Multiparametric Breast MRI Research Articles

Radiomic versus Convolutional Neural Networks Analysis for Classification of Contrast-enhancing Lesions at Multiparametric Breast MRI.

Purpose To compare the diagnostic performance of radiomic analysis (RA) and a convolutional neural network (CNN) to radiologists for classification of contrast agent-enhancing lesions as benign or malignant at multiparametric breast MRI. Materials and Methods Between August 2011 and August 2015, 447 patients with 1294 enhancing lesions (787 malignant, 507 benign; median size, 15 mm ± 20) were evaluated. Lesions were manually segmented by one breast radiologist. RA was performed by using L1 regularization and principal component analysis. CNN used a deep residual neural network with 34 layers. All algorithms were also retrained on half the number of lesions (n = 647). Machine interpretations were compared with prospective interpretations by three breast radiologists. Standard of reference was histologic analysis or follow-up. Areas under the receiver operating curve (AUCs) were used to compare diagnostic performance. Results CNN trained on the full cohort was superior to training on the half-size cohort (AUC, 0.88 vs 0.83, respectively; P = .01), but there was no difference for RA and L1 regularization (AUC, 0.81 vs 0.80, respectively; P = .76) or RA and principal component analysis (AUC, 0.78 vs 0.78, respectively; P = .93). By using the full cohort, CNN performance (AUC, 0.88; 95% confidence interval: 0.86, 0.89) was better than RA and L1 regularization (AUC, 0.81; 95% confidence interval: 0.79, 0.83; P < .001) and RA and principal component analysis (AUC, 0.78; 95% confidence interval: 0.76, 0.80; P < .001). However, CNN was inferior to breast radiologist interpretation (AUC, 0.98; 95% confidence interval: 0.96, 0.99; P < .001). Conclusion A convolutional neural network was superior to radiomic analysis for classification of enhancing lesions as benign or malignant at multiparametric breast MRI. Both approaches were inferior to radiologists' performance; however, more training data will further improve performance of convolutional neural network, but not that of radiomics algorithms. © RSNA, 2018 Online supplemental material is available for this article.

Multiparametric Breast MRI Analysis Predicts Axillary Nodal Involvement in Early Stage Breast Cancer

Multiparametric Breast MRI Analysis Predicts Axillary Nodal Involvement in Early Stage Breast Cancer

Multiparametric breast MRI with 3T: Effectivity of combination of contrast enhanced MRI, DWI and 1H single voxel spectroscopy in differentiation of Breast tumors

ObjectivesTo evaluate the diagnostic accuracy of dynamic contrast enhanced breast MRI (DCE-MRI) combined with diffusion weighted imaging (DWI) and 1H single-voxel magnetic resonance spectroscopy (1HMRS) in differentiating malignant from benign breast lesions. MethodsOne hundred twenty-nine patients with 138 lesions were included in the study. Multiparametric MRI of the breast was performed with a 3T unit. A DWI is followed by DCE-MRI and 1HMRS. All lesions were biopsied within one week after MRI. Histopathologic findings were accepted as the standard of reference. Probability of malignancy was assessed according to BI-RADS for DCE-MRI. ADC values were measured for DWI and choline peaks were assessed using a semi-quantitative method in 1HMRS. Two blinded radiologists evaluated findings in consensus. Diagnostic performance of DCE-MRI, DWI and 1HMRS alone or in combination for multiparametric imaging were statistically evaluated. ResultsHistopathology revealed malignancy in 54.4% of lesions (75/138). DCE-MRI showed the highest AUC (0.978), sensitivity (97.33%) and specificity (88.89%) compared to DWI and 1HMRS. Sensitivity was 100% when a positive result from any one of three techniques was accepted as malignancy, albeit with a trade-off for 65.1% specificity. Highest specificity (98.4%) was attained when all three techniques were required to be positive, though with a lower sensitivity (82.7%) as trade-off. Logistic regression analysis confirmed significant association with DCE-MRI (p<0.001) and 1H MRS (p=0.009) but not with DWI (p=0.127). There was one case of fat necrosis which was false positive in all three techniques. ConclusionsMultiparametric imaging with combination of DCE-MRI, DWI and 1HMRS does not improve, and may even reduce the diagnostic accuracy of breast MRI. Although, the specificity may be improved with a trade-off for lower sensitivity, we have not set a convenient algorithm for the combined use of these techniques.

SU-D-BRA-02: Radiomics of Multi-Parametric Breast MRI in Breast Cancer Diagnosis: A Quantitative Investigation of Diffusion Weighted Imaging, Dynamic Contrast-Enhanced, and T2-Weighted Magnetic Resonance Imaging

Purpose: For this study, we investigated the computer-extracted tumor phenotypes from diffusion weighted imaging, dynamic contrast-enhanced, and T2-weighted magnetic resonance imaging modalities on a dataset of malignant and benign breast lesions. Methods: The IRB-approved, retrospectively-collected dataset included 118 breast lesions with 105 malignant and 13 benign. All images were acquired during clinical breast MRI at both 1.5T and 3.0T magnet strength. Phenotypic categories extracted with each modality included tumor size, shape, margin sharpness, enhancement texture, kinetics, and variance kinetics for DCE, size, shape, margin sharpness, texture for T2w, and ADC features for DWI. Results: In the task of distinguishing between benign and malignant lesions, each modality’s performance was analyzed by Round Robin evaluation using Receiver Operating Characteristic (ROC) analysis. DCE alone outperformed DWI and T2w with an AUC value of 0.89 +/−0.06. DWI and T2w yielded AUC values of 0.86 +/−0.05 and 0.84 +/−0.06 respectively. The combination of all three modalities yielded an AUC value of 0.88 +/−0.04 under single-loop Round Robin evaluation. The contrast phenotype from T2w and the standard deviation phenotype from DWI were found to be statistically different between the malignant and benign multimodality lesion groups. Conclusion: The results obtained from merging radiomic features from multimodality breast MRI (DCE, T2w, and DWI) indicate that the additional benefit of multimodality breast MRI in cancer diagnosis could be significant. This method also has potential to determine the most discriminatory radiomic phenotype from each modality. APPM DREAM Fellowship and the University of Chicago Dean Bridge Fund. M. L. Giger is a stockholder in R2 technology/Hologic and receives royalties from Hologic, GE Medical Systems, MEDIAN Technologies, Riverain Medical, Mitsubishi, and Toshiba. MLG is a co-founder and stockholder in Quantitative Insights.