Institute of Fundamental Technological Research

Objective: Neoadjuvant chemotherapy (NAC) is widely used in the treatment of breast cancer. However, to date, there are no fully reliable, non-invasive methods for monitoring NAC. In this article, we propose a new method for classifying NAC-responsive and unresponsive tumors using quantitative ultrasound. Methods: The study used ultrasound data collected from breast tumors treated with NAC. The proposed method is based on the hypothesis that areas that characterize the effect of therapy particularly well can be found. For this purpose, parametric images of texture features calculated from tumor images were converted into NAC response probability maps, and areas with a probability above 0.5 were used for classification. Results: The results obtained after the third cycle of NAC show that the classification of tumors using the traditional method (AUC = 0.81 - 0.88) can be significantly improved thanks to the proposed new approach (AUC = 0.84–0.94). This improvement is achieved over a wide range of cutoff values (0.2-0.7), and the probability maps obtained from different quantitative parameters correlate well. Conclusion: The results suggest that there are tumor areas that are particularly well suited to assessing response to NAC. Significance: The proposed approach to monitoring the effects of NAC not only leads to a better classification of responses, but also may contribute to a better understanding of the microstructure of neoplastic tumors observed in an ultrasound examination.

breast cancer,neoadjuvant chemotherapy,quantitative ultrasound,treatment monitoring.

Ultrasonography is a basic tool used in the evaluation of thyroid nodules, but there is no single feature of this method which predicts malignancy with statistical significance. The aim of the study is to assess the usefulness of contrast enhanced-ultrasound (CEUS) in the differential diagnosis of thyroid nodules. The highest value of the study results from the multiparameter approach to the evaluation of thyroid lesions in the light of new diagnostics methods and assessment of the unique combinations of both B-mode and CEUS features as predictors of thyroid cancers. Moreover, several qualitative contrast features predicting benign lesions were evaluated. The preliminary results indicate that CEUS is a useful tool in assessing the risk of malignancy of thyroid lesions. The combination of the qualitative enhancement parameters and B-mode sonographic features significantly increases the method’s usefulness. Further studies should be performed to introduce CEUS patterns in the diagnostic algorithm of thyroid nodules.

thyroid cancer, cancer screening, clinical trial, contrast-enhanced ultrasound, thyroid lesion

Introduction:
The latest World Health Organization (WHO) classification from 2022 distinguishes the division of low-risk thyroid neoplasms such as non-invasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP), follicular tumour of uncertain malignant potential (FT-UMP), and well-differentiated tumour of uncertain malignant potential (WDT-UMP). The final diagnosis is made postoperatively according to histopathologic results. The aim of the study was the assessment of ultrasonographic and cytopathological features of borderline lesions to predict low-risk tumours preoperatively and plan the optimal treatment for that group of patients.

Material and methods:
A total of 35 patients (30 women; 5 men), aged 20–81 years with a mean age of 49 years, were enrolled in the study. The study evaluated 35 focal lesions of the thyroid gland, classified as low-risk neoplasms according to the WHO 2022 classification: FT-UMP (n = 21), NIFTP (n = 7), and WDT-UMP (n = 7). Ultrasonographic features of nodules including contrast-enhanced ultrasound (CEUS) and elastography were assessed by 2 specialists, and the risk of malignancy was evaluated according to EU-TIRADS-PL classification.

Results:
Of the 35 focal thyroid lesions, most were categorised as low or intermediate risk of malignancy according to EU-TIRADS-PL, with dominant category 3 [n = 13 (37.2%)] and category 4 [n = 15 (42.8%)]. High-risk category 5 was assessed in 7 lesions (20%). In cytopathology nodules were categorised as follows (Bethesda System TBSRTC 2023): Bethesda II (n = 4), Bethesda III (n = 2), Bethesda IV (n = 25), Bethesda V (n = 3), and Bethesda VI (n = 1). In the CEUS study, contrasting patterns dominated compared to the surrounding parenchyma, such as enhancement equal to the parenchyma (66.6%) or intense (28.5%), heterogeneous (61.9%), centripetal (42.8%), or diffuse (57.1%) with fast (33.3%) or compared to parenchyma contrast wash-in (42.8%) and its fast (33.3%) or comparable to thyroid parenchyma wash-out (52.3%).

Conclusions:
The study indicates that lesions with uncertain malignant potential typically present features suggesting low to intermediate risk of malignancy based on EU-TIRADS-PL classification, with dominant cytopathologic Bethesda IV category. However, 20% of lesions were assessed tas EU-TIRADS-PL category 5. Low-risk tumours, including NIFTP, FT-UMP, and WDT-UMP, require careful observation and monitoring post surgical treatment due to their potential for recurrence and metastasis. The preoperatively prediction of borderline tumour may play an important role in proper treatment and follow-up.

thyroid tumour, ultrasound, thyroid cancer, contrast-enhanced-ultrasound

Objective: Neoadjuvant chemotherapy (NAC) in breast cancer requires non-invasive methods of monitoring its effects after each dose of drug therapy. The aim is to isolate responding and non-responding tumors prior to surgery in order to increase patient safety and select the optimal medical follow-up. Methods: A new method of monitoring NAC therapy has been proposed. The method is based on image quality indexes (IQI) calculated from ultrasound data obtained from breast tumors and surrounding tissue. Four different tissue regions from the preliminary set of 38 tumors and three data pre-processing techniques are considered. Postoperative histopathology results were used as a benchmark in evaluating the effectiveness of the IQI classification. Results: Out of ten parameters considered, the best results were obtained for the Gray Relational Coefficient. Responding and non-responding tumors were predicted after the first dose of NAC with an area under the receiver operating characteristics curve of 0.88 and 0.75, respectively. When considering subsequent doses of NAC, other IQI parameters also proved usefulness in evaluating NAC therapy. Conclusions: The image quality parameters derived from the ultrasound data are well suited for assessing the effects of NAC therapy, in particular on breast tumors.

Quantitative ultrasound; Image quality; Neoadjuvant chemotherapy; Breast cancer; Treatment response

Thyroid, Ultrasound, CEUS

In this paper, we propose a novel deep learning method for joint classification and segmentation of breast masses based on radio-frequency (RF) ultrasound (US) data. In comparison to commonly used classification and segmentation techniques, utilizing B-mode US images, we train the network with RF data (data before envelope detection and dynamic compression), which are considered to include more information on tissue’s physical properties than standard B-mode US images. Our multi-task network, based on the Y-Net architecture, can effectively process large matrices of RF data by mixing 1D and 2D convolutional filters. We use data collected from 273 breast masses to compare the performance of networks trained with RF data and US images. The multi-task model developed based on the RF data achieved good classification performance, with area under the receiver operating characteristic curve (AUC) of 0.90. The network based on the US images achieved AUC of 0.87. In the case of the segmentation, we obtained mean Dice scores of 0.64 and 0.60 for the approaches utilizing US images and RF data, respectively. Moreover, the interpretability of the networks was studied using class activation mapping technique and by filter weights visualizations.

breast mass classification, breast mass segmentation, convolutional neural networks, deep learning, quantitative ultrasound, ultrasound imagin

Purpose: Neo-adjuvant chemotherapy (NAC) is used in breast cancer before tumor surgery to reduce the size of the tumor and the risk of spreading. Monitoring the effects of NAC is important because in a number of cases the response to therapy is poor and requires a change in treatment. A new method that uses quantitative ultrasound to assess tumor response to NAC has been presented. The aim was to detect NAC unresponsive tumors at an early stage of treatment. Methods: The method assumes that ultrasound scattering is different for responsive and nonresponsive tumors. The assessment of the NAC effects was based on the differences between the histograms of the ultrasound echo amplitude recorded from the tumor after each NAC dose and from the tissue phantom, estimated using the Kolmogorov–Smirnov statistics (KSS) and the symmetrical Kullback–Leibler divergence (KLD). After therapy, tumors were resected and histopathologically evaluated. The percentage of residual malignant cells was determined and was the basis for assessing the tumor response. The data set included ultrasound data obtained from 37 tumors. The performance of the methods was assessed by means of the area under the receiver operating characteristic curve (AUC). Results: For responding tumors, a decrease in the mean KLD and KSS values was observed after subsequent doses of NAC. In nonresponding tumors, the KLD was higher and did not change in subsequent NAC courses. Classification based on the KSS or KLD parameters allowed to detect tumors not respond- ing to NAC after the first dose of the drug, with AUC equal 0.83±0.06 and 0.84±0.07, respectively. After the third dose, the AUC increased to 0.90±0.05 and 0.91±0.04, respectively. Conclusions: The results indicate the potential usefulness of the proposed parameters in assessing the effectiveness of the NAC and early detection of nonresponding cases.

breast cancer, neoadjuvant therapy assessment, quantitative ultrasound

Objective. Prediction of the response to neoadjuvant chemotherapy (NAC) in breast cancer is important for patient outcomes. In this work, we propose a deep learning based approach to NAC response prediction in ultrasound (US) imaging. Approach. We develop recurrent neural networks that can process serial US imaging data to predict chemotherapy outcomes. We present models that can process either raw radio-frequency (RF) US data or regular US images. The proposed approach is evaluated based on 204 sequences of US data from 51 breast cancers. Each sequence included US data collected before the chemotherapy and after each subsequent dose, up to the 4th course. We investigate three pre-trained convolutional neural networks (CNNs) as back-bone feature extractors for the recurrent network. The CNNs were pre-trained using raw US RF data, US b-mode images and RGB images from the ImageNet dataset. The first two networks were developed using US data collected from malignant and benign breast masses. Main results. For the pre-treatment data, the better performing network, with back-bone CNN pre-trained on US images, achieved area under the receiver operating curve (AUC) of 0.81 (±0.04). Performance of the recurrent networks improved with each course of the chemotherapy. For the 4th course, the better performing model, based on the CNN pre-trained with RGB images, achieved AUC value of 0.93 (±0.03). Statistical analysis based on the DeLong test presented that there were no significant differences in AUC values between the pre-trained networks at each stage of the chemotherapy (p-values > 0.05). Significance. Our study demonstrates the feasibility of using recurrent neural networks for the NAC response prediction in breast cancer US.

breast cancer, deep learning, neoadjuvant chemotherapy, reccurent neural networks, ultrasound imaging

Aim of the study: Deep neural networks have achieved good performance in breast mass classification in ultrasound imaging. However, their usage in clinical practice is still lim¬ited due to the lack of explainability of decisions conducted by the networks. In this study, to address the explainability problem, we generated saliency maps indicating ultrasound image regions important for the network’s classification decisions. Material and methods: Ultrasound images were collected from 272 breast masses, including 123 malignant and 149 benign. Transfer learning was applied to develop a deep network for breast mass clas¬sification. Next, the class activation mapping technique was used to generate saliency maps for each image. Breast mass images were divided into three regions: the breast mass region, the peritumoral region surrounding the breast mass, and the region below the breast mass. The pointing game metric was used to quantitatively assess the overlap between the saliency maps and the three selected US image regions. Results: Deep learning classifier achieved the area under the receiver operating characteristic curve, accuracy, sensitivity, and specific¬ity of 0.887, 0.835, 0.801, and 0.868, respectively. In the case of the correctly classified test US images, analysis of the saliency maps revealed that the decisions of the network could be associated with the three selected regions in 71% of cases. Conclusions: Our study is an important step toward better understanding of deep learning models developed for breast mass diagnosis. We demonstrated that the decisions made by the network can be related to the appearance of certain tissue regions in breast mass US images.

deep learning, breast mass diagnosis, attention maps, explainability

Neoadjuvant chemotherapy is increasingly becoming the first treatment step in breast cancer. Despite the enormous advantages of this therapy, it is a method characterized by a high level of toxicity and thus carries a huge burden for the patient. Therefore, it is highly desirable to begin monitoring the patient’s response to treatment at an earlier stage. Currently, apart from traditional imaging methods, a completely new technique (in the context of monitoring the outcomes of chemotherapy), called quantitative ultrasound, is gaining popularity. It is a method based on the exact same ultrasound echoes as in traditional ultrasound imaging. The innovative approach of the method is that these echoes are not used for visualization but to characterize the condition of the tissue by parameterizing it with the aid of ultrasound biomarkers. The biomarkers make it possible to assess the state of the tissue at the microscopic level, and thus evaluate changes occurring in the tissue under the influence of treatment at a very early treatment stage. The present paper aims to familiarize the reader with the physical foundations of this method as well as present the latest results of related research.

quantitative ultrasound; breast cancer; neoadjuvant chemotherapy

A 22 year-old woman with no family history of breast cancer developed a painful breast tumor in the inner lower quadrant of the left breast. After surgical consultation, ultrasound examination was advised. On ultrasound,the mass demonstrated malignant features such as non-parallel orientation, hypoechogenicity, spiculated margins, posterior shadowing, peripheral vessels on color Doppler and increased stiffness in the tissue surrounding the tumor. Before biopsy, mammography was performed. The mass had higher density than fibroglandular tissue, margins were ill-defined (spiculated) and had no microcalcification. BIRADS 5 category was pre-assigned. Core biopsy revealed granular cell tumor (GCT). The patient is undergoing regular follow-up. Granular cell tumor is rare and generally benign, however, it typically demonstrates malignant features clinically and on obtained diagnostic images. This article highlights the variety of imaging features accompanying benign breast tumors.

Breast Cancer,Breast Ultrasound,Granular Cell Tumor

To investigate the performance of multiparametric ultrasound for the evaluation of treatment response in breast cancer patients undergoing neoadjuvant chemotherapy (NAC). The IRB approved this prospective study. Breast cancer patients who were scheduled to undergo NAC were invited to participate in this study. Changes in tumour echogenicity, stiffness, maximum diameter, vascularity and integrated backscatter coefficient (IBC) were assessed prior to treatment and 7 days after four consecutive NAC cycles. Residual malignant cell (RMC) measurement at surgery was considered as standard of reference. RMC < 30% was considered a good response and > 70% a poor response. The correlation coefficients of these parameters were compared with RMC from post-operative histology. Linear Discriminant Analysis (LDA), cross-validation and Receiver Operating Characteristic curve (ROC) analysis were performed. Thirty patients (mean age 56.4 year) with 42 lesions were included. There was a significant correlation between RMC and echogenicity and tumour diameter after the 3rd course of NAC and average stiffness after the 2nd course. The correlation coefficient for IBC and echogenicity calculated after the first four doses of NAC were 0.27, 0.35, 0.41 and 0.30, respectively. Multivariate analysis of the echogenicity and stiffness after the third NAC revealed a sensitivity of 82%, specificity of 90%, PPV = 75%, NPV = 93%, accuracy = 88% and AUC of 0.88 for non-responding tumours (RMC > 70%). High tumour stiffness and persistent hypoechogenicity after the third NAC course allowed to accurately predict a group of non-responding tumours. A correlation between echogenicity and IBC was demonstrated as well.

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.

breast cancer, deep learning, neoadjuvant chemotherapy, Siamese convolutional neural networks, ultrasound imaging

The aim of the study was to improve monitoring the treatment response in breast cancer patients undergoing neoadjuvant chemotherapy (NAC). The IRB approved this prospective study. Ultrasound examinations were performed prior to treatment and 7 days after four consecutive NAC cycles. Residual malignant cell (RMC) measurement at surgery was the standard of reference. Alteration in B-mode ultrasound (tumor echogenicity and volume) and the Kullback-Leibler divergence (kld), as a quantitative measure of amplitude difference, were used. Correlations of these parameters with RMC were assessed and Receiver Operating Characteristic curve (ROC) analysis was performed. Thirty-nine patients (mean age 57 y.) with 50 tumors were included. There was a significant correlation between RMC and changes in quantitative parameters (KLD) after the second, third and fourth course of NAC, and alteration in echogenicity after the third and fourth course. Multivariate analysis of the echogenicity and KLD after the third NAC course revealed a sensitivity of 91%, specificity of 92%, PPV = 77%, NPV = 97%, accuracy = 91%, and AUC of 0.92 for non-responding tumors (RMC ≥ 70%). In conclusion, monitoring the echogenicity and KLD parameters made it possible to accurately predict the treatment response from the second course of NAC.

quantitative ultrasound, B-mode ultrasound, echogenicity, breast cancer, neoadjuvant chemotherapy

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.

Background: Neoadjuvant chemotherapy (NAC) is used in patients with breast cancer to reduce tumor focus, metastatic risk, and patient mortality. Monitoring NAC effects is necessary to capture resistant patients and stop or change treatment. The existing methods for evaluating NAC results have some limitations. The aim of this study was to assess the tumor response at an early stage, after the first doses of the NAC, based on the variability of the backscattered ultrasound energy, and backscatter statistics. The backscatter statistics has not previously been used to monitor NAC effects. Methods: The B-mode ultrasound images and raw radio frequency data from breast tumors were obtained using an ultrasound scanner before chemotherapy and 1 week after each NAC cycle. The study included twenty-four malignant breast cancers diagnosed in sixteen patients and qualified for neoadjuvant treatment before surgery. The shape parameter of the homodyned K distribution and integrated backscatter, along with the tumor size in the longest dimension, were determined based on ultrasound data and used as markers for NAC response. Cancer tumors were assigned to responding and non-responding groups, according to histopathological evaluation, which was a reference in assessing the utility of markers. Statistical analysis was performed to rate the ability of markers to predict the final NAC response based on data obtained after subsequent therapeutic doses. Results: Statistically significant differences (p<0.05) between groups were obtained after 2, 3, 4, and 5 doses of NAC for quantitative ultrasound markers and after 5 doses for the assessment based on maximum tumor dimension. Statistical analysis showed that, after the second and third NAC courses the classification based on integrated backscatter marker was characterized by an AUC of 0.69 and 0.82, respectively. The introduction of the second quantitative marker describing the statistical properties of scattering increased the corresponding AUC values to 0.82 and 0.91. Conclusions: Quantitative ultrasound information can characterize the tumor's pathological response better and at an earlier stage of therapy than the assessment of the reduction of its dimensions. The introduction of statistical parameters of ultrasonic backscatter to monitor the effects of chemotherapy can increase the effectiveness of monitoring and contribute to a better personalization of NAC therapy.

Purpose: To evaluate the ultrasound (US) response in patients with breast cancer (BC) during neoadjuvant chemotherapy (NAC). Methods: Prospective US analysis was performed on 19 malignant tumors prior to NAC treatment and 7 days after each first four courses of NAC in 13 patients (median age=57years). Echogenicity, size, vascularity, and sonoelastography were measured and compared with posttreatment scores of residual cancers burden. Results: Changes in the echogenicity of tumors after 3 courses of NAC had the most statistically strong correlation with the percentage of residual malignant cells used in histopathology to assess the response to treatment (odds ratio=60, p < 0.05). Changes in lesion size and elasticity were also significant (p < 0.05). Conclusions: There is a statistically significant relationship between breast tumors' echogenicity in US, neoplasm size, and stiffness and the response to NAC. In particular, our results show that the change in tumor echogenicity could predict a pathological response with satisfactory accuracy and may be considered in NAC monitoring.

breast ultrasonography, neoadjuvant chemotherapy, clinical response, breast cancer, sonoelastography

Objective: Neoadjuvant chemotherapy was initially used in locally advanced breast cancer, and currently it is recommended for patients with Stage 3 and with early-stage disease with human epidermal growth factor receptors positive or triple-negative breast cancer. Ultrasound imaging in combination with a quantitative ultrasound method is a novel diagnostic approach. Aim of study: The aim of this study was to analyze the variability of the integrated backscatter coefficient, and to evaluate their use to predict the effectiveness of treatment and compare to ultrasound examination results. Material and method: Ten patients (mean age 52.9) with 13 breast tumors (mean dimension 41 mm) were selected for neoadjuvant chemotherapy. Ultrasound was performed before the treatment and one week after each course of neoadjuvant chemotherapy. The dimensions were assessed adopting the RECIST criteria. Tissue responses were classified as pathological response into the following categories: not responded to the treatment (G1, cell reduction by ≤9%) and responded to the treatment partially: G2, G3, G4, cell reduction by 10–29% (G2), 30–90% (G3), >90% (G4), respectively, and completely. Results: In B-mode examination partial response was observed in 9/13 cases (completely, G1, G3, G4), and stable disease was demonstrated in 3/13 cases (completely, G1, G4). Complete response was found in 1/13 cases. As for backscatter coefficient, 10/13 tumors (completely, and G2, G3, and G4) were characterized by an increased mean value of 153%. Three tumors 3/13 (G1) displayed a decreased mean value of 31%. Conclusion: The variability of backscatter coefficient, could be associated with alterations in the structure of the tumor tissue during neoadjuvant chemotherapy. There were unequivocal differences between responded and non-responded patients. The backscatter coefficient analysis correlated better with the results of histopathological verification than with the B-mode RECIST criteria.

integrated backscatter coefficient (IBSCs), neoadjuvant chemotherapy (NAC), breast cancer, ultrasound

Purpose: The aim of this paper is to provide access to a database consisting of the raw radio-frequency ultrasonic echoes acquired from malignant and benign breast lesions. The database is freely available for study and signal analysis. Acquisition and validation methods: The ultrasonic radio-frequency echoes were recorded from breast focal lesions of patients of the Institute of Oncology in Warsaw. The data were collected between 11/2013 and 10/2015. Patients were examined by a radiologist with 18 yr' experience in the ultrasonic examination of breast lesions. The set of data includes scans from 52 malignant and 48 benign breast lesions recorded in a group of 78 women. For each lesion, two individual orthogonal scans from the pathological region were acquired with the Ultrasonix SonixTouch Research ultrasound scanner using the L14-5/38 linear array transducer. All malignant lesions were histologically assessed by core needle biopsy. In the case of benign lesions, part of them was histologically assessed and another part was observed over a 2-year period. Data format and usage notes: The radio-frequency echoes were stored in Matlab file format. For each scan, the region of interest was provided to correctly indicate the lesion area. Moreover, for each lesion, the BI-RADS category and the lesion class were included. Two code examples of data manipulation are presented. The data can be downloaded via the Zenodo repository (https://doi.org/10.5281/zenodo.545928) or the website http ://bluebox.ippt.gov.pl/~hpiotrzk. Potential applications: The database can be used to test quantitative ultrasound techniques and ultrasound image processing algorithms, or to develop computer-aided diagno sis systems.

breast lesions, dataset, ultrasonic signals, ultrasonography

AIM: To develop a method combining the statistics of the ultrasound backscatter and the Breast Imaging-Reporting and Data System (BI-RADS) classification to enhance the differentiation of breast tumours. MATERIALS AND METHODS: The Nakagami shape parameter m was used to characterise the scatter properties of breast tumours. Raw data from the radiofrequency (RF) echo-signal and Bmode images from 107 (32 malignant and 75 benign) lesions and their surrounding tissue were recorded. Three different characteristic values of the shape parameters of m (maximum [mLmax], minimum [mLmin] and average [mLavg]) and differences between m parameters (Dmmax, Dmmin, Dmavg) of the lesions and their surrounding tissues were assessed. A lesion with a BI-RADS score of 3 was considered benign, while a lesion with a score of 4 was considered malignant (a cut-off of BI-RADS 3/4 was set for all patients). RESULTS: The area under the receiver operating characteristic (ROC) curve (AUC) was equal to 0.966 for BI-RADS, with 100% sensitivity and 54.67% specificity. All malignant lesions were diagnosed correctly, whereas 34 benign lesions were biopsied unnecessarily. In assessing the Nakagami statistics, the sum of the sensitivity and specificity was the best for mLavg (62.5% and 93.33%, respectively). Only four of 20 lesions were found over the cut-off value in BI-RADS of 4a. When comparing the differences in m parameters, Dmavg had the highest sensitivity of 90% (only three of 32 lesions were false negative). These three lesions were classified as BIRADS category 4c. The combined use of B-mode and mLmin parameter improve the AUC up to 0.978 (pĽ0.088), compared to BI-RADS alone.
CONCLUSION: The combination of the parametric imaging and the BI-RADS assessment does not significantly improve the differentiation of breast lesions, but it has the potential to better identify the group of patients with mainly benign lesions that have a low level of suspicion for malignancy with a BI-RADS score of 4a.

Purpose:
Statistical modeling of an ultrasound backscattered echo envelope is used for tissue characterization. However, in the presence of complex structures within the analyzed area, estimation of parameters is disturbed and unreliable, e.g., in the case of breast tumor classification. In order to improve the differentiation of breast lesions, the authors proposed a method based on the segmentation of homodyned K distribution parameter maps. Regions within lesions of different scattering properties were extracted and analyzed. In order to improve the classification, the best-performing features were selected from various regions and then combined.

Methods:
A radio-frequency data set consisting of 103 breast lesions was used in the authors’ analysis. Maps of homodyned K distribution parameters were created using an algorithm based on signal-to-noise ratio, kurtosis, and skewness of fractional-order envelope moments. A Markov random field model was used to segment parametric maps. Features of different segments were extracted and evaluated based on bootstrapping and the receiver operating characteristic curve. To determine the best-performing feature subset, the authors applied the joint mutual information criterion.

Results:
It was found that there were individual features which performed better than the ones commonly used for lesion characterization, like the parameter obtained through averaging of values over the whole lesion. The authors selected and discussed the best-performing features. Properties of different extracted regions were important and improved the distinction between benign and malignant tumors. The best performance was obtained by combining four features with the area under the receiver operating curve of 0.84.

Conclusions:
The study showed that the analysis of internal changes in lesion parametric maps leads to a better classification of breast tumors. The authors recommend combining multiple features for characterization, instead of using only one parameter, especially in the case of heterogeneous lesions.

Cancer, Ultrasonography, Backscattering, Data sets, Medical image noise

The purpose of this study was to evaluate the accuracy of statistical properties of the backscttered ultrasound in differential diagnosis of the breast lesions. The B-mode images together with the appropriate RF echoes from the breast lesions and surrounding tissues were collected. The RF data were processed for the statistics of the backscattered echo signals using K and Nakagami distributions characterized by the M and m parameters, respectively. Based on both, M and m parameters, a set of 18 parameters was derived.

From the point of view of the sensitivity of detection of the cancer the best score was obtained using maximum value of M parameter, the best specificity was received using the differential Nakagami parameter (the differential values between lesions and surrounding tissues). In conclusion the quantitative sonography is a method which has potential to be a complementary tool for classification of the breast lesions.

quantitative ultrasound, breast cancer, Nakagami distribution, K dstribution

A 30-MHz ultrasonic scanner was used to collect B-scan images together with appropriate radiofrequency echoes from diseased and healthy skin regions of patients with diagnosed basal cell carcinoma and pre-cancerous lesions (actinic keratosis). Radiofrequency data were processed to obtain the attenuation coefficient and statistics of the backscattered echo signal determination (K-distribution and effective density of scatterers [EDS]). The attenuation coefficient was significantly higher for patients with basal cell carcinoma than for healthy patients. Also, the pre-cancerous skin lesions had increased attenuation. The averaged EDS values for cancer lesions were significantly lower than those for pre-cancerous lesions and healthy skin. The successful differentiation between the tissue groups examined suggests the potential value of the attenuation coefficient and EDS for carcinoma characterization.

Quantitative ultrasound, High frequency, Human skin, Skin lesions, K-distribution, Attenuation coefficient, Tissue characterization

The paper presents a classification of the healthy skin and the skin lesions (basal cell carcinoma) basing on a statistics of the envelope of ultrasonic echoes. The echoes envelopes distributions were modeled using Rayleigh and K-distribution. The distributions were compared with empirical data to find which of them better models the statistics of the echo-signal obtained from the human skin. The results indicated that the K-distribution provides a better fit. Also, a characteristic parameter of the K-distribution, the effective number of scatterers (M), was investigated. The values of the M parameter, obtained for the skin cancer (basal cell carcinoma), were lower as compared to those obtained for the healthy skin. The results indicate that the statistical quantitative ultrasound parameters have a potential for extracting information useful for characterization of the skin condition.

statistics, K-distribution, Rayleigh distribution, ultrasonic scattering, human dermis

Propagation of ultrasonic waves in the tissue is sensitive to the alternation of tissue composition and structure.. This paper presents the classification of healthy skin and skin lesions (basal cell carcinoma (BCC)) based on statistic parameters of the envelope of echosignal. The statistics of envelope of the ultrasonic signal was modeled using Rayleigh and non-Rayleigh (the K-distribution) statistics. Furthermore the characteristic parameter of K-distribution, the effective number of scaterrers (M) was investigated.
Comparison of the results obtained for region of the skin where the BCC was diagnosed and the regions of healthy skin has shown differences in the values of M parameter. These results indicate that this parameter has the potential for extracting information useful for characterizing skin lesions.

high frequency, ultrasound, basall cell carcinoma, ststistics

The scattering of ultrasonic waves depends on the size, shape, acoustical properties and concentration of scatterers in tissue. In these study K distribution of the ultrasound backscatter envelope was used to assess the structural properties of the skin tissue. The custom-designed high frequency ultrasonic scanner was applied to obtain RF B-scans of the skin in vivo at the frequency of 20-30MHz.
The results are encouraging. The K distribution models the envelope statistics very well. The parameters of the K-distribution, namely, the effective number of scatterers may be useful for the skin characterization.

skin characterization, ultrasound, K distribution

The scattering of ultrasonic waves depends on the size, shape, acoustical properties and concentration of scatterers in the tissue. The spectrum of the ultrasonic backscatter can be used to characterize non-invasively the structural and mechanical properties of tissue. We intend to apply the custom-designed high-frequency ultrasonic scanner for the skin and cutaneous lesions characterization by evaluating their attenuating and scattering properties. In this pilot study, we have explored the possibility of extracting the human skin backscattering coefficient (BC) from the ultrasonic B-scans obtained in vivo at 20–30 MHz. The measured BC values of normal skin (dermis) agree well with the published data. We have found also that the spatial resolution of the BC determination using our scanner is sufficient (aprox. 1 mm2) to characterize small skin lesions and assess their penetration depth.

attenuation coefficient, backscattering coefficient, dermis

The presented study demonstrates the effectiveness of quantitative ultrasound imaging (H-scan ultrasound) in monitoring the breast cancer response to neoadjuvant chemotherapy and predicting pathological response. A group of 40 patients (51 breast tumors) participated in the study. The results showed that QUS imaging was able to distinguish between breast cancer patients responding and not responding to chemotherapy early in the course of treatment. Analysis of the percentage changes in red in the parametric images showed that this parameter can predict the response to treatment of patients with the area under the receiver operational characteristics (ROC) curve of 0.61, 0.70, 0.83, and 0.86, respectively, one week after 1st 2nd , 3rd and 4th dose of treatment.

breast cancer, neoadjuvant chemotherapy, ultrasound, H-scan ultrasounds.

In this work we investigate the usefulness and robustness of transfer learning with deep convolutional neural networks (CNNs) for breast lesion classification in ultrasound (US). Deep learning models can be vulnerable to adversarial examples, engineered input image pixel intensities perturbations that force models to make classification errors. In US imaging, distribution of US image pixel intensities relies on applied US image reconstruction algorithm. We explore the possibility of fooling deep learning models for breast mass classification by modifying US image reconstruction method. Raw radio-frequency US signals acquired from malignant and benign breast masses were used to reconstruct US images, and develop classifiers using transfer learning with the VGG19, InceptionV3 and InceptionResNetV2 CNNs. The areas under the receiver operating characteristic curve (AUCs) obtained for each deep learning model developed and evaluated using US images reconstructed in the same way were equal to approximately 0.85, and there were no associated differences in AUC values between the models (DeLong test p-values > 0.15). However, due to small modifications of the US image reconstruction method the AUC values for the models utilizing the VGG19, InceptionV3 and InceptionResNetV2 CNNs significantly decreased to 0.592, 0.584 and 0.687, respectively. Our study shows that the modification of US image reconstruction algorithm can have significant negative impact on classification performance of deep models. Taking into account medical image reconstruction algorithms may help develop more robust deep learning computer aided diagnosis systems.

Adversarial attacks, Breast lesion classification, Computer aided diagnosis, Deep learning, Robustness, Ultrasound imaging, Transfer learning

Neoadjuvant chemotherapy (NAC) is used in breast cancer patients to reduce tumor size, decrease the risk of local recurrence, and diminish the likelihood of metastases, all of which reduce patient mortality. Assessment of the response to NAC at an early stage of treatment allows therapy to be personalized. The quantitative ultrasound method is a novel diagnostic approach with great potential to improve outcomes for breast cancer patients. The aim of this study was to analyze the variability of the integrated backscatter coefficient (IBSC) in the context of the assessment of changes in tumor tissue structure resulting from chemotherapy, and to evaluate use of IBSC to predict the effectiveness of treatment. Ultrasound data (B-mode images and raw ultrasonic radio frequency signals RF) were collected from cancerous tumors from patients intended to NAC before starting treatment and a week after each chemotherapy dose. Data were processed to generate IBSC maps and to determine IBSC mean values. The assessment of tumors using IBSC, in comparison with histopathological verification, showed that IBSC changes can be associated with changes which tumor structure changes during NAC. IBSC analysis correlated better with the results of histopathological verification than B-mode imaging.

Breast cancer is one of the leading causes of cancer-related death in female patients. The quantitative ultrasound techniques being developed recently provide useful information facilitating the classification of tumors as malignant or benign. Quantitative parameters are typically determined on the basis of signals scattered within the tumor. The present paper demonstrates the utility of quantitative data estimated based on signal backscatter in the tissue surrounding the tumor. Two quantitative parameters, weighted entropy and Nakagami shape parameter were calculated from the backscatter signal envelope. The ROC curves and the AUC parameter values were used to assess their ability to classify neoplastic lesions. Results indicate that data from tissue surrounding the tumor may characterize it better than data from within the tumor. AUC values were on average 18% higher for parameters calculated from data collected from the tissue surrounding the lesion than from the data from the lesion itself.

uantitative ultrasound, Tissue characterisation, Tumor classification

In this paper we propose a computer-aided diagnosis system for the breast lesion classification. Our approach is based on quantitative ultrasound and deep learning. We used the Nakagami imaging to create parametric maps of breast lesions that illustrate tissue scattering properties. For this task the sliding window technique was applied. The Nakagami parameter was calculated using the maximum likelihood estimator. Next, we used the Nakagami parameter maps to train a convolutional neural network. Classification performance was evaluated by 5-fold cross-validation. We obtained the area under the receiver operating characteristic curve equal to 0.91. The results showed that our approach is useful to distinguishing between malignant and benign breast lesions. The proposed method serves as a general approach for tissue characterization and differentiation. The Nakagami parameter used in this study can be replaced with other QUS parameters and the neural network can be trained in a similar fashion.

Nakagami imaging, quantitative ultrasound, convolutional neural networks, breast lesion classification, deep learning

Nakagami distribution is used to model the statistical properties of backscattered echoes in tissue. The proper estimate requires the compensation of attenuation along each scanning line. Attenuation of the wave results in decreasing of the envelope mean intensity with depth what modifies the Nakagami scale parameter. This phenomenon violates the assumption that envelope samples within region of interest are identically distributed and disrupts estimation. Here, we investigate the influence of wave attenuation on Nakagami shape parameter estimators for various scattering scenarios, attenuation coefficients and region of interest size. Three methods are proposed to solve this issue. Scans of a thyroid and of a breast lesion are analyzed. It was found that proposed methods improved the estimation, especially when larger regions were used to collect envelope samples.

ultrasound, breast cancer, Nakagami distribution

The aim of the study was finding the relationship between BIRADS classification combined with envelope K and Nakagami statistics of the echoes backscattered in the breast tissue in vivo and the histological data. 107 breast lesions were examined. Both, the RF echo-signal and B-mode images from the lesions and surrounding tissue were recorded. The analysis method was based on the combining data from BIRADS classifications and both distributions parameters. 107 breasts lesions - 32 malignant and 75 benign - were examined. When only BIRADS classification was used all malignant lesions were diagnosed correctly, however 34 benign lesions were sent for the biopsy unnecessarily. For K distribution the sensitivity and specificity were 78.13%, and 86.67% while for Nakagami statistics the sensitivity and specificity were 62.50% and 93.33%, respectively. Combined K and BIRADS resulted in sensitivity of 96.67% and specificity 60%. Combined BIRADS (3/4a cut-off) plus Nakagami statistics showed 100% of sensitivity with specificity equal 57.33%, decreasing the number of lesions which were biopsied from 34 to 28.

breast cancer, quantitative ultrasound, BIRADS

Basal cell carcinoma is the most common cutaneous malignancy, representing 80% of all skin cancer cases. The quantitative ultrasound can provide information potentially helpful in diagnosing. The goal of this study was to find the quantitative measure of the skin tissue backscattering properties that could be used for differentiating the changes of tissue structure induced by Basal Cell Carcinoma (BCC) and precancerous lesions - Actinic Keratosis (AK). The study presents the results concerning the statistical properties of ultrasonic echoes scattered in cancer lesions and attenuation coefficient determined from the backscatter.

tissue characterization, quantitative ultrasound,skin lesions, statistical distribution, attenuation coefficient

The Ultrasonix SonixTouch scanner with the special RF block was used to collect Bmode images together with appropriate RF echoes from the pathological and healthy breasts regions of patients with diagnosed malignant and benign breast lesions. The RF data were processed for the statistics of the backscattered echo signals assessment (K distribution and effective density of scatterers – M and Nakagami distribution and its shape parameter m). The comparison of signals recorded from malignant and healthy tissues showed, that in 80% of examined cases the values of the statistical parameters M were higher for carcinomas tissues than for healthy tissue. Beside of that in the case of benign lesions obtained results was able to distinguish the fibroadenoma from the other with probability of 75%.

quantitative ultrasound, breast cancer, statistics

Quantitative ultrasounds were applied for the detection and assessment of skin lesions. 30MHz ultrasonic scanner was used to collect B-scan images together with appropriate RF echoes from the pathological and healthy skin regions of skin of patients with diagnosed Basal Cell Carcinoma and precancerous states. The RF data were processed for the attenuation coefficient and statistics assessment (K distribution and effective number of scatterers – M). For patients with BCC the attenuation coefficient was significantly higher than for the healthy skin ones. Also, precancerous skin lesions revealed increased attenuation. The averaged M parameter for cancer lesions was significantly lower than for precancerous states and healthy skin. Similar results were obtained from numerical simulations of the ultrasonic echoes scattered in skin and skin lesions.

skin cancer, K distribution, effective number of scatterers, high frequency ultrasound

While the needles biopsy is still the gold standard in skin cancer diagnosis there is a growing interest in application of the high frequency ultrasound for the skin lesions detection and their thickness assessment. The quantitative ultrasound can provide additional information, potentially helpful in diagnosis. The purpose of this study was to assess the usefulness of the attenuating and statistical properties of the backscattered ultrasounds for the skin tissues characterization. The paper presents the classification of the healthy skin and skin lesions (BCC-basal cell carcinoma and AK-actinic keratosis). For patients with BCC the attenuation coefficient was significantly higher than for the healthy ones. Also, precancerous skin lesions revealed increased attenuation. The statistical properties were different for BCC comparing to AK lesions and healthy skin.

basal cell carcinoma, actinic keratosis, attenuation coefficient, statistical properties, high frequency ultrasound

The scattering of ultrasonic waves depends on the size, shape, acoustical properties and concentration of scatterers in tissue. In these study the statistics of envelope of signal was used to assess the structural properties of the skin tissue. Ultrasound backscatter data were obtained from healthy and pathological human dermis in vivo. The signal envelope statistics were examined by fitting the Rayleigh and K distributions. The fit between the K distribution and the data shown much smaller error than the Rayleigh model. The parameter of the K-distribution, namely, the effective number of scatterer was calculated and the potential of the effective number of scatterers to distinguish between normal and disease tissues was investigated. Obtained results indicate that the parameter of the K-distribution maybe useful in classification of the skin lesions.

high frequency, ultrasound, skin, K distribution

Background: Monitoring of response to neoadjuvant chemotherapy (NAC) in advanced breast cancer is crucial for assessing the effectiveness of the treatment and overall survival. The purpose of this study was to investigate the ability of classical ultrasound (US) examination and quantitative ultrasound (QU) parameters to predict the therapy response comparing to histology results after surgical treatment. Material and Methods: Two ultrasound backscatter parameters: the integrated backscatter coefficient, (IBSC) and shape parameter (M) of the homodyned K distribution, were estimated from ultrasonic radiofrequency (RF) signals. Sixteen patient with 22 breast cancer tumor treated with NAC sequential Anthracyclines and Taxanes were prospectively assessed. Data were acquired using 5–14 MHz array transducer, pre-chemotherapy, and four times during treatment (one week after the subsequent courses). The US results were compared with histological response analyzing the stromal changes and the cellularity of the tumor. Results: An increase in IBSC and decrease M parameters was observed in 80% of tumors with complete response after chemotherapy. It correlates with increasing the stromal elements, fibrosis, and elastosis. In patients with partial response, the parameters IBSC and M parameters did not change during subsequent cycles of treatment and allow predicting partial response in 70% of tumors. In pathological results, a similar cluster of cells without fibrosis and elastosis were observed. Conclusions: Ultrasound parameters derived from the RF data give the promise to predict the tumor response to NAC and better personalize the therapy using US QU examination. This study was supported by the National Science Centre, Poland, grant 2016/23/B/ST8/03391. No conflict of interest

The soft tissue structure possess the multi-scale anatomical inhomogeneities. There are many types of tissues, where one can recognize at least two main scales: millimeter scale and micrometer scale which are the basic scattering structures for an ultrasound wave of the diagnostic frequency range penetrating the tissue. The millimeter scale is due to the existence of quasi-periodic blocks of cells forming semi-regular lobules e.g. in the anatomical units of the liver tissue, and the micrometer scale is formed with many small scatterers like cell walls or large cell nuclei. The breaking of anatomical structure is often caused by the beginning of the cancer process. It is very important and unresolved problem to find any tools in qualitative ultrasound to recognize this two type of scattering. To this end some phantom experiment were performed. 3D thread structure in the form of nylon threads with a thickness of 0.1 mm (or of 0.35 mm) placed at regular periodic structure with distances of 1 mm (or of 1.5 mm) was immersed in the water. This threads structure was used as a model to analyse the properties of ultrasound signal echoes registered with the use of different transducers, both focusing and plane ones. The range of carried frequencies for used transducers were of 1MHz to 15 MHz. Additionally, the threads structure with threads of of 0.35 mm thickness and located in distances of 1.5 mm, immersed in an oil and starch gel instead of the water were · was used to analyse the influence of the background medium properties on the ultrasound backscattering signals. Having measured pulse properties of a transducer and applied the wavelet analysis to the registered signals the identification of the threads positions in space, namely MSS (Mean Scatterer Spacing) was calculated and some aspects of the differences between scattering and reflection phenomena were discussed as a function of ratio between pulse length and geometrical parameters, i.e. threads thickness and distances between them.

attenuation estimation, parametric imaging

The scattering is the fundamental phenomena used for US imaging of specific organs. In this study the method searching for best fitted statistical distribution of the acquired echoes from the breast tissue is discussed, especially addressing the ‘‘effective’’ number of scatterers. The aim of the study was finding the relationship between the specific properties of statistics of envelope of the ultrasonic echoes backscattered in the breast tissue in vivo, and its morphological properties for normal tissue and the pathological lesions.
Methods: 72 patients with 83 suspicious breast lesions (BIRADS 3, 4, 5) were examined. The analysis method was based on the parametric imaging representing a map of local statistical properties of the scattering of ultrasound waves in normal and pathological tissues. Both, the RF echo-signal and B-mode images from the lesions and surrounding tissue were recorded. The statistics of backscattered speckle-like echoes envelopes were modelled using K and Nakagami distributions. For all lesions the set of sub-ROIs covering full lesion was chosen. The statistical analysis was done for every sub-ROI separately. The shape parameters were calculated including the compensation for TGC applied and for the attenuation.
Results: The evaluation of all 83 breasts lesions revealed 23 malignant and 60 benign lesions. Typically, both, shape parameters for malignant lesions were statistically larger than for surrounding tissue. On the other hand, the benign lesions revealed much larger variance of the parameters comparing to the surrounding and malignant tissue. The sensitivity and specificity of B-mode imaging with the cut-off points BIRADS-4a/4b were 93%, 86%. For K and Nakagami distributions obtained sensitivity and specificity were respectively 85% and 91%.
Conclusions: The quantitative measurements of the breast tissue backscattering statistical properties improve the specificity of B-mode examinations and can be helpful in the differentiation the character of the breast lesions. It was proved that the range of the shape parameters appears to be rather large and can not be interpreted without taking into account the corresponding values in the surrounding ‘‘normal’’ tissue.

breast cancer, ultrasound, RF echo-signal

A B-mode ultrasonography provides structural information on the tissue under investigation encoding the echo strength in gray scale in a two-dimensional image. Interpretation of the B-mode image of breast tissue is done by a physician. The analysis of statistical properties of backscattered RF signal has been recently applied successfully to distinct healthy tissue from tissue lesions regions as a new method of quantitative ultrasound (QUS). Up till now, the most reliable results were obtained for liver and renal tissue lesions, because their normal, healthy structures are nearly homogeneous while a heterogeneous breast tissue classification is still an open issue. The recent study revealed that the medium contraction and expansion induced by a temperature change may cause variations in the relative position of scatterers in a tissue. We have developed a new procedure of heating the patient breast and allowing to observe and record in vivo the influence of temperature changes on a B-mode image and properties of unprocessed radio frequency (RF) backscattered echoes. The initial, feasibility studies of influence of the temperature increase in breast tissue on the intensity, spectrum and statistics of ultrasonic echoes will be discussed.

breast tissue, RF signal, backscattered signal amplitude statistics, spectral properties

Elastografia fali poprzecznej jest nowoczesną metodą oceny sztywności tkanek. Obserwuje się wzrost zainteresowania tą techniką w różnicowaniu charakteru zmian ogniskowych w tarczycy również w diagnostyce przedoperacyjnej. Cel pracy: Celem niniejszej pracy była ocena klinicznej przydatności elastografi i fali poprzecznej w diagnostyce raka rdzeniastego tarczycy (RRT). Materiał i metoda: W badanej grupie 139 pacjentów stwierdzono 169 zmian ogniskowych, a u 4 pacjentów (średni wiek: 45 lat) rozpoznano 6 RRT. Wykonano USG B-mode oraz elastografię fali poprzecznej aparatem Aixplorer (Super-Sonic, Aix-en-Provence), głowicą liniową o częstotliwości 4–15 MHz. W USG oceniano echogeniczność i echo strukturę zmian, ich brzegi, objaw „halo”, stosunek wysokości dogłębokości (W/G), obecność zwapnień oraz wzorzec unaczynienia. Następnie analizowano: maksymalne i średnie wartości modułu Younga (E) dla największego ROI dla RRT (EmaxLR, EmeanLR ) oraz dla otaczających tkanek w miąższu gruczołu (EmaxSR, EmeanSR), a także średnie wartości E (EmeanLRz) dla 2 mm obszaru zainteresowania z najsztywniejszej części zmiany. Zmiany zweryfikowano histopatologicznie i/lub cytologicznie. Wyniki: W ocenie B-mode wszystkie RRT były hipoechogeniczne, bez obecności objawu „halo”, oraz zawierał y mikro- i/lub makrozwapnienia. Nieostre granice zmiany obecne były w 4 z 6 raków. Wartość ilorazu WG >1 dotyczył a 4 na 6 zmian. Niejednorodną echo strukturę oraz typ III unaczynienia stwierdzono w 5 na 6 zmian. W SWE średnia wartość E maxLR w obrębie RRT wynosił a 89,5 kPa, w otoczeniu – 39,7 kPa. Średnie wartości E meanLR, E meanSR wynosił y odpowiednio: 34,6 kPa i 24,4 kPa. Wartość średnia E meanLRz=49,2 kPa. Wnioski: W SWE RRT przedstawiały się jako zmiany sztywniejsze w porównaniu z otaczającymi tkankami. W ocenie B-mode spełniały wskazania do biopsji spiracyjnej cienkoigłowej. Jednak algorytm diagnostyczny RRT opiera się na pomiarze stężenia kalcytoniny w surowicy krwi, ocenie USG B-mode oraz biopsji.

rak rdzeniasty, tarczyca, badanie USG, sonoelastografia fali poprzecznej