Influence of residual fat signal on diffusion kurtosis MRI of suspicious mammography findings.

Recent studies showed the potential of diffusion kurtosis imaging (DKI) as a tool for improved classification of suspicious breast lesions. However, in diffusion-weighted imaging of the female breast, sufficient fat suppression is one of the main factors determining the success. In this study, the data of 198 patients examined in two study centres was analysed using standard diffusion and kurtosis evaluation methods and three DKI fitting approaches accounting phenomenologically for fat-related signal contamination of the lesions. Receiver operating characteristic curve analysis showed the highest area under the curve (AUC) for the method including fat correction terms (AUC = 0.85, p < 0.015) in comparison to the values obtained with the standard diffusion (AUC = 0.77) and kurtosis approach (AUC = 0.79). Comparing the two study centres, the AUC value improved from 0.77 to 0.86 (p = 0.036) using a fat correction term for the first centre, while no significant difference with no adverse effects was observed for the second centre (AUC 0.89 vs. 0.90, p = 0.95). Contamination of the signal in breast lesions with unsuppressed fat causing a reduction of diagnostic performance of diffusion kurtosis imaging may potentially be counteracted by proposed adapted evaluation methods.

Threshold Isocontouring on High b-Value Diffusion-Weighted Images in Magnetic Resonance Mammography.

OBJECTIVES: Motivated by the similar appearance of malignant breast lesions in high b-value diffusion-weighted imaging (DWI) and positron emission tomography, the purpose of this work was to evaluate the applicability of a threshold isocontouring approach commonly used in positron emission tomography to analyze DWI data acquired from female human breasts with minimal interobserver variability. METHODS: Twenty-three female participants (59.4 ± 10.0 years) with 23 lesions initially classified as suggestive of cancers in x-ray mammography screening were subsequently imaged on a 1.5-T magnetic resonance imaging scanner. Diffusion-weighted imaging was performed prior to biopsy with b values of 0, 100, 750, and 1500 s/mm. Isocontouring with different threshold levels was performed on the highest b-value image to determine the voxels used for subsequent evaluation of diffusion metrics. The coefficient of variation was computed by specifying 4 different regions of interest drawn around the lesion. Additionally, a receiver operating statistical analysis was performed. RESULTS: Using a relative threshold level greater than or equal to 0.85 almost completely suppresses the intra-individual and inter-individual variability. Among 4 studied diffusion metrics, the diffusion coefficients from the intravoxel incoherent motion model returned the highest area under curve value of 0.9. The optimal cut-off diffusivity was found to be 0.85 µm/ms with a sensitivity of 87.5% and specificity of 90.9%. CONCLUSION: Threshold isocontouring on high b-value maps is a viable approach to reliably evaluate DWI data of suspicious focal lesions in magnetic resonance mammography.

Radiomics Based on Adapted Diffusion Kurtosis Imaging Helps to Clarify Most Mammographic Findings Suspicious for Cancer.

Purpose To evaluate a radiomics model of Breast Imaging Reporting and Data System (BI-RADS) 4 and 5 breast lesions extracted from breast-tissue-optimized kurtosis magnetic resonance (MR) imaging for lesion characterization by using a sensitivity threshold similar to that of biopsy. Materials and Methods This institutional study included 222 women at two independent study sites (site 1: training set of 95 patients; mean age ± standard deviation, 58.6 years ± 6.6; 61 malignant and 34 benign lesions; site 2: independent test set of 127 patients; mean age, 58.2 years ± 6.8; 61 malignant and 66 benign lesions). All women presented with a finding suspicious for cancer at x-ray mammography (BI-RADS 4 or 5) and an indication for biopsy. Before biopsy, diffusion-weighted MR imaging (b values, 0-1500 sec/mm2) was performed by using 1.5-T imagers from different MR imaging vendors. Lesions were segmented and voxel-based kurtosis fitting adapted to account for fat signal contamination was performed. A radiomics feature model was developed by using a random forest regressor. The fixed model was tested on an independent test set. Conventional interpretations of MR imaging were also assessed for comparison. Results The radiomics feature model reduced false-positive results from 66 to 20 (specificity 70.0% [46 of 66]) at the predefined sensitivity of greater than 98.0% [60 of 61] in the independent test set, with BI-RADS 4a and 4b lesions benefiting from the analysis (specificity 74.0%, [37 of 50]; 60.0% [nine of 15]) and BI-RADS 5 lesions showing no added benefit. The model significantly improved specificity compared with the median apparent diffusion coefficient (P < .001) and apparent kurtosis coefficient (P = .02) alone. Conventional reading of dynamic contrast material-enhanced MR imaging provided sensitivity of 91.8% (56 of 61) and a specificity of 74.2% (49 of 66). Accounting for fat signal intensity during fitting significantly improved the area under the curve of the model (P = .001). Conclusion A radiomics model based on kurtosis diffusion-weighted imaging performed by using MR imaging machines from different vendors allowed for reliable differentiation between malignant and benign breast lesions in both a training and an independent test data set. © RSNA, 2018 Online supplemental material is available for this article.

On a fractional order calculus model in diffusion weighted breast imaging to differentiate between malignant and benign breast lesions detected on X-ray screening mammography.

OBJECTIVE: To evaluate a fractional order calculus (FROC) model in diffusion weighted imaging to differentiate between malignant and benign breast lesions in breast cancer screening work-up using recently introduced parameters (ßFROC, DFROC and µFROC). MATERIALS AND METHODS: This retrospective analysis within a prospective IRB-approved study included 51 participants (mean 58.4 years) after written informed consent. All patients had suspicious screening mammograms and indication for biopsy. Prior to biopsy, full diagnostic contrast-enhanced MRI examination was acquired including diffusion-weighted-imaging (DWI, b = 0,100,750,1500 s/mm2). Conventional apparent diffusion coefficient Dapp and FROC parameters (ßFROC, DFROC and µFROC) as suggested further indicators of diffusivity components were measured in benign and malignant lesions. Receiver operating characteristics (ROC) were calculated to evaluate the diagnostic performance of the parameters. RESULTS: 29/51 patients histopathologically revealed malignant lesions. The analysis revealed an AUC for Dapp of 0.89 (95% CI 0.80-0.98). For FROC derived parameters, AUC was 0.75 (0.60-0.89) for DFROC, 0.59 (0.43-0.75) for ßFROC and 0.59 (0.42-0.77) for µFROC. Comparison of the AUC curves revealed a significantly higher AUC of Dapp compared to the FROC parameters DFROC (p = 0.009), ßFROC (p = 0.003) and µFROC (p = 0.001). CONCLUSION: In contrast to recent description in brain tumors, the apparent diffusion coefficient Dapp showed a significantly higher AUC than the recently proposed FROC parameters ßFROC, DFROC and µFROC for differentiating between malignant and benign breast lesions. This might be related to the intrinsic high heterogeneity within breast tissue or to the lower maximal b-value used in our study.

Prediction of malignancy by a radiomic signature from contrast agent-free diffusion MRI in suspicious breast lesions found on screening mammography.

PURPOSE: To assess radiomics as a tool to determine how well lesions found suspicious on breast cancer screening X-ray mammography can be categorized into malignant and benign with unenhanced magnetic resonance (MR) mammography with diffusion-weighted imaging and T2 -weighted sequences. MATERIALS AND METHODS: From an asymptomatic screening cohort, 50 women with mammographically suspicious findings were examined with contrast-enhanced breast MRI (ceMRI) at 1.5T. Out of this protocol an unenhanced, abbreviated diffusion-weighted imaging protocol (ueMRI) including T2 -weighted, (T2 w), diffusion-weighted imaging (DWI), and DWI with background suppression (DWIBS) sequences and corresponding apparent diffusion coefficient (ADC) maps were extracted. From ueMRI-derived radiomic features, three Lasso-supervised machine-learning classifiers were constructed and compared with the clinical performance of a highly experienced radiologist: 1) univariate mean ADC model, 2) unconstrained radiomic model, 3) constrained radiomic model with mandatory inclusion of mean ADC. RESULTS: The unconstrained and constrained radiomic classifiers consisted of 11 parameters each and achieved differentiation of malignant from benign lesions with a .632 + bootstrap receiver operating characteristics (ROC) area under the curve (AUC) of 84.2%/85.1%, compared to 77.4% for mean ADC and 95.9%/95.9% for the experienced radiologist using ceMRI/ueMRI. CONCLUSION: In this pilot study we identified two ueMRI radiomics classifiers that performed well in the differentiation of malignant from benign lesions and achieved higher performance than the mean ADC parameter alone. Classification was lower than the almost perfect performance of a highly experienced breast radiologist. The potential of radiomics to provide a training-independent diagnostic decision tool is indicated. A performance reaching the human expert would be highly desirable and based on our results is considered possible when the concept is extended in larger cohorts with further development and validation of the technique. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:604-616.

Independent value of image fusion in unenhanced breast MRI using diffusion-weighted and morphological T2-weighted images for lesion characterization in patients with recently detected BI-RADS 4/5 x-ray mammography findings.

OBJECTIVES: The aim of this study was to evaluate the accuracy and applicability of solitarily reading fused image series of T2-weighted and high-b-value diffusion-weighted sequences for lesion characterization as compared to sequential or combined image analysis of these unenhanced sequences and to contrast- enhanced breast MRI. METHODS: This IRB-approved study included 50 female participants with suspicious breast lesions detected in screening X-ray mammograms, all of which provided written informed consent. Prior to biopsy, all women underwent MRI including diffusion-weighted imaging (DWIBS, b = 1500s/mm2). Images were analyzed as follows: prospective image fusion of DWIBS and T2-weighted images (FU), side-by-side analysis of DWIBS and T2-weighted series (CO), combination of the first two methods (CO+FU), and full contrast-enhanced diagnostic protocol (FDP). Diagnostic indices, confidence, and image quality of the protocols were compared by two blinded readers. RESULTS: Reading the CO+FU (accuracy 0.92; NPV 96.1 %; PPV 87.6 %) and the CO series (0.90; 96.1 %; 83.7 %) provided a diagnostic performance similar to the FDP (0.95; 96.1 %; 91.3 %; p > 0.05). FU reading alone significantly reduced the diagnostic accuracy (0.82; 93.3 %; 73.4 %; p = 0.023). CONCLUSIONS: MR evaluation of suspicious BI-RADS 4 and 5 lesions detected on mammography by using a non-contrast-enhanced T2-weighted and DWIBS sequence protocol is most accurate if MR images were read using the CO+FU protocol. KEY POINTS: • Unenhanced breast MRI with additional DWIBS/T2w-image fusion allows reliable lesion characterization. • Abbreviated reading of fused DWIBS/T2w-images alone decreases diagnostic confidence and accuracy. • Reading fused DWIBS/T2w-images as the sole diagnostic method should be avoided.

Applicability and discriminative value of a semiautomatic three-dimensional spherical volume for the assessment of the apparent diffusion coefficient in suspicious breast lesions-feasibility study.

INTRODUCTION: To evaluate the feasibility and accuracy of a semiautomatic, three-dimensional volume of interest (3D sphere) for measuring the apparent diffusion coefficient (ADC) in suspicious breast lesions compared to conventional single-slice two-dimensional regions of interest (2D ROIs). METHOD: This institutional-review-board-approved study included 56 participants with Breast Imaging Reporting and Data System 4/5 lesion. All received diffusion-weighted imaging magnetic resonance imaging prior to biopsy (b=0-1500 s/mm2). ADC values were measured in the lesions with both methods. Reproducibility and accuracies were compared. RESULTS: Area under the curve was 0.93 [95% confidence interval (CI) 0.86-0.99] for the 3D sphere and 0.91 (95% CI 0.84-0.98) for the 2D ROIs without significantly differing reproducibility (P=.45). CONCLUSION: A semiautomatic 3D sphere could reliably estimate ADC values in suspicious breast lesions without significant difference compared to conventional 2D ROIs.

Fast and Noninvasive Characterization of Suspicious Lesions Detected at Breast Cancer X-Ray Screening: Capability of Diffusion-weighted MR Imaging with MIPs.

PURPOSE: To evaluate the ability of a diagnostic abbreviated magnetic resonance (MR) imaging protocol consisting of maximum intensity projections (MIPs) from diffusion-weighted imaging with background suppression (DWIBS) and unenhanced morphologic sequences to help predict the likelihood of malignancy on suspicious screening x-ray mammograms, as compared with an abbreviated contrast material-enhanced MR imaging protocol and a full diagnostic breast MR imaging protocol. MATERIALS AND METHODS: This prospective institutional review board-approved study included 50 women (mean age, 57.1 years; range, 50-69 years), who gave informed consent and who had suspicious screening mammograms and an indication for biopsy, from September 2014 to January 2015. Before biopsy, full diagnostic contrast-enhanced MR imaging was performed that included DWIBS (b = 1500 sec/mm(2)). Two abbreviated protocols (APs) based on MIPs were evaluated regarding the potential to exclude malignancy: DWIBS (AP1) and subtraction images from the first postcontrast and the unenhanced series (AP2). Diagnostic indexes of both methods were examined by using the McNemar test and were compared with those of the full diagnostic protocol and histopathologic findings. RESULTS: Twenty-four of 50 participants had a breast carcinoma. With AP1 (DWIBS), the sensitivity was 0.92 (95% confidence interval [CI]: 0.73, 0.98), the specificity was 0.94 (95% CI: 0.77, 0.99), the negative predictive value (NPV) was 0.92 (95% CI: 0.75, 0.99), and the positive predictive value (PPV) was 0.93 (95% CI: 0.75, 0.99). The mean reading time was 29.7 seconds (range, 4.9-110.0 seconds) and was less than 3 seconds (range, 1.2-7.6 seconds) in the absence of suspicious findings on the DWIBS MIPs. With the AP2 protocol, the sensitivity was 0.85 (95% CI: 0.78, 0.95), the specificity was 0.90 (95% CI: 0.72, 0.97), the NPV was 0.87 (95% CI: 0.69, 0.95), the PPV was 0.89 (95% CI: 0.69, 0.97), and the mean reading time was 29.6 seconds (range, 6.0-100.0 seconds). CONCLUSION: Unenhanced diagnostic MR imaging (DWIBS mammography), with an NPV of 0.92 and an acquisition time of less than 7 minutes, could help exclude malignancy in women with suspicious x-ray screening mammograms. The method has the potential to reduce unnecessary invasive procedures and emotional distress for breast cancer screening participants if it is used as a complement after the regular screening clarification procedure.