Monitoring Breast Cancer Response to Neoadjuvant Chemotherapy Using Probability Maps Derived from Quantitative Ultrasound Parametric Images.

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 (area under the ROC curve 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.

The Utility of Contrast-Enhanced Ultrasound (CEUS) in Assessing the Risk of Malignancy in Thyroid Nodules.

BACKGROUND: Ultrasonography is a primary method used in the evaluation of thyroid nodules, but no single feature of this method predicts malignancy with high accuracy. Therefore, this paper aims to assess the utility of contrast-enhanced ultrasound (CEUS) in the differential diagnosis of thyroid nodules. METHODS: The study group comprised 188 adult patients (155 women and 33 men) who preoperatively underwent CEUS of a thyroid nodule classified as Bethesda categories II-VI after fine-needle aspiration biopsy. During the CEUS examination, 1.5 mL of SonoVue contrast was injected intravenously, after which 15 qualitative CEUS enhancement patterns were analysed. RESULTS: The histopathologic results comprised 65 benign thyroid nodules and 123 thyroid carcinomas. The dominant malignant CEUS features, such as hypo- and heterogeneous enhancement and slow wash-in phase, were evaluated, whereas high enhancement, ring enhancement, and a slow wash-out phase were assessed as predictors of benign lesions. Two significant combinations of B-mode and CEUS patterns were noted, namely, hypoechogenicity with heterogeneous enhancement and non-smooth margins with hypo- or iso-enhancement. CONCLUSIONS: 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.

Ultrasound and cytopathological characteristics of thyroid tumours of uncertain malignant potential - from diagnosis to treatment.

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.

Protocol of Breast Cancer Prevention Model with Addition of Breast Ultrasound to Routine Gynecological Visits as a Chance for an Early Diagnosis and Treatment in 25 to 49-Year-Old Polish Females.

The low attendance rate for cancer screening tests in Poland is a major healthcare concern that requires specific analysis and the development of implementation recommendations for prevention, and both actions are likely to benefit culturally similar countries. Four female cancers account for approximately 20% of all cancer cases-breast cancer, cervical cancer, endometrial cancer, and ovarian cancer-suggesting that gynecologists have a significant preventative role. Of the four, breast cancer and cervical cancer are among the 10 most common malignant neoplasms globally, regardless of gender, occur only in women and are known to have effective screening measures. Our research aims to create a screening model that combines cervical cancer and breast cancer to maximize health outcomes for women at risk of both cancers. In the study protocol, we have created a model that maximizes benefits for patients with minimal additional costs to the health care system. To achieve the set goal, instead of regular clinical breast exams as recommended by the gynecological societies, we proposed an ultrasound examination, during which palpation may also be performed (in the absence of elastography). We present a scheme for such a protocol that takes into consideration all types of prevention in both cancers, and that emphasizes breast ultrasound as the most frequently missing element. Our study includes a discussion of the strengths and weaknesses of our strategy, and the crucial need for infrastructure and education for the successful implementation of the program. We conclude that our model merits consideration and discussion among health-care decision makers, as the screening changes we propose have significant potential benefits for the female population.

Quantitative Ultrasonography as a Tool for the Evaluation of Breast Tumor Response to Neoadjuvant Chemotherapy.

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.

Explaining a Deep Learning Based Breast Ultrasound Image Classifier with Saliency Maps.

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 limited 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 classification. 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 specificity 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.

Immunohistochemical Subtypes of The Breast Cancer in The Ultrasound and Clinical Aspect - Literature Review.

Breast cancer is a heterogeneous disease both in its clinical and radiological manifestations and response to treatment. This is largely due to the polymorphism of the histological types as well as diversified molecular profiles of individual breast cancer types. Progress in the understanding of the biology of breast cancer was made with the introduction of immunohistochemical research into the common practice. On this basis, four main breast cancer subtypes were distinguished: luminal A, luminal B, HER2 positive (human epidermal growth factor receptor-2 positive), and triple negative cancer. The classification of a tumour to an appropriate subtype allows for the optimisation of treatment (surgery or pre-operative chemotherapy). In this study, the authors present different patterns of breast cancer subtypes in ultrasound examination and differences in their treatment, with particular emphasis on aggressive breast cancer subtypes, such as triple negative or HER2 positive. They can, unlike the luminal subtypes, create diagnostic problems. Based on multifactorial analysis of the ultrasound image, with the assessment of lesion margins, orientation, shape, echogenicity, vascularity, the presence of calcifications or assessment by sonoelastography, it is possible to initially differentiate individual subtypes.

Sarcoidosis of The Breasts - When Should It Be Considered? a Case Report.

Sarcoidosis is a systemic inflammatory disease of unknown aetiology. Given its complex clinical presentation, the disorder frequently causes diagnostic challenges. In most cases, the primary manifestation is in the lungs and mediastinum. Breast involvement as the primary manifestation of sarcoidosis is rare, accounting for less than 1% of cases. The authors present the case of a 44-year-old woman whose disease first manifested as multiple non-specific BIRADS 4 lesions in both breasts, accompanied by axillary lymphadenopathy, detected by ultrasound examination. The lesions were not visible on mammography. The course of the disease was clinically silent, with intermittent remissions, until the complete resolution of focal breast lesions on ultrasound after two years of follow-up. The paper presents an algorithm for the management of multifocal breast pathology with associated lymphadenopathy, which led to the prompt verification of sarcoidosis.

Joint segmentation and classification of breast masses based on ultrasound radio-frequency data and convolutional neural networks.

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.

Assessment of breast cancer response to neoadjuvant chemotherapy based on ultrasound backscattering envelope statistics.

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 responding to NAC after the first dose of the drug, with AUC equal 0.83 ± $\pm$ 0.06 and 0.84 ± $\pm$ 0.07, respectively. After the third dose, the AUC increased to 0.90 ± $\pm$ 0.05 and 0.91 ± $\pm$ 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.

Prediction of response to neoadjuvant chemotherapy in breast cancer with recurrent neural networks and raw ultrasound signals.

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.

Summary of Meta-analyses of Studies Involving TIRADS Classifications (EU-TIRADS, ACR-TIRADS, and K-TIRADS) in Evaluating the Malignant Potential of Focal Lesions of The Thyroid Gland.

Numerous scientific societies around the world have published their TIRADS (Thyroid Imaging Reporting and Data System) classifications that evaluate the risk of malignancy of focal thyroid lesions, presenting different ultrasound features for each category and lesion size thresholds to determine eligibility for biopsy. The use of such risk estimation systems in focal thyroid lesions facilitates the reporting of thyroid ultrasound findings and improves the qualification of focal lesions for fine-needle aspiration biopsy (FNAB). In this publication, the three most popular TIRADS classifications, European - EU-TIRADS, Korean - K-TIRADS, and developed by the American Society of Radiology - ACR-TIRADS, are presented and discussed based on a literature review. The results of available head-to-head statistical analyses comparing the classifications are also presented. The advantage of the EU-TIRADS and K-TIRADS systems is that they include only the most important ultrasound features, so their application is not time-consuming, and the scores are easy to incorporate into clinical practice. ACR-TIRADS, unlike other scales, is based on a unique classification system and represents the most comprehensive classification. Each of the five categories of ultrasound features - morphology, echogenicity, shape, margins, microcalcifications - are evaluated and assigned a score from 0 to 3, with a higher score being associated with a higher risk of cancer. Based on the available data, the greatest benefit has been demonstrated for the ACR-TIRADS classification, which also has implications for minimising the number of unnecessary FNABs. However, limitations related to the heterogeneity of the groups analysed in the study, including differences in the populations studied, inclusion criteria, proportions of patients of either sexes, and the number of malignant lesions analysed, should also be taken into account.

Summary of Meta-analyses of Studies Considering Lesion Size Cut-off Thresholds for The Assessment of Eligibility for FNAB and Sonoelastography and Inter- and Intra-observer Agreement in Estimating the Malignant Potential of Focal Lesions of The Thyroid Gland.

Thyroid cancer is a tumour with a steadily increasing incidence. It accounts for 7% to 15% of focal lesions detected by ultrasound, depending on age, gender and other factors affecting its occurrence. Fine-needle aspiration biopsy is an essential method to establish the diagnosis but, in view of its limitations, sonoelastography is seen as a non-invasive technique useful in differentiating the nature of lesions and monitoring them after fine-needle aspiration biopsy. This paper presents a literature review on the role of both sonoelastographic techniques (relative strain sonoelastography, shear wave sonoelastography) to assess the deformability of focal thyroid lesions. Ultrasound examination is a relatively subjective method of thyroid imaging, depending on the skills of the examiner, the experience of the centre, and the quality of equipment used. As a consequence, there are inconsistencies between the results obtained by different examiners (inter-observer variability) and by the same examiner (intra-observer variability). In this paper, the authors present a review of the literature on inter-observer and intra-observer variability in the assessment of individual features of ultrasound imaging of focal lesions in the thyroid. In addition, the authors report on an analysis of cut-off thresholds for the size of lesions constituting the basis for fine-needle aspiration biopsy eligibility assessment. The need to diagnose carcinomas up to 10 mm in diameter is highlighted, however a more liberal approach is recommended in terms of indications for biopsy in lesions associated with a low risk of malignancy, where, based on consultations with patients, active ultrasound surveillance might even be considered.

Diagnosis and treatment of thyroid cancer in adult patients - Recommendations of Polish Scientific Societies and the National Oncological Strategy. 2022 Update [Diagnostyka i leczenie raka tarczycy u chorych doroslych - Rekomendacje Polskich Towarzystw Naukowych oraz Narodowej Strategii Onkologicznej. Aktualizacja na rok 2022].

The guidelines Thyroid Cancer 2022 are prepared based on previous Polish recommendations updated in 2018. They consider international guidelines - American Thyroid Association (ATA) 2015 and National Comprehensive Cancer Network (NCCN); however, they are adapted according to the ADAPTE process. The strength of the recommendations and the quality of the scientific evidence are assessed according to the GRADE system and the ATA 2015 and NCCN recommendations. The core of the changes made in the Polish recommendations is the inclusion of international guidelines and the results of those scientific studies that have already proven themselves prospectively. These extensions allow de-escalation of the therapeutic management in low-risk thyroid carcinoma, i.e., enabling active surveillance in papillary microcarcinoma to be chosen alternatively to minimally invasive techniques after agreeing on such management with the patient. Further extensions allow the use of thyroid lobectomy with the isthmus (hemithyroidectomy) in low-risk cancer up to 2 cm in diameter, modification of the indications for postoperative radioiodine treatment toward personalized approach, and clarification of the criteria used during postoperative L-thyroxine treatment. At the same time, the criteria for the preoperative differential diagnosis of nodular goiter in terms of ultrasonography and fine-needle aspiration biopsy have been clarified, and the rules for the histopathological examination of postoperative thyroid material have been updated. New, updated rules for monitoring patients after treatment are also presented. The updated recommendations focus on ensuring the best possible quality of life after thyroid cancer treatment while maintaining the good efficacy of this treatment.

Is There a Correlation between Multiparametric Assessment in Ultrasound and Intrinsic Subtype of Breast Cancer?

Molecular profile of breast cancer provides information about its biological activity, prognosis and treatment strategies. The purpose of our study was to investigate the correlation between ultrasound features and molecular subtypes of breast cancer. From June 2019 to December 2019, 86 patients (median age 57 years; range 32-88) with 102 breast cancer tumors were included in the study. The molecular subtypes were classified into five types: luminal A (LA), luminal B without HER2 overexpression (LB HER2-), luminal B with HER2 overexpression (LB HER2+), human epidermal growth factor receptor 2 positive (HER2+) and triple negative breast cancer (TNBC). Histopathological verification was obtained in core biopsy or/and post-surgery specimens in all cases. Univariate logistic regression analysis was performed to assess the association between the subtypes and ultrasound imaging features. Experienced radiologists assessed lesions according to the BIRADS-US lexicon. The ultrasound scans were performed with a Supersonic Aixplorer and Supersonix. Based on histopathological verification, the rates of LA, LB HER2-, LB HER2+, HER2+, and TNBC were 33, 17, 17, 16, 19, respectively. Both LB HER2+ and HER2+ subtypes presented higher incidence of calcification (OR = 3.125, p = 0.02, CI 0.0917-5.87) and HER2+ subtype presented a higher incidence of posterior enhancement (OR = 5.75, p = 0.03, CI 1.2257-32.8005), compared to other subtypes. The calcifications were less common in TNBC (OR = 0.176, p = 0.0041, CI 0.0469-0.5335) compared to other subtypes. There were no differences with regard to margin, shape, orientation, elasticity values and vascularity among five molecular subtypes. Our results suggest that there is a correlation between ultrasonographic features assessed according to BIRADS-US lexicon and BC subtypes with HER2 overexpression (both LB HER2+ and HER2+). It may be useful for identification of these aggressive subtypes of breast cancer.

Early Prediction of Response to Neoadjuvant Chemotherapy in Breast Cancer Sonography Using Siamese Convolutional Neural Networks.

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.

Quantitative Assessment of the Echogenicity of a Breast Tumor Predicts the Response to Neoadjuvant Chemotherapy.

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.

S-Detect Software vs. EU-TIRADS Classification: A Dual-Center Validation of Diagnostic Performance in Differentiation of Thyroid Nodules.

Computer-aided diagnosis (CAD) and other risk stratification systems may improve ultrasound image interpretation. This prospective study aimed to compare the diagnostic performance of CAD and the European Thyroid Imaging Reporting and Data System (EU-TIRADS) classification applied by physicians with S-Detect 2 software CAD based on Korean Thyroid Imaging Reporting and Data System (K-TIRADS) and combinations of both methods (MODELs 1 to 5). In all, 133 nodules from 88 patients referred to thyroidectomy with available histopathology or with unambiguous results of cytology were included. The S-Detect system, EU-TIRADS, and mixed MODELs 1-5 for the diagnosis of thyroid cancer showed a sensitivity of 89.4%, 90.9%, 84.9%, 95.5%, 93.9%, 78.9% and 93.9%; a specificity of 80.6%, 61.2%, 88.1%, 53.7%, 73.1%, 89.6% and 80.6%; a positive predictive value of 81.9%, 69.8%, 87.5%, 67%, 77.5%, 88.1% and 82.7%; a negative predictive value of 88.5%, 87.2%, 85.5%, 92.3%, 92.5%, 81.1% and 93.1%; and an accuracy of 85%, 75.9%, 86.5%, 74.4%, 83.5%, 84.2%, and 87.2%, respectively. Comparison showed superiority of the similar MODELs 1 and 5 over other mixed models as well as EU-TIRADS and S-Detect used alone (p-value < 0.05). S-Detect software is characterized with high sensitivity and good specificity, whereas EU-TIRADS has high sensitivity, but rather low specificity. The best diagnostic performance in malignant thyroid nodule (TN) risk stratification was obtained for the combined model of S-Detect ("possibly malignant" nodule) and simultaneously obtaining 4 or 5 points (MODEL 1) or exactly 5 points (MODEL 5) on the EU-TIRADS scale.

Ultrasound echogenicity reveals the response of breast cancer to chemotherapy.

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 = 57 years). 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-lesions characterization using Quantitative Ultrasound features of peritumoral tissue.

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.