Wolfgang Lederer

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.

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

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.

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.

Revealing hidden potentials of the q-space signal in breast cancer

Mammography screening for early detection of breast lesions currently suffers from high amounts of false positive findings, which result in unnecessary invasive biopsies. Diffusion-weighted MR images (DWI) can help to reduce many of these false-positive findings prior to biopsy. Current approaches estimate tissue properties by means of quantitative parameters taken from generative, biophysical models fit to the q-space encoded signal under certain assumptions regarding noise and spatial homogeneity. This process is prone to fitting instability and partial information loss due to model simplicity. We reveal unexplored potentials of the signal by integrating all data processing components into a convolutional neural network (CNN) architecture that is designed to propagate clinical target information down to the raw input images. This approach enables simultaneous and target-specific optimization of image normalization, signal exploitation, global representation learning and classification. Using a multicentric data set of 222 patients, we demonstrate that our approach significantly improves clinical decision making with respect to the current state of the art.

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 (βFROC, 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 (βFROC, 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 βFROC 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), βFROC (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 βFROC, 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.

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.

Domain Adaptation for Deviating Acquisition Protocols in CNN-Based Lesion Classification on Diffusion-Weighted MR Images

End-to-end deep learning improves breast cancer classification on diffusion-weighted MR images (DWI) using a convolutional neural network (CNN) architecture. A limitation of CNN as opposed to previous model-based approaches is the dependence on specific DWI input channels used during training. However, in the context of large-scale application, methods agnostic towards heterogeneous inputs are desirable, due to the high deviation of scanning protocols between clinical sites. We propose model-based domain adaptation to overcome input dependencies and avoid re-training of networks at clinical sites by restoring training inputs from altered input channels given during deployment. We demonstrate the method’s significant increase in classification performance and superiority over implicit domain adaptation provided by training-schemes operating on model-parameters instead of raw DWI images.

Abstract: Revealing Hidden Potentials of the q-Space Signal in Breast Cancer

Mammography screening for early detection of breast lesions currently suffers from high amounts of false positive findings, which result in unnecessary invasive biopsies. Diffusion-weighted MR images (DWI) can help to reduce many of these false-positive findings prior to biopsy. Current approaches estimate tissue properties by means of quantitative parameters taken from generative, biophysical models fit to the q-space signal under certain assumptions regarding noise and spatial homogeneity. This process is prone to fitting instability and partial information loss due to model simplicity.