymal heterogeneity and hypoechogenicity in 12 (33%) and five (13%) women, respectively. On MRI, enhancing mass lesions were detected in 24 patients, whereas enhancing nonmass lesions were observed in 28. Sixteen patients had both enhancing mass lesions and nonmass lesions together.

Conclusions

Although not characteristic for this entity, asymmetric density on mammography, solitary or multiple clustered heterogeneous hypoechogenicity with a tubular configuration on sonography and round, smooth-contoured masslike lesion with rim enhancement or segmental non-mass-like lesion on MRI are the most common features of the disease.     

Differentiation of malignant and benign breast lesions using magnetization transfer imaging and dynamic contrast‐enhanced MRI

Purpose:

To evaluate feasibility of using magnetization transfer ratio (MTR) in conjunction with dynamic contrast‐enhanced MRI (DCE‐MRI) for differentiation of benign and malignant breast lesions at 3 Tesla.

Materials and Methods:

This prospective study was IRB and HIPAA compliant. DCE‐MRI scans followed by MT imaging were performed on 41 patients. Regions of interest (ROIs) were drawn on co‐registered MTR and DCE postcontrast images for breast structures, including benign lesions (BL) and malignant lesions (ML). Initial enhancement ratio (IER) and delayed enhancement ratio (DER) were calculated, as were normalized MTR, DER, and IER (NMTR, NDER, NIER) values. Diagnostic accuracy analysis was performed.

Results:

Mean MTR in ML was lower than in BL (P < 0.05); mean DER and mean IER in ML were significantly higher than in BL (P < 0.01, P < 0.001). NMTR, NDER, and NIER were significantly lower in ML versus BL (P < 0.007, P < 0.001, P < 0.001). IER had highest diagnostic accuracy (77.6%), sensitivity (86.2%), and area under the ROC curve (.879). MTR specificity was 100%. Logistic regression modeling with NMTR and NIER yielded best results for BL versus ML (sensitivity 93.1%, specificity 80%, AUC 0.884, accuracy 83.7%).

Conclusion:

Isolated quantitative DCE analysis may increase specificity of breast MR for differentiating BL and ML. DCE‐MRI with NMTR may produce a robust means of evaluating breast lesions. J. Magn. Reson. Imaging 2013;37:138–145. © 2012 Wiley Periodicals, Inc.