Improved diagnostic accuracy with multiparametric magnetic resonance imaging of the breast using dynamic contrast-enhanced magnetic resonance imaging, diffusion-weighted imaging, and 3-dimensional proton magnetic resonance spectroscopic imaging.

ORIGINAL ARTICLE

Improved Diagnostic Accuracy With Multiparametric Magnetic Resonance Imaging of the Breast Using Dynamic Contrast-Enhanced Magnetic Resonance Imaging, Diffusion-Weighted Imaging, and 3-Dimensional Proton Magnetic Resonance Spectroscopic Imaging Katja Pinker, MD,* Wolfgang Bogner, PhD,Þ Pascal Baltzer, MD,* Stephan Gruber, PhD,Þ Hubert Bickel, MD,* Benedikt Brueck, MD,* Siegfried Trattnig, MD,Þ Michael Weber, PhD,* Peter Dubsky, MD,þ Zsuzsanna Bago-Horvath, MD,§ Rupert Bartsch, MD,|| and Thomas H. Helbich, MD* Introduction: The purpose of this study was to compare the diagnostic accuracy of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) as a single parameter to multiparametric (MP) MRI with 2 (DCE MRI and diffusionweighted imaging [DWI]) and 3 (DCE MRI, DWI, and 3-dimensional proton magnetic resonance spectroscopic imaging [3D 1H-MRSI]) parameters in breast cancer diagnosis. Materials and Methods: This prospective study was approved by the institutional review board. Written informed consent was obtained in all patients. One hundred thirteen female patients (mean age, 52 years; range, 22Y86 years) with an imaging abnormality (Breast Imaging Reporting and Data System 0, 4Y5) were included in this study. Multiparametric MRI of the breast at 3 T with DCE MRI, DWI, and 3D 1H-MRSI was performed. The likelihood of malignancy was assessed for DCE MRI and MP MRI with 2 (DCE MRI and DWI) and 3 (DCE MRI, DWI, and 3D 1H-MRSI) parameters separately. Histopathology was used as the standard of reference. Appropriate statistical tests were used to assess sensitivity, specificity, and diagnostic accuracy for each assessment combination. Results: There were 74 malignant and 39 benign breast lesions. Multiparametric MRI with 3 MRI parameters yielded significantly higher areas under the curve (0.936) in comparison with DCE MRI alone (0.814) (P G 0.001). Multiparametric MRI with just 2 parameters at 3 T did not yield higher areas under the curve (0.808) than did DCE MRI alone (0.814). Multiparametric MRI with 3 parameters resulted in elimination of false-negative lesions and significantly reduced the false-positives ones (P = 0.002). Conclusions: Multiparametric MRI with 3 parameters increases the diagnostic accuracy of breast cancer in comparison with DCE-MRI alone and MP MRI with 2 parameters. Key Words: breast cancer diagnosis, multiparametric MRI, dynamic contrast-enhanced magnetic resonance imaging, diffusion-weighted imaging, 3D proton MR spectroscopic imaging (Invest Radiol 2014;49: 421Y430)

D

ynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) of the breast has been the mainstay of breast MRI with an

Received for publication October 9, 2013; and accepted for publication, after revision, November 25, 2013. From the Departments of *Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, †Biomedical Imaging and Image-guided Therapy, MR Centre of Excellence, ‡Surgery, §Pathology, and ||Internal Medicine, Division of Oncology, Medical University of Vienna, Vienna, Austria. Conflicts of interest and sources of funding: Supported by the Austrian National Bank Jubila¨umsfond Project nos. 13652 and 15082. The authors report no conflicts of interest. Reprints: Thomas H. Helbich, MD, Department of Biomedical Imaging and Imageguided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria. E-mail: thomas.helbich@ meduniwien.ac.at. Copyright * 2014 by Lippincott Williams & Wilkins ISSN: 0020-9996/14/4906Y0421

Investigative Radiology

& Volume 49, Number 6, June 2014

excellent sensitivity.1Y5 However, it is limited by a low specificity.6,7 Dynamic contrast-enhanced MRI of the breast provides morphologic information and limited functional information regarding angiogenesis and tumor vascularity. Available data suggest that the use of other functional MRI parameters such as diffusion-weighted imaging (DWI) with quantitative apparent diffusion coefficient (ADC) mapping and proton magnetic resonance (MR) spectroscopic imaging (1H-MRSI) may provide additional specificity.8Y15 Diffusion-weighted imaging reflects the diffusivity of water molecules in tissue. Typically, the movement of water is restricted in malignant cells.16,17 Proton MR spectroscopic imaging reflects tumor metabolism. Choline (Cho), a marker of cell turnover, is increased in breast cancer.12,18Y20 To date, research groups have investigated multiparametric (MP) MRI of the breast using combinations of DCE-MRI with one additional functional MRI parameter, for example, DWI or 1H-MRSI. The results obtained with MP MRI of the breast using 2 parameters were promising.9Y13,21,22 However, to our knowledge, the diagnostic accuracy of a combination of 3 parameters (DCE MRI, DWI, and 3dimensional [3D] 1H-MRSI) has not been performed. The number of parameters necessary to significantly improve the accuracy of breast cancer diagnosis is unknown. In this study, we aimed to assess the diagnostic accuracy of 3 parameters (DCE MRI, DWI, and 3D 1H-MRSI) in comparison with dual-parameter (DCE MRI and DWI) and single-parameter DCE MRI.

MATERIALS AND METHODS Patients This prospective; single-institution study was approved by the institutional review board of the Medical University of Vienna. Written informed consent was obtained from all patients before their MRI examination. From September 2008 to December 2012, a total of 133 consecutive female patients were enrolled in this study. They underwent MP MRI of the breast with DCE-MRI, DWI, and 3D 1H-MRSI. The inclusion criteria were as follows: age of 18 years or older and an abnormality at mammography or breast ultrasound (asymmetric density, architectural distortion, suspicious microcalcifications, or breast mass; Breast Imaging Reporting and Data System (BI-RADS) category 0 or 4Y5). The exclusion criteria were pregnancy, lactation, prior treatment (eg, breast biopsy before MRI, neoadjuvant chemotherapy), and contraindications to MRI or contrast agents.1 In addition to the previously mentioned criteria, 12 patients were excluded because of the technical failure of 3D 1H-MRSI (frequency shift 91 ppm resulting in insufficient water and/or fat suppression) and 8 patients were excluded because of insufficient data quality because of motion artifacts, resulting in a total of 113 (mean age, 52 years; range, 22Y86 years) patients to be included in the study. Regardless of the results of MP MRI, histopathological verification of the lesion in question was performed. The initial BI-RADS category www.investigativeradiology.com

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distributions of the lesions before MRI were the following: BI-RADS 0 for 43, BI-RADS 4 for 38, and BI-RADS 5 for 32 lesions.

MR Imaging All patients underwent MP MRI of the breast. All MR examinations were performed in the prone position using a 3-T MR scanner (Tim Trio; Siemens, Erlangen, Germany) and a dedicated 4-channel breast coil (InVivo, Orlando, FL). In premenopausal women, MRI was performed in the second week of the menstrual cycle.1 The MRI sequence protocol was acquired in the following order: a) T2-weighted turbo spin echo sequence with fat suppression (repetition time [TR]/echo time [TE]/inversion time [TI], 4800/61/230 milliseconds; field of view (FOV), 340 mm; 34 slices at 4 mm; matrix, 314 320; 1 average; time of acquisition (TA) 2 minutes 26 seconds); b) Diffusion-weighted, double-refocused, single-shot echo-planar imaging with inversion recovery fat suppression (TR/TE/inversion time, 13700/83/220 milliseconds; FOV, 340 117 mm; 40 slices at 3.5 mm; matrix 192 64 [50% oversampling]; 2 averages; b values, 50 and 850 s/mm2; TA, 3 minutes 19 seconds)8; c) Point-resolved spectroscopic sequence with spectral water and fat suppression and spatial outer volume suppression (TR/TE, 750/ 145 milliseconds; FOV, 12 12 12 cm3; matrix, 12 12 12; interpolated voxel size, 7.5 7.5 7.5 mm3; 5 averages; TA, 11 minutes 17 seconds) before the application of contrast agent application to avoid an influence of the contrast agent on the Cho signal12; d) A split dynamics protocol combining high-spatial and high-temporal resolution was used. Parameters applied were as follows: T1weighted volume interpolated breathhold examination sequences (TR/TE, 3.61/1.4 milliseconds; FOV, 320 mm; 72 slices; 1.7-mm isotropic; matrix, 192 192; 1 average; 13.2 seconds per volume) with a total TA of 15 minutes 20 seconds and T1-weighted turbo fast low-angle shot 3D sequences with selective water excitation (TR/TE, 877/3.82 milliseconds; FOV, 320 mm; 96 slices; 1-mm isotropic; matrix, 320 134; 1 average; 2 minutes).23 All patients were injected with a single dose of the contrast agent. Gadoterate meglumine (Dotarem; Guerbet, Roissy, France) was injected intravenously as a bolus (0.1 mmol/kg of body weight) at 4 mL/s, followed by a 20-mL saline flush using a power injector (Spectris Solaris EP; Medrad, Pittsburgh, PA). Application of the contrast agent was started at 75 seconds after starting the first coronal T1-weighted volume interpolated breathhold examination. The total examination time for the MP MRI protocol was approximately 34 minutes.

Data Analysis Multiparametric MRI data were prospectively evaluated by 2 experienced breast radiologists (K.P., 7 years of experience in breast MRI; T.H., 16 years of experience in breast MRI) in consensus as previous studies have shown very good intraobserver and interobserver agreement by using the following evaluation of each MRI parameter.8,14 The readers were aware that the patients had a breast lesion, but they were not provided with the previous mammographic and sonographic imaging data or histopathological results.

MRI Parameters DCE-MRI of the Breast To distinguish between benign and malignant contrastenhancing lesions descriptors according to the American College of Radiology, BI-RADS lexicon was used.24,25 Lesions were classified as masses or nonYmass like enhancing lesions (NMLEs). According to the American College of Radiology MRI BI-RADS lexicon, the following descriptors were assessed for masses: shape (round, oval, lobulated, 422

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irregular), margin (smooth, irregular, spiculated), enhancement pattern (homogenous, heterogeneous), and enhancement kinetics (persistent enhancement [type I], initial strong enhancement and plateau phase [type II], initial strong enhancement and wash-out [type III]).26 For NMLE, the distribution (focal, regional, mutiple regions; segmental, ductal, linear, and diffuse), the pattern of enhancement (homogenous, heterogeneous, clumped, and stippled), and the symmetry were assessed. Lesion enhancement kinetics were used to assess functional information. Regions of interest (ROIs) were manually drawn in the most enhancing part of a lesion, and intensity courses were plotted against time. For NMLE, the enhancements kinetics were not taken into account. The probability of malignancy was determined by assigning a final BI-RADS category.6,7,24 In addition, the lesion size measuring the largest diameter in 1 plane was assessed.

Diffusion-Weighted Imaging When assessing water diffusivity, highYb value (ie, 850 s/mm2) images were visually evaluated for hyperintense regions that corresponded to enhancing lesions on DCE MRI. Three-dimensional ROIs were drawn manually on ADC maps in all lesions. Partial volume effects due to normal parenchyma at the border of the lesion and areas of necrotic tissue identified in the morphological and contrast-enhanced images were avoided. The ROIs were defined as slightly smaller than the actual lesions to avoid partial-volume effects. Bogner et al8 evaluated the diagnostic quality of DWI regarding apparent ADC accuracy. On the basis of receiver operating characteristic (ROC) curves, the optimal ADC threshold of 1.25 10j3 mm2/s for the differentiation between benign and malignant breast lesions was determined. This ADC threshold was applied in the current study. Lesions were classified as benign if ADC values were equal to or greater than 1.25 10j3 mm2/s and malignant if less than 1.25 10j3 mm2/s.

3D 1H-MRSI An experienced spectroscopist (S.G., more than 10 years of experience) evaluated the 3D 1H-MRSI data. For the assessment of metabolic information, all 3D 1H-MRSI voxels in the lesion volume as detected with DCE MRI were evaluated for elevated levels of Cho. The voxel with the maximum Cho signal-to-noise ratio (SNR) was determined. Gruber et al12 evaluated the diagnostic accuracy of 3D 1 H-MRSI for the differentiation of benign and malignant breast lesions, on the basis of Cho SNR threshold levels, defining a threshold of 2.6. Choline SNR measurements are a commonly accepted approach in MR spectroscopy of the breast.20 This Cho SNR threshold level was applied in the current study. A lesion was classified as malignant if SNR was equal to or greater than 2.6 and benign if SNR was lower than 2.6.

MP MRI With 2 Parameters Dynamic Contrast-Enhanced MRI and DWI Multiparametric MRI with DCE MRI and DWI was considered positive if one or more MRI parameter was indicative of malignancy.

Dynamic Contrast-Enhanced MRI and 3D 1H-MRSI Multiparametric MRI with DCE MRI and 3D 1H-MRSI was considered positive if one or more MR imaging parameter was indicative of malignancy.

DWI and 3D 1H-MRSI In the current study, interpretation of DWI and 3D 1H-MRSI data was always performed after identification of lesions on DCE MR images. Therefore, these 2 parameters were not assessed individually or in combination without the information provided by DCE MRI. * 2014 Lippincott Williams & Wilkins




MP MRI With 3 Parameters For the assessment of MP MRI with all 3 parameters (DCE MRI, DWI, and 3D 1H-MRSI), the following reading scheme was used: If the results of DCE MRI, DWI, and 1H-MRSI of the breast were positive, MP MRI was considered positive for malignancy (Fig. 1). If the results of DCE MRI, DWI, and 1H-MRSI of the breast were negative, MP MRI was considered negative for malignancy.

Diagnostic Accuracy of Multiparametric Breast MRI

If 2 of 3 parameters were positive, MP MRI was considered positive for malignancy (Figs. 2, 3). If 2 of the 3 parameters were negative, MP MRI was considered negative for malignancy.

Histopathology The final diagnosis was established through histopathology. All cases were read by 1 pathologist (Z.B-H, 6 years of experience in breast pathology).

FIGURE 1. Invasive ductal carcinoma G3 in a 67-year-old woman retroareolar in the left breast. A to D, The indistinct irregular mass lesion (large arrow) demonstrates heterogeneous initial strong enhancement followed by a wash-out and is classified as BI-RADS 5 (suspicious finding) through DCE MRI of the breast. Note the clumped NMLE in the anterior aspect of the lesions representing an extensive intraductal component (small arrow) (E). The mass demonstrates low ADC values (0.988 10j3 mm2/s). F, In 3D 1H-MRSI, the lesion presents with a Cho peak at 3.23 ppm (SNR, 7.45). Multiparametric MRI with 3 parameters accurately classifies the tumor as malignant. * 2014 Lippincott Williams & Wilkins



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FIGURE 2. Melanoma metastasis in a 39-year-old woman in the left breast retroareolar. A to D, The oval smooth mass (arrow) shows a persistent homogenous enhancement and is classified through DCE MRI of the breast as BI-RADS 2 (benign). E, The lesion demonstrates decreased ADC values (0.646 10j3 mm2/s). F, In 3D 1H-MRSI, elevated Cho is observed at 3.23 ppm (SNR, 10.2). Multiparametric MRI with 3 parameters accurately classifies the tumor as malignant.

All patients underwent image-guided needle biopsy,27 surgical biopsy, mastectomy, or lumpectomy. In case of a benign histopathological diagnosis at image-guided needle biopsy, the final diagnosis was benign (n = 31). In case of discordant findings between histopathological diagnosis and imaging findings, the final diagnosis was established with open surgery (n = 4). In case of a high-risk lesion, which has an uncertain potential for malignancy, the final diagnosis was established with open surgery (n = 4).27,28

Statistical Methods Statistical analysis was performed by a statistician (M.W.), using Statistical Package for the Social Sciences 19.0 and CIA 2.2.0. 424


All calculations were performed on a per-lesion basis. To calculate the sensitivity and specificity of DCE MRI of the breast, the assigned BI-RADS classifications for MRI data were dichotomized. Magnetic resonance imaging BI-RADS 1, 2, and 3 were considered as benign. Magnetic resonance imaging BI-RADS 4 and 5 were considered as malignant. Sensitivity, specificity, accuracy, negative predictive value (NPV), positive predictive value (PPV), and their 95% confidence intervals for DCE MRI of the breast and MP MRI with 2 and 3 parameters were calculated. Histopathology was used as the criterion standard. Significant differences in sensitivity, specificity, NPV, PPV, and diagnostic accuracy were assessed through logistic regression for multiple measures * 2014 Lippincott Williams & Wilkins





FIGURE 3. Fibroadenoma in a 48-year-old woman in the 2-o’clock position of the right breast. A to D, The slightly irregular partly indistinct mass (arrow) with a heterogeneous initial strong enhancement and plateau curve is classified through DCE MRI of the breast as BI-RADS 4 (probably malignant). E, The mass lesion shows high ADC values (1.55 10j3 mm2/s). F, There is no Cho peak depicted at 3.23 ppm in 3D 1H-MRSI. The lesion is false positive in DCE MRI but true negative in DWI and 3D 1H-MRSI. Therefore, MP MRI with 3 parameters accurately classifies the tumor as benign.

(generalized estimating equations). Receiver operating characteristic curves were plotted, and the area under the curve (AUC) was determined. Statistical differences between the AUCs were assessed using the method proposed by DeLong et al.29 A P value of less than or equal to 0.05 was considered a significant result.

RESULTS There were 113 lesions with a lesion size ranging from 5 to 98 mm (mean, 29 mm). Histopathologic diagnosis revealed 74 lesions to be * 2014 Lippincott Williams & Wilkins

malignant and 39 to be benign (Table 1). There were 98 enhancing masses (size range, 5Y98 mm; mean, 28.1 mm) and 15 NMLE (size range, 5Y89 mm; mean, 36.8 mm) at DCE MRI of the breast. Dynamic contrast-enhanced MRI classified 87 lesions as malignant (MRI BI-RADS 4 and 5) and 26 lesions as benign (MRI BIRADS 2 and 3). The benign mass lesions showed type 1 curve in 15 (46.9%) of 32, type 2 curve in 13 (40.6%) of 32, and type 3 curve in 4 (12.5%) of 32. Curve types in the malignant mass lesions were distributed as follows: type 1 in 14 (21.2%) of 66, type 2 in 22 (33.3%) of 66, and type 3 in 30 (45.5%) of 66. www.investigativeradiology.com


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TABLE 1. Detailed Histopathological Diagnoses of all Malignant (n = 74) and Benign (n = 39) Breast Lesions Malignant

n

%

74

65.5

Histopathological subtype IDC ILC Mucinous carcinoma Papillary carcinoma Phylloid high grade Metastasis DCIS

52 11 2 1 1 1 6

46 9.7 1.8 0.9 0.9 0.9 5.3

Benign

n

%

39 3

34.5 2.7

18 2 9 7

15.9 1.8 7.9 6.2


Multiparametric MRI with 2 parameters, that is, DCE MRI and DWI or 3D 1H-MRSI, did not improve diagnostic accuracy (AUC, 0.808) compared with DCE MRI alone (P = 0.323). Diagnostic accuracy of MP MRI with 3 parameters was significantly higher compared with MP MRI with 2 parameters (P G 0.001).

Sensitivity, Specificity, NPV, and PPV

High risk (complex sclerosing lesion, FEA, CCC with focal atypia) FA/FAH Papilloma DH, CCC, FCC, focal fibrosis, nodular sclerosing adenosis Miscellaneous (chronic abscess, gynecomastia, fat necrosis, pseudoangiomatosis)

CCC indicates columnar cell changes; DCIS, ductal carcinoma in situ; DH, ductal hyperplasia; FA, fibroadenoma; FAH, fibroadenomatous hyperplasia; FCC, fibrocystic changes; FEA, flat epithelial atypia; IDC, invasive ductal carcinoma; ILC, invasive lobular carcinoma.

Multiparametric MRI with 3 parameters, that is, DCE MRI, DWI, and 3D 1H-MRSI, yielded the highest sensitivity (100%), which was maximized compared with DCE MRI with 98.6%. Multiparametric MRI with 3 parameters significantly increased specificity to 87.2% compared with DCE-MRI with 64.1% (P G 0.001). Multiparametric MRI with 2 parameters also maximized sensitivity to 100% but showed a decrease in specificity of 61.5%. Positive predictive values of DCE MRI alone and MP MRI with 2 or 3 parameters for breast cancer diagnosis are summarized in Figure 5. The PPV was significantly higher with MP MRI with 3 parameters at 93.7% compared with DCE MRI alone at 83.9% (P G 0.001). Multiparametric MRI with 2 parameters demonstrated lower PPVs of 83.1% compared with DCE MRI alone. The NPV of MP MRI with either 2 or 3 parameters was increased to 100% compared with DCE MRI at 96.2%. Multiparametric MRI with all 3 parameters allowed the elimination of the false-negative finding and a significant reduction in false-positive findings from 14 with DCE MRI and 15 with MP MRI with 2 parameters to 5 with 3 parameters (P = 0.002). Detailed histopathological results for all false-positive and false-negative lesions for DCE MRI and MP MRI are listed in Table 3.

DISCUSSION The ADC values of the benign lesions ranged from 0.98 to 2.54 10j3 mm2/s (mean, 1.61 10j3 mm2/s); those of the malignant lesions, from 0.4 to 1.62 (mean, 0934 10j3 mm2/s). Choline SNR values of the benign lesions ranged from 0 to 5.6 (mean, 0.89); those of the malignant lesions, from 0 to 56.1(mean, 9.98). Sensitivities, specificities, PPV, NPV, diagnostic accuracies, and the AUCs for DCE MRI and MP MRI with 2 or 3 MRI parameters are summarized in Table 2.

Diagnostic Accuracy: ROC Analysis Diagnostic accuracy was significantly higher with MP MRI using 3 parameters, that is, DCE MRI, DWI, and 3D 1H-MRSI, with an AUC of 0.936 compared with DCE MRI alone with an AUC of 0.814 (P G 0.001) (Fig. 4).

The results of our study show that the highest diagnostic accuracy for breast cancer diagnosis is achieved with MP MRI using 3 parameters, that is, DCE MRI, DWI, and 3D 1H-MRSI, with an AUC of 0.936 compared with DCE MRI with an AUC of 0.814 (P G 0.001). Multiparametric MRI with 2 parameters does improve diagnostic accuracy in comparison with DCE-MRI alone. As demonstrated in our study, each MRI parameter has an incremental value,8,12,23,30 but only MP MRI with 3 parameters allows an increase in both sensitivity and particularly specificity, resulting in a significantly improved diagnostic accuracy. With MP MRI using parameters, there were no false negatives and there was a significant reduction in false-positive lesions compared with DCE MRI and MP MRI with 2 parameters. In addition, MP MRI with 3 parameters has the potential to eliminate almost two thirds of unnecessary breast biopsies recommended by DCE MRI alone. The results are in agreement with

TABLE 2. Sensitivities, Specificities, PPV, NPV, Diagnostic Accuracy, AUC, and 95% Confidence Intervals (in Brackets) for DCE-MRI and MP MRI Sensitivity DCE-MRI (1 parameter) MP MRI with DCE MRI and DWI (2 parameters) MP MRI with DCE-MRI and 3D 1H-MRSI (2 parameters) MP MRI with DCE-MRI, DWI, and 3D 1H-MRSI (3 parameters)

98.6% (92.7%Y99.8%) 100% (93.6%Y100%) 100% (93.6%Y100%) 100% (95.1%Y100%)

Specificity 64.1% (48.4%Y77.3%) 61.5% (44.7%Y76.2%) 61.5% (45.9%Y75.1%) 87.2%*† (73.3%Y94.4%)

PPV 83.9% (74.8%Y90.2%) 83.1% (74%Y89.5%) 83.1% (74%Y89.5%) 93.7%*† (86.0%Y97.3%)

NPV

Accuracy

96.2% (81.1%Y99.3%) 100% (86.2%Y100%) 100% (86.2%Y100%) 100% (89.8%Y100%)

86.7% (79.2%Y91.8%) 86.7% (79.2%Y91.8%) 86.7% (79.2%Y91.8%) 95.6%*† (90.1%Y98.1%)

AUC 0.814 (0.72Y0.91) 0.808 (0.71Y0.91) 0.808 (0.71Y0.91) 0.936*† (0.87Y0.99)

*Significantly different from DCE-MRI (P G 0.001). †Significantly different from MP MRI with 2 MR imaging parameters (P G 0.001). 3D 1H-MRSI indicates 3-dimensional proton spectroscopic imaging; AUC, area under the curve; DCE MRI, dynamic contrast-enhanced magnetic resonance imaging; DWI, diffusion-weighted imaging; MP, multiparametric; MRI, magnetic resonance imaging; NPV, negative predictive value; PPV, positive predictive value.

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FIGURE 4. The ROC curves illustrate the higher diagnostic value (that is, higher sensitivity, specificity, and larger AUC) that is reached for MP MRI with 3 parameters compared with DCE MRI of the breast and MP MRI with 2 parameters.

studies that have proven the additional value of MP MRI with more than 2 parameters in other neoplastic diseases such as brain and prostate cancer.31Y37 It is evident that MP MRI leads to an increased scan time; however, the increase in diagnostic accuracy resulting in a reduction of unnecessary breast biopsies is beneficial for the patient and might even be cost-effective, although no formal cost-effectiveness analysis was performed. Even with the use of MP MRI, there were some false-positive lesions. These comprised of 3 high-risk lesions at biopsy, which remained lesions with atypia at surgery.38,39 Studies have demonstrated that high-risk lesions such as atypical ductal hyperplasia are genetically advanced nonYobligatory precursors of invasive breast cancer.40 The fact that these lesions remained false positive with MP MRI with all 3 parameters possibly reflects malignant potential. Another false positive was a very metabolically active juvenile fibroadenoma with a high cellularity.41,42 The other remaining false-positive lesion was a clinically


asymptomatic chronic abscess, which demonstrated suspicious morphologic and kinetic features and decreased ADC levels,43,44 but was negative on 3D 1H-MRSI. In the past, several studies have evaluated the role of MP MRI with 2 parameters, that is, DCE MRI and either DWI or 1H-MRSI, in breast cancer diagnosis. Kul et al11 investigated the combination of DCE MRI and DWI in the assessment of breast tumors. The authors reported an increase in specificity of 86.5% but a decrease in sensitivity of 95.2%. Yabuuchi et al21,45 and Ei Khouli et al,9 who used a logistic regression model, reported similar results for MP MRI with DCE MRI and DWI. In all these studies, the improvement of specificity led to a trade-off in sensitivity. In contrast, the combination approach in this study led to an increase in sensitivity to 100% but no increase in specificity. Several groups have evaluated the diagnostic accuracy of 1HMRSI with encouraging results. A recent meta-analysis reported pooled estimates of sensitivity and specificity of 73 and 88% and demonstrated that 1H-MRSI has a high specificity for diagnosis of breast cancer.20 Potentially, this specificity may further increase diagnostic accuracy in breast imaging. However, a practical algorithm for the combination of DCE MRI and 1H-MRSI has not yet been proposed. According to Gruber et al,12 3D 1H-MRSI has a higher sensitivity of 97%. We applied the same 3D 1H-MRSI technique, and our results are concordant with these findings. It has to be kept in mind that MR spectroscopy even if performed as a 3D technique is limited to a certain region of one breast. As a consequence, it can only be considered as an additional tool for the characterization of lesions in a prior known location. This limits its application for the characterization of incidental small and nonYmasslike enhancements. In the current study, interpretation of DWI and 3D 1H-MRSI data was always performed after identification of lesions on DCE MR images. There have been approaches described for lesion identification in DWI on high b-value images,22,46 but no data exist for the application of 3D 1H-MRSI in breast imaging without lesion identification on DCE MR images. Because lesion identification on DCE MR images is the most commonly used approach, we chose to use this in the current study; therefore, DWI and 3D 1H-MRSI were not assessed individually or in combination. The data suggest that MP MRI with 2 parameters is not as beneficial as MP MRI with 3 parameters with respect to diagnostic accuracy. However, there are other approaches for the combination of quantitative multiparametric data than the empirical approach chosen in this study. Methods such as classification trees, logistic regression, or artificial neural networks have been described. These methods are able to combine raw quantitative data and assign a probability of malignancy. Although desirable, these approaches are limited

FIGURE 5. Positive predictive values of DCE MRI and MP MRI with either 3 or 2 parameters. * 2014 Lippincott Williams & Wilkins



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TABLE 3. Detailed Histopathological Results of False-Negative and False-Positive Lesions 1

False Postives

Mass/NMLE

Size (mm)

n

False Negatives

Mass/NMLE

Size (mm)

n

DCE-MRI (1 parameter)

FA FA FA FAH DH, CCC DH, CCC DH, CCC Miscellaneous Miscellaneous Miscellaneous High risk High risk High risk High risk FA FA FA FAH DH, CCC DH, CCC DH, CCC DH, CCC Miscellaneous Miscellaneous Miscellaneous High risk High risk High risk High risk FA FA FA FA FAH DH, CCC DH, CCC DH, CCC Miscellaneous Miscellaneous Miscellaneous High risk High risk High risk High risk FA Miscellaneous High risk High risk High risk

Mass Mass NMLE Mass Mass NMLE NMLE Mass Mass NMLE Mass Mass Mass NMLE Mass Mass NMLE Mass Mass Mass NMLE NMLE Mass Mass NMLE Mass Mass Mass NMLE Mass Mass Mass NMLE Mass Mass NMLE NMLE Mass Mass NMLE Mass Mass Mass NMLE NMLE Mass Mass Mass NMLE

20 28 39 10 8 10 20 20 90 19 7 8 10 58 20 28 39 10 8 8 10 20 20 90 19 7 8 10 58 20 28 36 39 10 8 10 20 20 90 19 7 8 10 58 20 90 7 8 50

14

metastasis

1

25

1

MP MRI with DCE-MRI and DWI (2 parameters)

MP MRI with DCE-MRI and 3D 1H-MRSI (2 parameters)

MP MRI with DCE-MRI, DWI, and 3D 1H-MRSI (3 parameters)

15

0

15

0

5

0

3D 1H-MRSI indicates 3-dimensional proton MR spectroscopic imaging, CCC, columnar cell changes; DCE-MRI, dynamic contrast-enhanced magnetic resonance imaging; DH, ductal hyperplasia; DWI, diffusion-weighted imaging; FA, fibroadenoma; FAH, fibroadenomatous hyperplasia; FCC, fibrocystic changes; IDC, invasive ductal carcinoma; NMLE, nonYmasslike enhancing lesions.

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because no internationally accepted reference values for quantitative MP MRI results do exist. Providing such values in a multiparametric approach would require case numbers beyond the scope of this study. To date, because of these limitations, only little data on multivariate data combinations and no data using MP MRI has been published in breast MRI.47,48 The approach used in the current study was defined before commencement of the study, was readily applicable in clinical practice, and achieved high diagnostic accuracies. There was a relatively small number of pure ductal carcinoma in situ and invasive lobular cancers as compared with the number of invasive ductal cancers. Consequently, there was also a relatively small number of NMLEs, which often represent ductal carcinoma in situ and invasive lobular carcinoma. This limits the specific insights into the performance of MP MRI in these subgroups. However, in a prospective study with consecutive patients, the collective distribution of malignant lesion subtypes is as expected and underlines the representativeness of our patient sample. In addition, the readers in this study were experienced breast radiologists with extensive training in the interpretation of breast MR images; therefore, the results might not be applicable to every radiologist. However, it can be expected that dedicated training and the addition of computer-aided detection systems that implement all MR imaging parameters will be helpful for radiologists in everyday practice. All MR examinations were performed at 3 T instead of 1.5 T, which is the most commonly used field strength in breast MRI. This should not be seen as a limitation because 3 T offers advantages such as an improved temporal and spatial resolution.6,8,12,14,23

CONCLUSIONS In conclusion, MP MRI with 3 parameters (DCE-MRI, DWI, and 3D-1H-MRSI) has a greater diagnostic accuracy for breast cancer diagnosis than DCE-MRI alone or MP MRI with only 2 MRI parameters. ACKNOWLEDGMENTS The authors thank Dr Oshaani Abeyakoon for helping with the editing of the manuscript of this article. REFERENCES 1. Sardanelli F, Boetes C, Borisch B, et al. Magnetic resonance imaging of the breast: recommendations from the EUSOMA working group. Eur J Cancer. 2010;46:1296Y1316. 2. Kuhl CK. Breast MR imaging at 3T. Magn Reson Imaging Clin N Am. 2007;15: 315Y320. 3. Morris EA. Diagnostic breast MR imaging: current status and future directions. Magn Reson Imaging Clin N Am. 2007;45:863Y880. 4. Mercado CL, Moy L. Breast MRI. Preface. Magn Reson Imaging Clin N Am. 2010;18(2):xiii. doi: 10.1016/j.mric.2010.02.014. 5. Pediconi F, Miglio E, Telesca M, et al. Effect of preoperative breast magnetic resonance imaging on surgical decision making and cancer recurrence rates. Invest Radiol. 2012;47:128Y135. 6. Pinker-Domenig K, Bogner W, Gruber S, et al. High resolution MRI of the breast at 3 T: which BI-RADS(R) descriptors are most strongly associated with the diagnosis of breast cancer? Eur Radiol. 2012;22:322Y330. 7. Kuhl CK, Schild HH, Morakkabati N. Dynamic bilateral contrast-enhanced MR imaging of the breast: trade-off between spatial and temporal resolution. Radiology. 2005;236:789Y800. 8. Bogner W, Gruber S, Pinker K, et al. Diffusion-weighted MR for differentiation of breast lesions at 3.0 T: how does selection of diffusion protocols affect diagnosis? Radiology. 2009;253:341Y351. 9. Ei Khouli RH, Jacobs MA, Mezban SD, et al. Diffusion-weighted imaging improves the diagnostic accuracy of conventional 3.0-T breast MR imaging. Radiology. 2010;256:64Y73. 10. Partridge SC, DeMartini WB, Kurland BF, et al. Quantitative diffusionweighted imaging as an adjunct to conventional breast MRI for improved positive predictive value. AJR Am J Roentgenol. 2009;193:1716Y1722. 11. Kul S, Cansu A, Alhan E, et al. Contribution of diffusion-weighted imaging to dynamic contrast-enhanced MRI in the characterization of breast tumors. AJR Am J Roentgenol. 2011;196:210Y217.



12. Gruber S, Debski BK, Pinker K, et al. Three-dimensional proton MR spectroscopic imaging at 3 T for the differentiation of benign and malignant breast lesions. Radiology. 2011;261:752Y761. 13. Bartella L, Morris EA, Dershaw DD, et al. Proton MR spectroscopy with choline peak as malignancy marker improves positive predictive value for breast cancer diagnosis: preliminary study. Radiology. 2006;239:686Y692. 14. Bogner W, Pinker-Domenig K, Bickel H, et al. Readout-segmented echo-planar imaging improves the diagnostic performance of diffusion-weighted MR breast examinations at 3.0 T. Radiology. 2012;263:64Y76. 15. Eyal E, Shapiro-Feinberg M, Furman-Haran E, et al. Parametric diffusion tensor imaging of the breast. Invest Radiol. 2012;47:284Y291. 16. Woodhams R, Matsunaga K, Kan S, et al. ADC mapping of benign and malignant breast tumors. Magn Reson Med Sci. 2005;4:35Y42. 17. Chen X, Li WL, Zhang YL, et al. Meta-analysis of quantitative diffusionweighted MR imaging in the differential diagnosis of breast lesions. BMC Cancer. 2010;10:693. 18. Meisamy S, Bolan PJ, Baker EH, et al. Adding in vivo quantitative 1H MR spectroscopy to improve diagnostic accuracy of breast MR imaging: preliminary results of observer performance study at 4.0 T. Radiology. 2005;236: 465Y475. 19. Sardanelli F, Fausto A, Di Leo G, et al. In vivo proton MR spectroscopy of the breast using the total choline peak integral as a marker of malignancy. AJR Am J Roentgenol. 2009;192:1608Y1617. 20. Baltzer PA, Dietzel M. Breast lesions: diagnosis by using proton mr spectroscopy at 1.5 and 3.0 TVsystematic review and meta-analysis. Radiology. 2013;267:735Y746. 21. Yabuuchi H, Matsuo Y, Okafuji T, et al. Enhanced mass on contrast-enhanced breast MR imaging: lesion characterization using combination of dynamic contrast-enhanced and diffusion-weighted MR images. J Magn Reson Imaging. 2008;28:1157Y1165. 22. Yabuuchi H, Matsuo Y, Sunami S, et al. Detection of non-palpable breast cancer in asymptomatic women by using unenhanced diffusion-weighted and T2-weighted MR imaging: comparison with mammography and dynamic contrast-enhanced MR imaging. Eur Radiol. 2011;21:11Y17. 23. Pinker K, Grabner G, Bogner W, et al. A combined high temporal and high spatial resolution 3 Tesla MR imaging protocol for the assessment of breast lesions: initial results. Invest Radiol. 2009;44:553Y558. 24. American College of Radiology (ACR). Breast Imaging and Reporting Data System (BI-RADS). Reston, VA: American College of Radiology; 2003. 25. Tardivon AA, Athanasiou A, Thibault F, et al. Breast imaging and reporting data system (BIRADS): magnetic resonance imaging. Eur J Radiol. 2007;61: 212Y215. 26. Kuhl CK, Mielcareck P, Klaschik S, et al. Dynamic breast MR imaging: are signal intensity time course data useful for differential diagnosis of enhancing lesions? Radiology. 1999;211:101Y110. 27. Wallis M, Tardivon A, Helbich T, et al. Guidelines from the European Society of Breast Imaging for diagnostic interventional breast procedures. Eur Radiol. 2007;17:581Y588. 28. Krishnamurthy S, Bevers T, Kuerer H, et al. Multidisciplinary considerations in the management of high-risk breast lesions. AJR Am J Roentgenol. 2012;198: W132YW140. 29. DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44:837Y845. 30. Renz DM, Diekmann F, Schmitzberger FF, et al. Pharmacokinetic approach for dynamic breast MRI to indicate signal intensity time curves of benign and malignant lesions by using the tumor flow residence time. Invest Radiol. 2013;48:69Y78. 31. Oliveira Neto JA, Parente DB. Multiparametric magnetic resonance imaging of the prostate. Magn Reson Imaging Clin N Am. 2013;21:409Y426. 32. Hedgire SS, Oei TN, McDermott S, et al. Multiparametric magnetic resonance imaging of prostate cancer. Indian J Radiol Imaging. 2012;22:160Y169. 33. Turkbey B, Mani H, Shah V, et al. Multiparametric 3T prostate magnetic resonance imaging to detect cancer: histopathological correlation using prostatectomy specimens processed in customized magnetic resonance imaging based molds. J Urol. 2011;186:1818Y1824. 34. Franiel T, Stephan C, Erbersdobler A, et al. Areas suspicious for prostate cancer: MR-guided biopsy in patients with at least one transrectal US-guided biopsy with a negative findingVmultiparametric MR imaging for detection and biopsy planning. Radiology. 2011;259:162Y172. 35. Bian W, Khayal IS, Lupo JM, et al. Multiparametric characterization of grade 2 glioma subtypes using magnetic resonance spectroscopic, perfusion, and diffusion imaging. Transl Oncol. 2009;2:271Y280. 36. Roy B, Gupta RK, Maudsley AA, et al. Utility of multiparametric 3-T MRI for glioma characterization. Neuroradiology. 2013;55:603Y613. 37. Pinker K, Stadlbauer A, Bogner W, et al. Molecular imaging of cancer: MR spectroscopy and beyond. Eur J Radiol. 2012;81:566Y577. 38. Kluttig A, Trocchi P, Heinig A, et al. Reliability and validity of needle biopsy evaluation of breast-abnormalities using the B-categorizationVdesign and objectives of the Diagnosis Optimisation Study (DIOS). BMC Cancer. 2007;7:100.



429


Pinker et al

39. Bianchi S, Caini S, Renne G, et al. Positive predictive value for malignancy on surgical excision of breast lesions of uncertain malignant potential (B3) diagnosed by stereotactic vacuum-assisted needle core biopsy (VANCB): a large multi-institutional study in Italy. Breast. 2011;20:264Y270. 40. Riedl CC, Ponhold L, Flory D, et al. Magnetic resonance imaging of the breast improves detection of invasive cancer, preinvasive cancer, and premalignant lesions during surveillance of women at high risk for breast cancer. Clin Cancer Res. 2007;13:6144Y6152. 41. Hochman MG, Orel SG, Powell CM, et al. Fibroadenomas: MR imaging appearances with radiologic-histopathologic correlation. Radiology. 1997;204: 123Y129. 42. Kuijper A, Mommers EC, van der Wall E, et al. Histopathology of fibroadenoma of the breast. Am J Clin Pathol. 2001;115:736Y742. 43. Unal O, Koparan HI, Avcu S, et al. The diagnostic value of diffusion-weighted magnetic resonance imaging in soft tissue abscesses. Eur J Radiol. 2011;77: 490Y494.

430



44. Oto A, Schmid-Tannwald C, Agrawal G, et al. Diffusion-weighted MR imaging of abdominopelvic abscesses. Emerg Radiol. 2011;18:515Y524. 45. Yabuuchi H, Matsuo Y, Kamitani T, et al. Non-mass-like enhancement on contrast-enhanced breast MR imaging: lesion characterization using combination of dynamic contrast-enhanced and diffusion-weighted MR images. Eur J Radiol. 2010;75:e126Ye132. 46. Baltzer PA, Benndorf M, Dietzel M, et al. Sensitivity and specificity of unenhanced MR mammography (DWI combined with T2-weighted TSE imaging, ueMRM) for the differentiation of mass lesions. Eur Radiol. 2010;20: 1101Y1110. 47. Dietzel M, Baltzer PA, Dietzel A, et al. Artificial neural networks for differential diagnosis of breast lesions in MR-mammography: a systematic approach addressing the influence of network architecture on diagnostic performance using a large clinical database. Eur J Radiol. 2012;81:1508Y1513. 48. Baltzer PA, Dietzel M, Kaiser WA. A simple and robust classification tree for differentiation between benign and malignant lesions in MR-mammography. Eur Radiol. 2013;23:2051Y2060.



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