Machine learning in breast MRI

Abstract

Machine-learning techniques have led to remarkable advances in data extraction and analysis of medical imaging. Applications of machine learning to breast MRI continue to expand rapidly as increasingly accurate 3D breast and lesion segmentation allows the combination of radiologist-level interpretation (eg, BI-RADS lexicon), data from advanced multiparametric imaging techniques, and patient-level data such as genetic risk markers. Advances in breast MRI feature extraction have led to rapid dataset analysis, which offers promise in large pooled multiinstitutional data analysis. The object of this review is to provide an overview of machine-learning and deep-learning techniques for breast MRI, including supervised and unsupervised methods, anatomic breast segmentation, and lesion segmentation. Finally, it explores the role of machine learning, current limitations, and future applications to texture analysis, radiomics, and radiogenomics. Level of Evidence: 3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019. J. Magn. Reson. Imaging 2020;52:998-1018.

Keywords: MR; artificial intelligence; breast; deep learning; machine learning; radiomics.

FIGURE 1:

Example of radiomics study workflow. In all, 163 breast cancer patients with DCE-MRI scans were included in this study. The ROIs were identified on (a) the first postcontrast image; (b) the corresponding intratumoral and peritumoral ROIs: the yellow region is the original intratumoral ROI covering the enhancing tumor drawn by the radiologist, while the red region indicates the peritumoral ROI after dilation; radiomic features were extracted from (c) washin map, (d) washout map, and (e) SER map. The dataset was then randomly separated into a training set (~67%) and a validation set (~33%). The prediction model was built in the training set after combining clinical and histopathological information and was further tested in the completely independent validation set. (Reprinted and adapted with permission from Liu et al. J Magn Reson Imaging 2019;49:131–140.)

FIGURE 2:

Automatic breast segmentation pipeline incorporating machine learning. Axial T₁-weighted precontrast images are automatically segmented at breast-air (blue) and breast-chest wall (red) boundary (a). Breast is further subdivided into fibroglandular breast tissue (FGT, green) and fat. Background parenchymal enhancement (BPE) is calculated as FGT enhancement over baseline and is 12% (minimal) in this 66-year-old screening patient, as demonstrated on the first postcontrast subtraction images (b).

FIGURE 3:

Images from a patient who subsequently developed cancer (a) and the two matched controls (b,c, respectively) The MIPs are presented in the first column and the breast masks are shown in the second column. The third column represents enhanced FGT on the MIP in green, vessels in red, and remaining breast mask in blue. The FGT for these images was extracted from the corresponding T₁ nonfat-saturated sequence. (Reprinted and adapted with permission from Saha et al. J Magn Reson Imaging 2019.)

FIGURE 4:

SVM analysis of known cancers offers promise in evaluating extent of disease. Maximum washin-slope and peak enhancement were associated with malignancy in SVM analysis of predictors of malignancy in ipsilateral and contralateral breast lesions. A 63-year-old woman had a new diagnosis of 8 mm right retroareolar papillary carcinoma with planned breast conservation. Breast MRI demonstrated two irregular masses and two additional foci (blue arrows) of abnormal enhancement (a, first postcontrast images). Manual volumes of interest annotating these foci on high temporal resolution MRI (b, high temporal resolution T₁-weighted subtraction images at 45 seconds postcontrast) demonstrated early peak enhancement (c). Subsequent MR-directed ultrasound and MRI biopsies yielded additional papillary carcinoma and papillary lesions, leading to surgical decision for mastectomy instead of breast conservation.

FIGURE 5:

Visualization of (a) the MRI slices from three different samples, (b) the corresponding heatmap obtained from the GMP model, (c) the corresponding refined weak label using DenseCRF, and (d) the manual annotation. Fired color indicates higher values for the activations in (b). Red color indicates the annotation by model and human in (c,d). The Dice coefficients of each sample were: 0.823, 0.683, and 0.091, respectively. (Reprinted and adapted with permission from Zhou et al. J Magn Reson Imaging 2019.)

FIGURE 6:

Schematic depiction of image processing. Left: 3D segmentations of lesions shown on single T₂w slices (left) and as surface shaded 3D renderings (right). I: Segmentations (red) are shown overlaid on the four imaging sequences (T₂w, DWIBS1500, ADCDWIBS, and ADCDWI). II: Intensity normalization transforms variable MR signal intensities on T₂w and DWI images (top) into comparable image intensities (bottom). III: Radiomic feature extraction uses first-order statistics, volumetric and texture features as defined in Data Supplement S1 to generate a multidimensional imaging signature. IV: The radiomic feature matrix and corresponding outcome data (histopathological results) are combined and used for supervised training of the Lasso regularized logistic regression model. Performance of the constructed model is compared to the performance of known standard parameters using ROC analysis. T₂w = T₂-weighted; DWI = diffusion-weighted imaging; DWIBS = diffusion-weighted imaging with background suppression (DWIBS); ADC = apparent diffusion coefficient: ROC = receiver operating characteristics. (Reprinted and adapted with permission from Bickelhaupt et al. J Magn Reson Imaging 2017;46:604–616.)

FIGURE 7:

Initial enhancement (a,d), overall enhancement (b,e), and area under the enhancement curve (c,f) maps for a benign papilloma (a–c) and a malignant invasive ductal carcinoma (d–f). It is evident that spatially heterogeneous enhancement is present, which can thus be quantified using texture analysis. (Reprinted and adapted with permission from Gibbs et al. J Magn Reson Imaging 2019.)

FIGURE 8:

A 52-year-old woman with triple positive (ER, PR, and HER2+) high-grade invasive ductal carcinoma evaluated with breast MRI before and after four cycles neoadjuvant chemotherapy (a: first postcontrast subtraction, b: T₂-weighted). 3D whole lesion volume of interest (VOI) was annotated using a seed-growing semiautomated segmentation technique on subtraction images (c, red lesion) with peritumoral region (c, blue lesion) automatically generated based on VOI. Tumor and peritumoral VOIs were then propagated to coregistered T₂ images (c) and first-order texture features were analyzed. Lesions that demonstrated high T₂ whole lesion entropy, T₁ core lesion entropy, and T₂ peritumoral skewness and kurtosis were more likely to exhibit pathologic complete response (pCR; accuracy = 74%). The patient demonstrated complete imaging response on post-neoadjuvant therapy imaging (e,f) and had pCR at final surgical pathology. (Reprinted and adapted with permission from Heacock et al. RSNA 2017.)

FIGURE 9:

A 52-year-old woman with CHEK2 mutation undergoing high-risk screening MRI. A new 4 mm focus of enhancement (blue arrow) at left 7:00 (a,b, first postcontrast axial subtraction and sagittal images) was manually segmented in a 3D volume of interest. The segmented lesion demonstrated early washin on high temporal resolution sequences acquired in the first 60 seconds (c) but persistent temporal kinetics on washout curve analysis (d). MRI-guided biopsy yielded high-grade invasive ductal carcinoma. Early maximum slope on high temporal resolution images is associated with malignancy in SVM analysis.