Ultrasound elastography: How can it help in differentiating breast lesions?

Deep learning-based multimodal fusion network for segmentation and classification of breast cancers using B-mode and elastography ultrasound images

Ultrasonography is one of the key medical imaging modalities for evaluating breast lesions. For differentiating benign from malignant lesions, computer-aided diagnosis (CAD) systems have greatly assisted radiologists by automatically segmenting and identifying features of lesions. Here, we present deep learning (DL)-based methods to segment the lesions and then classify benign from malignant, utilizing both B-mode and strain elastography (SE-mode) images. We propose a weighted multimodal U-Net (W-MM-U-Net) model for segmenting lesions where optimum weight is assigned on different imaging modalities using a weighted-skip connection method to emphasize its importance. We design a multimodal fusion framework (MFF) on cropped B-mode and SE-mode ultrasound (US) lesion images to classify benign and malignant lesions. The MFF consists of an integrated feature network (IFN) and a decision network (DN). Unlike other recent fusion methods, the proposed MFF method can simultaneously learn complementary information from convolutional neural networks (CNNs) trained using B-mode and SE-mode US images. The features from the CNNs are ensembled using the multimodal EmbraceNet model and DN classifies the images using those features. The experimental results (sensitivity of 100 ± 0.00% and specificity of 94.28 ± 7.00%) on the real-world clinical data showed that the proposed method outperforms the existing single- and multimodal methods. The proposed method predicts seven benign patients as benign three times out of five trials and six malignant patients as malignant five out of five trials. The proposed method would potentially enhance the classification accuracy of radiologists for breast cancer detection in US images.

Bi-Modal Transfer Learning for Classifying Breast Cancers via Combined B-Mode and Ultrasound Strain Imaging

Although accurate detection of breast cancer still poses significant challenges, deep learning (DL) can support more accurate image interpretation. In this study, we develop a highly robust DL model based on combined B-mode ultrasound (B-mode) and strain elastography ultrasound (SE) images for classifying benign and malignant breast tumors. This study retrospectively included 85 patients, including 42 with benign lesions and 43 with malignancies, all confirmed by biopsy. Two deep neural network models, AlexNet and ResNet, were separately trained on combined 205 B-mode and 205 SE images (80% for training and 20% for validation) from 67 patients with benign and malignant lesions. These two models were then configured to work as an ensemble using both image-wise and layer-wise and tested on a dataset of 56 images from the remaining 18 patients. The ensemble model captures the diverse features present in the B-mode and SE images and also combines semantic features from AlexNet and ResNet models to classify the benign from the malignant tumors. The experimental results demonstrate that the accuracy of the proposed ensemble model is 90%, which is better than the individual models and the model trained using B-mode or SE images alone. Moreover, some patients that were misclassified by the traditional methods were correctly classified by the proposed ensemble method. The proposed ensemble DL model will enable radiologists to achieve superior detection efficiency owing to enhance classification accuracy for breast cancers in ultrasound (US) images.

Evaluation of Elastographic Features in Complex Fibroadenomas With Radiologic-Pathologic Correlation

Fibroadenoma is the most common benign tumor of the breast, and complex fibroadenoma (CFA) is one of its variants. Of the fibroadenomas, 22% are CFAs, and in women with CFAs, the malignancy development is found to be higher than in women with noncomplex fibroadenomas. Although there is an increased risk of malignancy with CFAs, the imaging findings of CFAs are fundamentally similar to those of other variants of fibroadenomas. In the literature, B-mode ultrasound features of CFAs were reported in detail. To our knowledge, there is no study that has specifically described the elastographic findings of CFAs. This article aims to illustrate the elastographic features of CFAs and to correlate radiologic and histopathologic findings of different cases.

New Approaches in the Study of the Pathogenesis of Urethral Pain Syndrome

INTRODUCTION

Urethral pain syndrome (UPS) is still a pathology in which the diagnosis is formulated as a "diagnosis of exclusion". The exact pathogenetic mechanisms are not yet fully understood and clear recommendations for the prevention and treatment of UPS are absent.

METHODS AND PARTICIPANTS

A clinical and laboratory evaluation of 55 patients with established UPS included history taking, basic laboratory tests (e.g., complete blood count and clinical urine test), physical examination, uroflowmetry, and cystourethroscopy. Additionally, transvaginal ultrasound (TVUS) with compression elastography and cross-polarization optical tomography (CP OCT) were performed in 24 and 33 patients with UPS, respectively. The control group consisted of 14 patients with no complaints from the urinary system.

RESULTS

TVUS showed an expansion in the diameter of the internal lumen of the urethra, especially in the proximal region compared with the norm. Compression elastography revealed areas with increased stiffness (presence of fibrosis) in urethral and surrounding tissues. The performed CP OCT study showed that in UPS, the structure of the tissues in most cases was changed: trophic alterations in the epithelium (hypertrophy or atrophy) and fibrosis of underlying connective tissue were observed. The proximal fragment of the urethra with UPS underwent changes identical to those of the bladder neck.

CONCLUSION

This paper showed that the introduction of new technology-CP OCT-in conjunction with TVUS will allow verification of structural changes in tissues of the lower urinary tract at the level of their architectonics and will help doctors understand better the basics of the UPS pathogenesis.