For centuries tissue palpation has been an important diagnostic tool. During palpation, tumors are felt as tissues harder than the surrounding tissues. The significance of palpation is related to the relationship between mechanical properties of different tissue lesions. The assessment of tissue stiffness through palpation is based on the fact that mechanical properties of tissues are changing as a result of various diseases. A higher tissue stiffness translates into a higher elasticity modulus. In the 90's, ultrasonography was extended by the option of examining the stiffness of tissue by estimating the difference in backscattering of ultrasound in compressed and non-compressed tissue. This modality is referred to as the static, compression elastography and is based on tracking the deformation of tissue subjected to the slowly varying compression through the recording of the backscattered echoes. The displacement is estimated using the methods of cross-correlation between consecutive ultrasonic lines of examined tissue, so calculating the degree of similarity of ultrasonic echoes acquired from tissue before and after the compression was applied. The next step in the development of ultrasound palpation was to apply the local remote tissue compression by using the acoustic radiation force generated through the special beam forming of the ultrasonic beam probing the tissue. The acoustic radiation force causes a slight deformation the tissue thereby forming a shear wave propagating in the tissue at different speeds dependent on the stiffness of the tissue. Shear wave elastography, carries great hopes in the field of quantitative imaging of tissue lesions. This article describes the physical basis of both elastographic methods: compression elastography and shear wave elastography.

Early prediction of response to neoadjuvant chemotherapy (NAC) in breast cancer is crucial for guiding therapy decisions. In this work, we propose a deep learning based approach for the early NAC response prediction in ultrasound (US) imaging. We used transfer learning with deep convolutional neural networks (CNNs) to develop the response prediction models. The usefulness of two transfer learning techniques was examined. First, a CNN pre-trained on the ImageNet dataset was utilized. Second, we applied double transfer learning, the CNN pre-trained on the ImageNet dataset was additionally fine-tuned with breast mass US images to differentiate malignant and benign lesions. Two prediction tasks were investigated. First, a L1 regularized logistic regression prediction model was developed based on generic neural features extracted from US images collected before the chemotherapy (a priori prediction). Second, Siamese CNNs were used to quantify differences between US images collected before the treatment and after the first and second course of NAC. The proposed methods were evaluated using US data collected from 39 tumors. The better performing deep learning models achieved areas under the receiver operating characteristic curve of 0.797 and 0.847 in the case of the a priori prediction and the Siamese model, respectively. The proposed approach was compared with a method based on handcrafted morphological features. Our study presents the feasibility of using transfer learning with CNNs for the NAC response prediction in US imaging.

The aim of the study was to review two techniques that can be used to verify focal lesions in the breasts: fine-needle aspiration biopsy and core-needle biopsy. Fifty-five articles (original papers and reviews), half of them published within the past 5 years, were included in the analysis. The authors also took their own experience into account. Pre-operative assessment of focal lesions in the breasts is crucial in the planning of further therapeutic management. The role of fine-needle aspiration biopsy has been reduced lately due to its low sensitivity and specificity as well as a high rate of non-diagnostic, suspicious and false negative results. This method does not enable one to differentiate between in situ and invasive disease. Currently, fine-needle biopsy is recommended for cystic lesions, suspected of being recurrences in the chest wall, and lymph node metastases. Core-needle biopsy is the basic diagnostic method of breast lesions. According to the recommendations of the Polish Ultrasound Society and American College of Radiology, BIRADS 4 and 5 lesions should be evaluated histopathologically. Core-needle biopsy makes it possible to establish a final diagnosis more frequently than fine-needle biopsy, both in the case of benign and malignant lesions. It delivers more information about the nature of a tumor (mutation of HER-2, estrogen and progesterone receptors and Ki-67 index). Its limitations include: underestimation of invasion and failure to recognize the components of ductal carcinoma in situ in papillary and atypical lesions. Single fine-needle aspiration biopsy is inexpensive, but when considering the cost of further diagnosis due to non-diagnostic, suspicious and atypical results, this method generates high additional costs. Microscopic verification of focal breast lesions is crucial for further therapeutic decisions. It has been proven that histopathological verification is more accurate and has more advantages than cytological assessment.

The presented studies evaluate for the first time the efficiency of tumour classification based on the quantitative analysis of ultrasound data originating from the tissue surrounding the tumour. 116 patients took part in the study after qualifying for biopsy due to suspicious breast changes. The RF signals collected from the tumour and tumour-surroundings were processed to determine quantitative measures consisting of Nakagami distribution shape parameter, entropy, and texture parameters. The utility of parameters for the classification of benign and malignant lesions was assessed in relation to the results of histopathology. The best multi-parametric classifier reached an AUC of 0.92 and of 0.83 for outer and intra-tumour data, respectively. A classifier composed of two types of parameters, parameters based on signals scattered in the tumour and in the surrounding tissue, allowed the classification of breast changes with sensitivity of 93%, specificity of 88%, and AUC of 0.94. Among the 4095 multi-parameter classifiers tested, only in eight cases the result of classification based on data from the surrounding tumour tissue was worse than when using tumour data. The presented results indicate the high usefulness of QUS analysis of echoes from the tissue surrounding the tumour in the classification of breast lesions.