Does shear wave ultrasound independently predict axillary lymph node metastasis in women with invasive breast cancer?

Enhancing Axillary Lymph Node Diagnosis in Breast Cancer with a Novel Photoacoustic Imaging-Based Radiomics Nomogram: A Comparative Study of Peritumoral Regions

RATIONALE AND OBJECTIVES

This study aims to assess the predictive ability of photoacoustic (PA) imaging-based radiomics combined with clinical characteristics for axillary lymph node (ALN) status in early-stage breast cancer patients and to compare performance in different peritumoral regions.

METHODS

This study involved 369 patients from Shenzhen People's Hospital, divided into a training set of 295 and a testing set of 74. PA imaging data were collected from all participants, and radiomics analysis was performed on intratumoral and various peritumoral regions. Features extracted from the training set were analyzed using LASSO regression to construct a model integrating radiomics features with clinical characteristics. Clinical factors were determined through multivariate logistic regression analysis. A radiomics nomogram was developed using logistic regression classifiers, combining radiomics features and clinical factors. The predictive efficacy of the model was evaluated using the areas under curves (AUC), and its clinical utility and accuracy were assessed through decision curve analysis and calibration curves, respectively.

RESULTS

The developed nomogram combines 5 mm peritumoral data with intratumoral and clinical features and shows excellent diagnostic performance, achieving an AUC of 0.972 in the training set and in the testing achieved 0.905. They both showed good calibrations. The model outperformed models based solely on clinical features or other radiomics methods, with the 5 mm surrounding tumor area proving most effective in identifying positive versus negative ALN in breast cancer patients.

CONCLUSION

The established nomogram is a prospective clinical prediction tool for non-invasive assessment of ALN status. It has the ability to enhance the accuracy of early-stage breast cancer treatment.

SUMMARY

This study highlights the effectiveness of combining photoacoustic radiomics with clinical parameters to predict axillary lymph node status in breast cancer, identifying a 5 mm peritumoral model as particularly potent. Future research should aim to enhance this model's robustness by expanding the sample size and advancing imaging technologies for broader clinical application.

An Intraoperative Ultrasound Evaluation of Axillary Lymph Nodes: Cassandra Predictive Models in Patients with Breast Cancer-A Multicentric Study

Background and Objectives: Axillary lymph node (ALN) staging is crucial for the management of invasive breast cancer (BC). Although various radiological investigations are available, ultrasound (US) is the preferred tool for evaluating ALNs. Despite its immediacy, widespread use, and good predictive value, US is limited by intra- and inter-operator variability. This study aims to evaluate US and Elastosonography Shear Wave (SW-ES) parameters for ALN staging to develop a predictive model, named the Cassandra score (CS), to improve the interpretation of findings and standardize staging. Materials and Methods: Sixty-three women diagnosed with BC and treated at two Italian hospitals were enrolled in the study. A total of 529 lymph nodes were surgically removed, underwent intraoperative US examination, and were individually sent for a final histological analysis. The study aimed to establish a direct correlation between eight US-SWES features (margins, vascularity, roundness index (RI), loss of hilum fat, cortical thickness, shear-wave elastography hardness (SWEH), peripheral infiltration (PI), and hypoechoic appearance) and the histological outcome (benign vs. malignant). Results: Several statistical models were compared. PI was strongly correlated with malignant ALNs. An ROC analysis for Model A revealed an impressive AUC of 0.978 (S.E. = 0.007, p < 0.001), while in Model B, the cut-offs of SWEH and RI were modified to minimize the risk of false negatives (AUC of 0.973, S.E. = 0.009, p < 0.001). Model C used the same cut-offs as Model B, but excluded SWEH from the formula, to make the Cassandra model usable even if the US machine does not have SW-ES capability (AUC of 0.940, S.E. = 0.015, p < 0.001). A two-tiered model was finally set up, leveraging the strong predictive capabilities of SWEH and RI. In the first tier, only SWES and RI were evaluated: a positive result was predicted if both hardness and roundness were present (SWES > 137 kPa and RI < 1.55), and conversely, a negative result was predicted if both were absent (SWES < 137 kPa and RI > 1.55). In the second tier, if there was a mix of the results (SWES > 137 kPa and RI > 1.55 or SWES < 137 kPa and RI < 1.55), the algorithm in Model B was applied. The model demonstrated an overall prediction accuracy of 90.2% in the training set, 87.5% in the validation set, and 88.9% across the entire dataset. The NPV was notably high at 99.2% in the validation set. This model was named the Cassandra score (CS) and is proposed for the clinical management of BC patients. Conclusion: CS is a simple, non-invasive, fast, and reliable method that showed a PPV of 99.1% in the malignancy prediction of ALNs, potentially being also well suited for young sonographers.

The correlation between multi-mode ultrasonographic features of breast cancer and axillary lymph node metastasis

Objective

This study aimed to explore the correlation between multi-mode ultrasonographic features of breast cancer and axillary lymph node metastasis.

Method

A total of 196 patients with surgically confirmed breast cancer between September 2019 and December 2023 were included. Data on preoperative B-mode ultrasound (US), color Doppler, and shear wave elastography (SWE) features of breast cancer masses were collected and analyzed to determine their correlation with axillary lymph node metastasis. The area under the receiver operating characteristic curve (AUC) of B-mode US, color Doppler, SWE, and the multi-mode predictive model for evaluating axillary lymph node metastasis were compared.

Results

Among the 196 patients, 70 had positive axillary lymph nodes, while 126 had negative axillary lymph nodes. There was no significant difference in the color features between the negative and positive axillary lymph node groups. Multifocality/multicentricity, architectural distortion, microcalcifications, and the "stiff rim" sign in SWE were identified as independent risk factors to predict axillary lymph node metastasis according to binary logistic regression analysis. The AUC of the predictive model based on these independent risk factors was 0.803 (95% CI: 0.739-0.867), which was significantly higher than that of B-mode US or SWE alone.

Conclusion

Multifocality/multicentricity, architectural distortion, microcalcifications, and the "stiff rim" sign in SWE were found to be valuable for predicting axillary lymph node metastasis in patients with breast cancer. The predictive model developed in this study, combining the multi-mode ultrasonographic features of breast cancer masses, could serve as a noninvasive and convenient method to predict axillary lymph node status. This approach could aid in clinical decision-making and individualized treatment to improve the prognosis of breast cancer patients.

Preoperative Role of Superb Microvascular Imaging and Shear-Wave Elastography for Prediction of Axillary Lymph Node Metastasis in Patients With Breast Cancer

ABSTRACT

This study aims to evaluate the role of shearwave elastography (SWE) and superb microvascular imaging (SMI) for preoperative prediction of axillary lymph node metastasis (ALNM) in patients with breast cancer. In a cohort of 214 women with breast cancer, B-Mode ultrasonography (US), SMIvascular-index (SMIvi), and SWE (E-mean, E-ratio) values were recorded before tru-cut biopsy. Axillary fine-needle aspiration biopsy (FNAB) and sentinel lymph node sampling results were collected. Imaging findings and histopathological data were statistically compared. Receiver operating characteristic curve analysis was used to evaluate diagnostic performance. Reverse stepwise logistical regression analysis was conducted. Although ALNM was negative in 111 cases, it was positive in 103 patients. Axillary lymph node metastasis (+) group had larger size ( P < 0.001), higher vascularization (SMIvi: 8.0 ± 6.0 versus 5.0 ± 4.3, P < 0.001), and higher elasticity value (E-mean: 129 ± 31 kPa versus 117.3 ± 40 kPa, P = 0.014). Axillary lymph node metastasis was observed statistically more frequently in Her-2 positive cases ( P = 0.005). There was no significant difference between other B-mode US findings ( P > 0.05), SMI Adler ( P = 0.878), and E-ratio ( P = 0.212). The most appropriate cutoff value for the prediction of ALNM was 23.5 mm for size, 3.8 for SMIvi, and 138.5 kPa for E-mean. The most sensitive (77%) method was the SMIvi measurement, while the most specific (86%) finding was Her-2 positivity. The combined model (being Her-2 positive, >23.5 cm, and >3.8 SMIvi) increased the specificity (78%), PPV (71%), and accuracy (68%). Although the increased size is a previously studied parameter in predicting the risk of ALNM, Her-2 and data obtained by SWE, and SMI can be used to assist conventional US.

Predictive nomogram using multimodal ultrasonographic features for axillary lymph node metastasis in early‑stage invasive breast cancer

Axillary lymph node (ALN) status is a key prognostic factor in patients with early-stage invasive breast cancer (IBC). The present study aimed to develop and validate a nomogram based on multimodal ultrasonographic (MMUS) features for early prediction of axillary lymph node metastasis (ALNM). A total of 342 patients with early-stage IBC (240 in the training cohort and 102 in the validation cohort) who underwent preoperative conventional ultrasound (US), strain elastography, shear wave elastography and contrast-enhanced US examination were included between August 2021 and March 2022. Pathological ALN status was used as the reference standard. The clinicopathological factors and MMUS features were analyzed with uni- and multivariate logistic regression to construct a clinicopathological and conventional US model and a MMUS-based nomogram. The MMUS nomogram was validated with respect to discrimination, calibration, reclassification and clinical usefulness. US features of tumor size, echogenicity, stiff rim sign, perfusion defect, radial vessel and US Breast Imaging Reporting and Data System category 5 were independent risk predictors for ALNM. MMUS nomogram based on these factors demonstrated an improved calibration and favorable performance [area under the receiver operator characteristic curve (AUC), 0.927 and 0.922 in the training and validation cohorts, respectively] compared with the clinicopathological model (AUC, 0.681 and 0.670, respectively), US-depicted ALN status (AUC, 0.710 and 0.716, respectively) and the conventional US model (AUC, 0.867 and 0.894, respectively). MMUS nomogram improved the reclassification ability of the conventional US model for ALNM prediction (net reclassification improvement, 0.296 and 0.288 in the training and validation cohorts, respectively; both P<0.001). Taken together, the findings of the present study suggested that the MMUS nomogram may be a promising, non-invasive and reliable approach for predicting ALNM.

Ultrasound-based radiomics nomogram for predicting axillary lymph node metastasis in early-stage breast cancer

PURPOSE

We aimed at assessing the predictive ability of ultrasound-based radiomics combined with clinical characteristics for axillary lymph node (ALN) status in early-stage breast cancer patients and to compare performance in different peritumoral regions.

MATERIALS AND METHODS

A total of 755 patients (527 in the primary cohort and 228 in the external validation cohort) were enrolled in this study. Ultrasound images for all patients were acquired and radiomics analysis performed for intratumoral and different peritumoral regions. The MRMR and LASSO regression analyses were performed on extracted features from the primary cohort to construct a radiomics signature formula combined with clinical characteristics. Pearson's coefficient and the variance inflation factor (VIF) were performed to check the correlation and the multicollinearity among the final predictors. The best performing model was selected to develop a nomogram, which was established by performing binary logistic regression and acquiring cut-off values based on the corresponding nomogram scores of the masses.

RESULTS

Among all the radiomics models, the "Mass + Margin3mm" model exhibited the best performance. The areas under the curves (AUC) of the nomogram in the primary and external validation cohorts were 0.906 (95% confidence intervals [CI] 0.882-0.930) and 0.922 (95% CI 0.894-0.960), respectively. They both showed good calibrations. The nomogram exhibited a good ability to discriminate between positive and negative lymph nodes (AUC: 0.853 (95% CI 0.816-0.889) in primary cohort, 0.870 (95% CI 0.818-0.922) in validation cohort), and between low-volume and high-volume lymph nodes (AUC: 0.832 (95% CI 0.781-0.884) in primary cohort, 0.911 (95% CI 0.858-0.964) in validation cohort).

CONCLUSIONS

The established nomogram is a prospective clinical prediction tool for non-invasive assessment of ALN status. It has the ability to enhance the accuracy of early-stage breast cancer treatment.

Elasticity Values as a Predictive Modality for Response to Neoadjuvant Chemotherapy in Breast Cancer

Shear-wave elastography (SWE) is an effective tool in discriminating malignant lesions of breast and axillary lymph node metastasis in patients with breast cancer. However, the association between the baseline elasticity value of breast cancer and the treatment response of neoadjuvant chemotherapy is yet to be elucidated. Baseline SWE measured mean stiffness (E-mean) and maximum stiffness (E-max) in 830 patients who underwent neoadjuvant chemotherapy and surgery from January 2012 to December 2022. Association of elasticity values with breast pCR (defined as ypTis/T0), pCR (defined as ypTis/T0, N0), and tumor-infiltrating lymphocytes (TILs) was analyzed. Of 830 patients, 356 (42.9%) achieved breast pCR, and 324 (39.0%) achieved pCR. The patients with low elasticity values had higher breast pCR and pCR rates than those with high elasticity values. A low E-mean (adjusted odds ratio (OR): 0.620; 95% confidence interval (CI): 0.437 to 0.878; p = 0.007) and low E-max (adjusted OR: 0.701; 95% CI: 0.494 to 0.996; p = 0.047) were independent predictive factors for breast pCR. Low elasticity values were significantly correlated with high TILs. Pretreatment elasticity values measured using SWE were significantly associated with treatment response and inversely correlated with TILs, particularly in HR+HER2- breast cancer and TNBC.

The value of microvascular Doppler ultrasound technique, qualitative or quantitative shear-wave elastography of breast lesions for predicting axillary nodal burden in patients with breast cancer

Background

The status of the axillary lymph node (ALN) in patients with breast cancer can critically inform clinical decision-making and prognosis. Preoperative evaluation of limited nodal burden (0-2 metastatic ALNs) and high nodal burden (≥3 metastatic ALNs) is vital for individual treatment in patients with breast cancer. Thus, this study aimed to evaluate the value of Angio-PLUS (AP; Aixplorer, SuperSonic Imagine) and the qualitative and quantitative shear-wave elastography (SWE) of breast lesions to predict limited or high axillary nodal burden and to develop a model for predicting limited or high axillary nodal burden.

Methods

From March 2020 to November 2022, a total of 232 consecutive patients with breast cancer comprising 232 breast lesions were enrolled retrospectively from Yueyang Central Hospital. The sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), accuracy, and area under the receiver operating characteristic curve (AUC) of AP, qualitative SWE, quantitative SWE, and the predictive model for evaluating limited or high axillary nodal burden were compared.

Results

There was no significant difference in AP patterns between the limited nodal burden group and high nodal burden group. The best cutoff values of Emin (the minimal value of the first Q-box), Emean (the mean value of the first Q-box), Emax (the maximum value of the first Q-box), Eratio (ratio of the first Q-Box and the second Q-Box) and standard deviation for predicting limited or high nodal burden were 80.85 KPa, 133.45 KPa, 153.40 KPa, 9.95, and 19.25 KPa, respectively. The Emax had the highest AUC, and its sensitivity, specificity, PPV, NPV, accuracy, and AUC were 71.64%, 56.36%, 40.00%, 83.04%, 60.78%, and 0.640 [95% confidence interval (CI): 0.575-0.702], respectively. The sensitivity, specificity, PPV, NPV, accuracy, and AUC of seven color patterns for qualitative SWE were 71.64%, 74.55%, 53.33%, 86.62%, 73.71%, and 0.731 (95% CI: 0.669-0.787), respectively, which was significantly higher than all the other quantitative SWE parameters. ALN evaluation in ultrasound and qualitative SWE were independent risk factors for predicting limited or high nodal burden according to a binary logistics regression analysis. The AUC of the predictive model based on independent risk factors was 0.820 (95% CI: 0.765-0.867), which was significantly higher than that of the other independent risk factors.

Conclusions

The seven color patterns in the qualitative SWE of breast lesions were valuable for predicting limited or high nodal burden for patients with breast cancer. Compared with quantitative SWE, qualitative SWE exhibited a better diagnostic performance. Breast lesions present no findings, vertical stripes, and spot patterns were important indicators for limited nodal burden. The predictive model developed in this study could be a simple, noninvasive, and convenient method for predicting limited or high nodal burden, which would be beneficial for clinical decision-making and individual treatment to improve prognosis.

Strain ultrasonic elastography imaging features of locally advanced breast cancer: association with response to neoadjuvant chemotherapy and recurrence-free survival

BACKGROUND

Due to the highly heterogeneity of the breast cancer, it would be desirable to obtain a non-invasive method to early predict the treatment response and survival outcome of the locally advanced breast cancer (LABC) patients undergoing neoadjuvant chemotherapy (NAC). This study aimed at investigating whether strain elastography (SE) can early predict the pathologic complete response (pCR) and recurrence-free survival (RFS) in LABC patients receiving NAC.

METHODS

In this single-center retrospective study, 122 consecutive women with LABC who underwent SE examination pre-NAC and after one and two cycles of NAC enrolled in the SHPD001(NCT02199418) and SHPD002 (NCT02221999) trials between January 2014 and August 2017 were included. The SE parameters (Elasticity score, ES; Strain ratio, SR; Hardness percentage, HP, and Area ratio, AR) before and during NAC were assessed. The relative changes in SE parameters after one and two cycles of NAC were describe as ΔA1 and ΔA2, respectively. Logistic regression analysis and Cox proportional hazards model were used to identify independent variables associated with pCR and RFS.

RESULTS

Forty-nine (40.2%) of the 122 patients experienced pCR. After 2 cycles of NAC, SR2 (odds ratio [OR], 1.502; P = 0.003) and ΔSR2 (OR, 0.013; P = 0.015) were independently associated with pCR, and the area under the receiver operating characteristic curve for the combination of them to predict pCR was 0.855 (95%CI: 0.779, 0.912). Eighteen (14.8%) recurrences developed at a median follow-up of 60.7 months. A higher clinical T stage (hazard ratio [HR] = 4.165; P = 0.005.), a higher SR (HR = 1.114; P = 0.002.) and AR (HR = 1.064; P <  0.001.) values at pre-NAC SE imaging were independently associated with poorer RFS.

CONCLUSION

SE imaging features have the potential to early predict pCR and RFS in LABC patients undergoing NAC, and then may offer valuable predictive information to guide personalized treatment.

Usefulness of machine learning and deep learning approaches in screening and early detection of breast cancer

Breast cancer (BC) is one of the most common types of cancer in women, and its prevalence is on the rise. The diagnosis of this disease in the first steps can be highly challenging. Hence, early and rapid diagnosis of this disease in its early stages increases the likelihood of a patient's recovery and survival. This study presents a systematic and detailed analysis of the various ML approaches and mechanisms employed during the BC diagnosis process. Further, this study provides a comprehensive and accurate overview of techniques, approaches, challenges, solutions, and important concepts related to this process in order to provide healthcare professionals and technologists with a deeper understanding of new screening and diagnostic tools and approaches, as well as identify new challenges and popular approaches in this field. Therefore, this study has attempted to provide a comprehensive taxonomy of applying ML techniques to BC diagnosis, focusing on the data obtained from the clinical methods diagnosis. The taxonomy presented in this study has two major components. Clinical diagnostic methods such as MRI, mammography, and hybrid methods are presented in the first part of the taxonomy. The second part involves implementing machine learning approaches such as neural networks (NN), deep learning (DL), and hybrid on the dataset in the first part. Then, the taxonomy will be analyzed based on implementing ML approaches in clinical diagnosis methods. The findings of the study demonstrated that the approaches based on NN and DL are the most accurate and widely used models for BC diagnosis compared to other diagnostic techniques, and accuracy (ACC), sensitivity (SEN), and specificity (SPE) are the most commonly used performance evaluation criteria. Additionally, factors such as the advantages and disadvantages of using machine learning techniques, as well as the objectives of each research, separately for ML technology and BC detection, as well as evaluation criteria, are discussed in this study. Lastly, this study provides an overview of open and unresolved issues related to using ML for BC diagnosis, along with a proposal to resolve each issue to assist researchers and healthcare professionals.

Ultra-Rapid and Specific Gelation of Collagen Molecules for Transparent and Tough Gels by Transition Metal Complexation

Collagen is the most abundant protein in the human body and one of the main components of stromal tissues in tumors which have a high elastic modulus of over 50 kPa. Although collagen has been widely used as a cell culture scaffold for cancer cells, there have been limitations when attempting to fabricate a tough collagen gel with cells like a cancer stroma. Here, rapid gelation of a collagen solution within a few minutes by transition metal complexation is demonstrated. Type I collagen solution at neutral pH shows rapid gelation with a transparency of 81% and a high modulus of 1,781 kPa by mixing with K2 PtCl4 solution within 3 min. Other transition metal ions also show the same rapid gelation, but not basic metal ions. Interestingly, although type I to IV collagen molecules show rapid gelation, other extracellular matrices  do not exhibit this phenomenon. Live imaging of colon cancer organoids in 3D culture indicates a collective migration property with modulating high elastic modulus, suggesting activation for metastasis progress. This technology will be useful as a new class of 3D culture for cells and organoids due to its facility for deep-live observation and mechanical stiffness adjustment.

Progress in research on ultrasound radiomics for predicting the prognosis of breast cancer

Breast cancer is the most common malignant tumor and the leading cause of cancer-related deaths in women worldwide. Effective means of predicting the prognosis of breast cancer are very helpful in guiding treatment and improving patients' survival. Features extracted by radiomics reflect the genetic and molecular characteristics of a tumor and are related to its biological behavior and the patient's prognosis. Thus, radiomics provides a new approach to noninvasive assessment of breast cancer prognosis. Ultrasound is one of the commonest clinical means of examining breast cancer. In recent years, some results of research into ultrasound radiomics for diagnosing breast cancer, predicting lymph node status, treatment response, recurrence and survival times, and other aspects, have been published. In this article, we review the current research status and technical challenges of ultrasound radiomics for predicting breast cancer prognosis. We aim to provide a reference for radiomics researchers, promote the development of ultrasound radiomics, and advance its clinical application.

Correlation of shear-wave elastography parameters with the molecular subtype and axillary lymph node status in breast cancer

PURPOSE

To examine correlations between shear-wave elastography (SWE) parameters with molecular subtype and axillary lymph node (LN) status of breast cancer.

METHODS

We retrospectively analyzed 545 consecutive women (mean age, 52.7 ± 10.7 years; range, 26-83) with breast cancer who underwent preoperative breast ultrasound with SWE between December 2019 and January 2021. SWE parameters (E_max, E_mean, and E_ratio) and the histopathologic information from surgical specimens including histologic type, histologic grade, size of invasive cancer, hormone receptor and HER2 status, Ki-67 proliferation index, and axillary LN status were analyzed. The relationships between SWE parameters and histopathologic findings were analyzed using an independent sample t-test, one-way ANOVA test with Tukey's post hoc test, and logistic regression analyses.

RESULTS

Higher stiffness values of SWE were associated with larger lesion size (>20 mm) on ultrasound, high histologic grade, larger invasive cancer size (>20 mm), high Ki-67, and axillary LN metastasis. E_max and E_mean were the lowest in the luminal A-like subtype, and all three parameters were the highest in the triple-negative subtype. Lower value of E_max was independently associated with the luminal A-like subtype (P = 0.04). Higher value of E_mean was independently associated with axillary LN metastasis for tumors ≤ 20 mm (P = 0.03).

CONCLUSION

Increases in the tumor stiffness values on SWE were significantly associated with aggressive histopathologic features of breast cancer. Lower stiffness values were associated with the luminal A-like subtype, and tumors with higher stiffness values were associated with axillary LN metastasis in small breast cancers.

An improved nomogram including elastography for the prediction of non-sentinel lymph node metastasis in breast cancer patients with 1 or 2 sentinel lymph node metastases

Background

The rate of breast-conserving surgery is very low in China, compared with that in developed countries; most breast cancer patients receive mastectomy. It is great important to explore the possibility of omitting axillary lymph node dissection (ALND) in early-stage breast cancer patients with 1 or 2 positive sentinel lymph nodes (SLNs) in China. The aim of this study was to develop a nomogram based on elastography for the prediction of the risk of non-SLN (NSLN) metastasis in early-stage breast cancer patients with 1 or 2 positive SLNs.

Methods

A total of 601 breast cancer patients were initially recruited. According to the inclusion and exclusion criteria, 118 early-stage breast cancer patients with 1 or 2 positive SLNs were finally enrolled and were assigned to the training cohort (n=82) and the validation cohort (n=36), respectively. In the training cohort, the independent predictors were screened by logistic regression analysis and then were used to conducted the nomogram for the prediction of NSLN metastasis in early-stage breast cancer patients with 1 or 2 positive SLNs. The calibration curves, concordance index (C-index), the area under the receiver operating characteristic (ROC) curve (AUC), and Decision curve analysis (DCA) were used to verified the performance of the nomogram.

Results

The multivariable analysis showed that the enrolled patients with positive HER2 expression (OR=6.179, P=0.013), Ki67≥14% (OR=8.976, P=0.015), larger lesion size (OR=1.038, P=0.045), and higher Emean (OR=2.237, P=0.006) were observed to be the independent factors of NSLN metastasis. Based on the above four independent predictors, a nomogram was conducted to predict the risk of the NSLN metastasis in early-stage breast cancer patients with 1 or 2 positive SLNs. The nomogram showed good discrimination in the prediction of NSLN metastasis, with bias-corrected C-index of 0.855 (95% CI, 0.754-0.956) and 0.853 (95% CI, 0.724-0.983) in the training and validation cohorts, respectively. Furthermore, the AUC was 0.877 (95%CI: 0.776- 0.978) and 0.861 (95%CI: 0.732-0.991), respectively, indicating a good performance of the nomogram. The calibration curve suggested a satisfactory agreement between the predictive and actual risk in both the training (χ2 = 11.484, P=0.176, HL test) and validation (χ2 = 6.247, p = 0.620, HL test) cohorts, and the obvious clinical nets were revealed by DCA.

Conclusions

We conducted a satisfactory nomogram model to evaluate the risk of NSLN metastasis in early-stage breast cancer patients with 1 or 2 SLN metastases. This model could be considered as an ancillary tool to help such patients to be selectively exempted from ALND.

Correlation analysis between shear-wave elastography and pathological profiles in breast cancer

PURPOSE

To explore the correlation between shear-wave elastography (SWE) parameters and pathological profiles of invasive breast cancer.

METHODS

A total of 197 invasive breast cancers undergoing preoperative SWE and primary surgical treatment were included. Maximum elastic modulus (Emax), mean elastic modulus (Emean), and elastic modulus standard deviation (Esd) were calculated by SWE. Pathological profile was gold standard according to postoperative pathology. The relationship between SWE parameters and pathological factors were analyzed using univariate and multivariate analysis.

RESULTS

In univariate analysis, large cancers showed significantly higher Emax, Emean and Esd (all P < 0.001). Emax and Esd in the group of histological grade III were higher than those in the group of grade I (both P < 0.05). Invasive lobular carcinomas (ILC) showed higher Emean than invasive ductal carcinoma (IDC) (P < 0.001). Lymphovascular invasion (LVI) group showed higher Emax values than negative group (P < 0.05). Emax, Emean and Esd of the Ki-67 positive group presented higher values than negative group (all P < 0.05). Androgen receptor (AR) positive lesions had lower Esd than AR negative lesions (P < 0.05). In multivariate analysis, invasive size independently influenced Emax (P < 0.001). Invasive size and pathological type both independently influenced Emean (both P < 0.001). Invasive size and AR status were both independently influenced Esd (both P < 0.05).

CONCLUSION

SWE parameters correlated with pathological profiles of invasive breast cancer.In particular, AR positive group showed significantly low Esd than negative group.

In Search of an Imaging Classification of Adenomyosis: A Role for Elastography?

Adenomyosis is a complex and poorly understood gynecological disease. It used to be diagnosed exclusively by histology after hysterectomy; today its diagnosis is carried out increasingly by imaging techniques, including transvaginal ultrasound (TVUS) and magnetic resonance imaging (MRI). However, the lack of a consensus on a classification system hampers relating imaging findings with disease severity or with the histopathological features of the disease, making it difficult to properly inform patients and clinicians regarding prognosis and appropriate management, as well as to compare different studies. Capitalizing on our grasp of key features of lesional natural history, here we propose adding elastographic findings into a new imaging classification of adenomyosis, incorporating affected area, pattern, the stiffest value of adenomyotic lesions as well as the neighboring tissues, and other pathologies. We argue that the tissue stiffness as measured by elastography, which has a wider dynamic detection range, quantitates a fundamental biologic property that directs cell function and fate in tissues, and correlates with the extent of lesional fibrosis, a proxy for lesional "age" known to correlate with vascularity and hormonal receptor activity. With this new addition, we believe that the resulting classification system could better inform patients and clinicians regarding prognosis and the most appropriate treatment modality, thus filling a void.

Collagen fiber features and COL1A1: are they associated with elastic parameters in breast lesions, and can COL1A1 predict axillary lymph node metastasis?

Background

This study aimed to explore whether collagen fiber features and collagen type I alpha 1 (COL1A1) are related to the stiffness of breast lesions and whether COL1A1 can predict axillary lymph node metastasis (LNM).

Methods

Ninety-four patients with breast lesions were consecutively enrolled in the study. Amongst the 94 lesions, 30 were benign, and 64 were malignant (25 were accompanied by axillary lymph node metastasis). Ultrasound (US) and shear wave elastography (SWE) were performed for each breast lesion before surgery. Sirius red and immunohistochemical staining were used to examine the shape and arrangement of collagen fibers and COL1A1 expression in the included tissue samples. We analyzed the correlation between the staining results and SWE parameters and investigated the effectiveness of COL1A1 expression levels in predicting axillary LNM.

Results

The optimal cut-off values for Emax, Emean, and Eratio for diagnosing the benign and malignant groups, were 58.70 kPa, 52.50 kPa, and 3.05, respectively. The optimal cutoff for predicting axillary LNM were 107.5 kPa, 85.15 kPa, and 3.90, respectively. Herein, the collagen fiber shape and arrangement features in breast lesions were classified into three categories. One-way analysis of variance (ANOVA) showed that Emax, Emean, and Eratio differed between categories 0, 1, and 2 (P < 0.05). Meanwhile, elasticity parameters were positively correlated with collagen categories and COL1A1 expression. The COL1A1 expression level > 0.145 was considered the cut-off value, and its efficacy in benign and malignant breast lesions was 0.808, with a sensitivity of 66% and a specificity of 90%. Furthermore, when the COL1A1 expression level > 0.150 was considered the cut-off, its efficacy in predicting axillary LNM was 0.796, with sensitivity and specificity of 96% and 59%, respectively.

Conclusions

The collagen fiber features and expression levels of COL1A1 positively correlated with the elastic parameters of breast lesions. The expression of COL1A1 may help diagnose benign and malignant breast lesions and predict axillary LNM.

Prediction of Axillary Lymph Node Metastasis in Invasive Breast Cancer by Sound Touch Elastography

The aims of this study were to investigate the value of sound touch elastography (STE) in predicting axillary lymph node metastasis (ALNM) in patients with invasive breast cancer (IBC) and to explore whether lysyl oxidase (LOX) is correlated with increasing stiffness and promotion of metastasis in IBC. A total of 142 lesions in 142 patients were assessed by STE. The STE values of IBCs in the two groups were compared and the best cutoff values for diagnosing ALNM determined. Immunohistochemistry was used to detect LOX expression. Collagen fiber and elastic fiber content was determined by Masson and Weigert elastic fiber staining. Correlation analyses were performed to identify the associations of the data. The optimal cutoff values of E_max (maximum stiffness value of the tumor) and S_max (maximum stiffness value of the shell) for predicting ALNM of IBC were 94.58 and 148.78 kPa. Immunohistochemistry and Masson and Weigert elastic fiber staining were performed on 67 samples. LOX expression and collagen volume fraction were significantly higher in the ALNM+ group than in the ALNM- group (p = 0.04 and 0.03), except for elastic fiber content (p = 0.628). Moreover, E_max, S_max and LOX expression were positively correlated with collagen volume fraction (r = 0.624, 0.512, and 0.533, respectively). E_max and S_max were found to be predictors for ALNM of IBC. STE could serve as a non-invasive method for assessing lymph node status before surgery. Overexpression of LOX and increased collagen fiber contributed to the increased stiffness in the lesions and metastases of IBC.

Effectiveness of Quantitative Shear Wave Elastography for the Prediction of Axillary Lymph Node Metastasis

Objective

Invasive breast cancer can be metastasized through axillary lymph nodes (LNs). This study was to evaluate the effectiveness of quantitative shear wave elastography (SWE) to predict axillary LN metastasis, which also provides prognostic implication of SWE as a histopathologic element of invasive breast cancer.

Methods

72 prospectively enrolled patients received B-mode ultrasound (BUS) and SWE, and the elasticity index (EI) of SWE at the stiffest part of lymph nodes (LNs) was measured. EI of SWE was closely associated with pathologic results and the histopathologic elements. The receiver operating characteristics (ROC) curve was drawn to evaluate the optimal cut-off value for the assessment of disease severity.

Results

A significantly longer short-axis diameter and a larger maximal cortex were observed in malignant LNs than that in healthy LNs. The absence of the hilum was associated with metastatic LNs. The EI of SWE varied markedly between the benign and malignant LNs. The combination of E_{max and} BUS showed higher area under the curve (AUC) than BUS alone to predict metastatic LNs (0.7762 vs. 0.7230). EI of SWE in malignant lymph nodes those with extranodal extension are higher than those without extranodal extension.

Conclusions

Quantitative SWE provides a viable alternative for the assessment of axillary LN and shows great potential to predict pathological prognostic elements of metastatic axillary LNs in invasive breast cancer. Joint use of SWE and BUS allows examination of the predictive outcome of BUS for axillary lymph node metastasis in invasive breast cancer.

Ultrasound elastography

Physicians have used palpation as a diagnostic examination to understand the elastic properties of pathology for a long time since they realized that tissue stiffness is closely related to its biological characteristics. US elastography provided new diagnostic information about elasticity comparing with the morphological feathers of traditional US, and thus expanded the scope of the application in clinic. US elastography is now widely used in the field of diagnosis and differential diagnosis of abnormality, evaluating the degree of fibrosis and assessment of treatment response for a range of diseases. The World Federation of Ultrasound Medicine and Biology divided elastographic techniques into strain elastography (SE), transient elastography and acoustic radiation force impulse (ARFI). The ARFI techniques can be further classified into point shear wave elastography (SWE), 2D SWE, and 3D SWE techniques. The SE measures the strain, while the shear wave-based techniques (including TE and ARFI techniques) measure the speed of shear waves in tissues. In this review, we discuss the various techniques separately based on their basic principles, clinical applications in various organs, and advantages and limitations and which might be most appropriate given that the majority of doctors have access to only one kind of machine.

Radiomics model based on shear-wave elastography in the assessment of axillary lymph node status in early-stage breast cancer

OBJECTIVES

To develop and validate an ultrasound elastography radiomics nomogram for preoperative evaluation of the axillary lymph node (ALN) burden in early-stage breast cancer.

METHODS

Data of 303 patients from hospital #1 (training cohort) and 130 cases from hospital #2 (external validation cohort) between Jun 2016 and May 2019 were enrolled. Radiomics features were extracted from shear-wave elastography (SWE) and corresponding B-mode ultrasound (BMUS) images. The minimum redundancy maximum relevance and least absolute shrinkage and selection operator algorithms were used to select ALN status-related features. Proportional odds ordinal logistic regression was performed using the radiomics signature together with clinical data, and an ordinal nomogram was subsequently developed. We evaluated its performance using C-index and calibration.

RESULTS

SWE signature, US-reported LN status, and molecular subtype were independent risk factors associated with ALN status. The nomogram based on these variables showed good discrimination in the training (overall C-index: 0.842; 95%CI, 0.773-0.879) and the validation set (overall C-index: 0.822; 95%CI, 0.765-0.838). For discriminating between disease-free axilla (N0) and any axillary metastasis (N + (≥ 1)), it achieved a C-index of 0.845 (95%CI, 0.777-0.914) for the training cohort and 0.817 (95%CI, 0.769-0.865) for the validation cohort. The tool could also discriminate between low (N + (1-2)) and heavy metastatic ALN burden (N + (≥ 3)), with a C-index of 0.827 (95%CI, 0.742-0.913) in the training cohort and 0.810 (95%CI, 0.755-0.864) in the validation cohort.

CONCLUSION

The radiomics model shows favourable predictive ability for ALN staging in patients with early-stage breast cancer, which could provide incremental information for decision-making.

KEY POINTS

• Radiomics analysis helps radiologists to evaluate the axillary lymph node status of breast cancer with accuracy. • This multicentre retrospective study showed that radiomics nomogram based on shear-wave elastography provides incremental information for risk stratification. • Treatment can be given with more precision based on the model.

Comprehensive Risk System Based on Shear Wave Elastography and BI-RADS Categories in Assessing Axillary Lymph Node Metastasis of Invasive Breast Cancer-A Multicenter Study

PURPOSE

To develop a risk stratification system that can predict axillary lymph node (LN) metastasis in invasive breast cancer based on the combination of shear wave elastography (SWE) and conventional ultrasound.

MATERIALS AND METHODS

A total of 619 participants pathologically diagnosed with invasive breast cancer underwent breast ultrasound examinations were recruited from a multicenter of 17 hospitals in China from August 2016 to August 2017. Conventional ultrasound and SWE features were compared between positive and negative LN metastasis groups. The regression equation, the weighting, and the counting methods were used to predict axillary LN metastasis. The sensitivity, specificity, and the areas under the receiver operating characteristic curve (AUC) were calculated.

RESULTS

A significant difference was found in the Breast Imaging Reporting and Data System (BI-RADS) category, the "stiff rim" sign, minimum elastic modulus of the internal tumor and peritumor region of 3 mm between positive and negative LN groups (p < 0.05 for all). There was no significant difference in the diagnostic performance of the regression equation, the weighting, and the counting methods (p > 0.05 for all). Using the counting method, a 0-4 grade risk stratification system based on the four characteristics was established, which yielded an AUC of 0.656 (95% CI, 0.617-0.693, p < 0.001), a sensitivity of 54.60% (95% CI, 46.9%-62.1%), and a specificity of 68.99% (95% CI, 64.5%-73.3%) in predicting axillary LN metastasis.

CONCLUSION

A 0-4 grade risk stratification system was developed based on SWE characteristics and BI-RADS categories, and this system has the potential to predict axillary LN metastases in invasive breast cancer.

Feasibility of Shear Wave Elastography Imaging for Evaluating the Biological Behavior of Breast Cancer

Objective

To explore the feasibility of shear wave elastography (SWE) parameters for assessing the biological behavior of breast cancer.

Materials and Methods

In this prospective study, 224 breast cancer lesions in 216 female patients were examined by B-mode ultrasound and shear wave elastography in sequence. The maximum size (S_max) of the lesion was measured by B-mode ultrasound, and then shear wave elastography was performed on this section to obtain relevant parameters, including maximum elasticity (E_max), mean elasticity (E_mean), standard deviation of elasticity (SD), and the area ratio of shear wave elastography to B-mode ultrasound (AR). The relationship between SWE parameters and pathological type, histopathological classification, histological grade, lymphovascular invasion status (LVI), axillary lymph node status (ALN), and immunohistochemistry of breast cancer lesions was performed according to postoperative pathology.

Results

In the univariate analysis, the pathological type and histopathological classification of breast cancer were not significantly associated with SWE parameters; with an increase in the histological grade of invasive ductal carcinoma (IDC), SD (p = 0.016) and S_max (p = 0.000) values increased. In the ALN-positive group, S_max (p = 0.004) was significantly greater than in the ALN-negative group; S_max (p = 0.003), E_max (p = 0.034), and SD (p = 0.045) were significantly higher in the LVI-positive group than in the LVI-negative group; SD (p = 0.043, p = 0.047) and S_max (p = 0.000, p = 0.000) were significantly lower in the ER⁺ and PR⁺ groups than in the ER^- and PR^- groups, respectively; AR (p = 0.032) was significantly higher in the ER⁺ groups than in the ER^- groups, and S_max (p = 0.002) of the HER2⁺ group showed higher values than that of the HER2^- group; S_max (p = 0.000), SD (p = 0.006), and E_max (p = 0.004) of the Ki-67 high-expression group showed significantly higher values than those of the Ki-67 low-expression group. In the multivariate analysis, Ki-67 was an independent factor of S_max (p = 0.005), E_max (p = 0.004), and SD (p = 0.006); ER was an independent influencing factor of S_max (p = 0.000) and AR (p = 0.032). LVI independently influences S_max (p = 0.006).

Conclusions

The SWE parameters E_max, SD, and AR can be used to evaluate the biological behavior of breast cancer.

Diagnostic Accuracy of Shear Wave Elastography as an Adjunct Tool in Detecting Axillary Lymph Nodes Metastasis

OBJECTIVES

This study evaluates the diagnostic performance of shear wave elastography (SWE) in differentiating between benign and axillary lymph node (ALN) metastasis in breast carcinoma.

MATERIALS AND METHODS

Breast lesions and axillae of 107 patients were assessed using B-mode ultrasound and SWE. Histopathology was the diagnostic gold standard.

RESULTS

In metastatic axillary lymph nodes, qualitative SWE using color patterns had the highest area under curve (AUC) value, followed by B-mode Ultrasound (cortical thickening >3 mm) and quantitative SWE using Emax of 15.2 kPa (AUC of 81.3%, 70.1%, and 61.2%, respectively). Qualitative SWE exhibited better diagnostic performance than the other two parameters, with sensitivity of 96.0% and specificity of 56.1%. Combination of B-mode Ultrasound (using cortical thickness of >3 mm as cut-off point) and qualitative SWE (Color patterns of 2 to 4) showed sensitivity of 71.6%, specificity of 95%, PPV of 96%, NPV of 66.7%, and accuracy of 80.4%.

CONCLUSION

Qualitative SWE assessment exhibited higher accuracy compared to quantitative values. Qualitative SWE as an adjunct to B-mode ultrasound can further improve the diagnostic accuracy of metastatic ALN in breast cancer.

Stiffness Could be a Predictor of AJCC Prognostic Stage Groups in Preoperative Invasive Ductal Carcinoma

OBJECTIVES

This study aimed to evaluate the stiffness of 2-dimensional (2D) shear wave elastography (SWE) in preoperatively predicting the prognostic stage groups of invasive ductal carcinoma (IDC).

METHODS

Eighty-six newly diagnosed lesions on 83 patients with IDCs were analyzed. All parameters from conventional ultrasound and stiffness to virtual touch tissue imaging and quantification were collected, and mean shear wave velocity (SWVmean) was calculated. Data on maximum diameter, estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), histologic grading system and Tumor Node Metastasis (TNM) stages were collected. The levels of maximum shear wave velocity (SWVmax), minimum shear wave velocity (SWVmin) and SWVmean were compared. In receiver operating characteristic (ROC) curves analysis, the diagnostic efficacy was found in area under the curve (AUC). Parallel mode was used to improve the predictive value of sensitivity.

RESULTS

The median stiffness of SWVmax and SWVmean for IDCs were 9.38 and 6.32 m/s for late stage (stages II, III, IV) and 6.39 m/s and 4.72 m/s for early stage (stage I) of the prognostic stage groups, respectively. The median stiffness values in the late stage were significantly higher than those in the early stage (P = .003, P = .005). The optimal cutoff stiffness of SWVmax and SWVmean were 8.62 and 6.13 m/s, respectively. In ROC curves analysis, the AUC for SWVmax was 0.742, and it showed a better diagnostic value than SWVmean (0.725). In predictive diagnosis, the sensitivity for SWVmax and SWVmean were both 62.50%. The parallel mode improved the prediction power of sensitivity to 68.75%.

CONCLUSIONS

Preoperative SWV level may serve as a promising prognostic imaging indicator for breast IDCs.

Mapping Mechanical Properties of the Tumor Microenvironment by Laser Speckle Rheological Microscopy

Altered mechanical properties of the tumor matrix have emerged as both the cause and consequence of breast carcinogenesis. Increased tumor stiffness has traditionally provided a viable metric to screen for malignancies via palpation or imaging. Previous studies have demonstrated that the microscale mechanical properties of the cell substrate influence tumor proliferation and invasive migration in vitro. Nevertheless, the association of the mechanical microenvironment with clinical hallmarks of aggressiveness in human breast tumors, including histopathological subtype, grade, receptor expression status, and lymph node involvement is poorly understood. This is largely due to the lack of tools for mapping tumor viscoelastic properties in clinical specimens with high spatial resolution over a large field of view (FoV). Here we introduce laser Speckle rHEologicAl micRoscopy (SHEAR) that for the first time enables mapping the magnitude viscoelastic or shear modulus, |G*(x,y,ω)|, over a range of frequencies (ω = 1-250 rad/second) in excised tumors within minutes with a spatial resolution of approximately 50 μm, over multiple cm² FoV. Application of SHEAR in a cohort of 251 breast cancer specimens from 148 patients demonstrated that |G*(x,y,ω)| (ω = 2π rad/second) closely corresponds with histological features of the tumor, and that the spatial gradient of the shear modulus, |∇|G*(x,y,ω)||, is elevated at the tumor invasive front. Multivariate analyses established that the metrics, (|G* |) and (|∇|G* ||), measured by SHEAR are associated with prognosis. These findings implicate the viscoelastic properties of the tumor microenvironment in breast cancer prognosis and likely pave the path for identifying new modifiable targets for treatment. SIGNIFICANCE: Laser speckle rheological microscopy establishes the links between microscale heterogeneities of viscoelasticity and histopathological subtype, tumor grade, receptor expression, as well as lymph node status in breast carcinoma.

Are baseline mammographic and ultrasound features associated with metastasis free survival in women receiving neoadjuvant chemotherapy for invasive breast cancer?

OBJECTIVES

To identify associations between baseline ultrasound (US) and mammographic features and metastasis free survival (MFS) in women receiving neo-adjuvant chemotherapy (NACT) for breast cancer.

METHODS

The data were collected as part of an ethically approved prospective study. Women with invasive breast cancer receiving NACT who were metastasis free at diagnosis were included. Baseline US and mammography were performed. Imaging was assessed by an experienced breast radiologist who was blinded to outcomes. US imaging features documented included posterior effect, skin thickening, size and stiffness using shear wave elastography (SWE). The mammographic features documented were spiculation and microcalcification. The development of metastatic disease was ascertained from computer records. Statistical analysis was performed using Kaplan Meier survival curves and Receiver Operator Characteristic (ROC) analysis.

RESULTS

171 women with 172 cancers were included in the study and 55 developed metastatic disease. Mean follow-up was 6.0 years. Women with mammographic calcification had significantly poorer metastasis free survival (MFS) compared to women without calcification (p = 0.043, 6 yr MFS 50 % vs 69 %). Women bearing cancer with distal shadowing had poorer MFS than women without shadowing (p = 0.025, 6 yr MFS 47 % vs. 73 %). Women with US skin thickening had poorer MFS compared to women without skin thickening (p = 0.032, 6 yr MFS 52 % vs. 68 %). Mammographic spiculation, US size and stiffness at SWE had no significant association with MFS.

CONCLUSION

We have identified mammographic and US features associated with MFS in women receiving NACT. Such information may be useful when counselling patients about the benefits and risks of NACT.

Prediction of Invasive Breast Cancer Using Mass Characteristic Frequency and Elasticity in Correlation with Prognostic Histologic Features and Immunohistochemical Biomarkers

This purpose of this study is to correlate a new shear-wave elastography (SWE) parameter, mass characteristic frequency (fmass) and other elasticity measure with the prognostic histological factors and immunohistochemical (IHC) biomarkers for the evaluation of heterogeneous breast carcinomas. The new parameter, fmass, first introduced in this paper, is defined as the ratio of the averaged minimum shear wave speed taken spatially within regions of interest to the largest mass dimension. 264 biopsy-proven breast cancerous masses were included in this study. Mean (Emean), maximum (Emax), minimum (Emin) shear wave elasticity and standard deviation (Esd) of shear wave elasticity were found significantly correlated with tumor size, axillary lymph node (ALN) status, histological subtypes and IHC subtypes. The areas under the curve for the ALN prediction are 0.73 (95% confidence interval [CI]: 0.67-0.80) and 0.75 (95% CI: 0.69-0.81) for the combination of Emean with Breast Imaging Reporting and Data System (BI-RADS) score and Emax with BI-RADS score, respectively. fmass was significantly correlated with the presence of calcifications, ALN status, histological grade, the expressions of IHC biomarkers and IHC subtypes. To conclude, poor prognostic factors were associated with high shear wave elasticity values and low mass characteristic frequency value. Therefore, SWE provides valuable information that may help with prediction of breast cancer invasiveness.

Decoupling convolution network for characterizing the metastatic lymph nodes of breast cancer patients

PURPOSE

The dual-energy computed tomography (DECT) technique is an emerging imaging tool that can better characterize material features and has the potential to be a noninvasive means of predicting lymph node metastasis. The purpose of this study was to establish a DECT-specified quantitative approach based on a neural network to characterize the sentinel lymph node (SLN).

METHODS

With IRB approval, we retrospectively collected a total of 229 patients (100/229 metastasis) with biopsy proven breast cancer in this study. The chest and axillary spectral CT examinations were performed prior to the axillary lymph node (ALN) surgery. A decoupling convolution network with 11 ROIs from sequential keV (40 to 140 keV with 10 keV increment) was proposed to explicitly extract the spectral and spatial features in a DECT to predict the lymph node status. Focal loss was introduced as the loss function. The metric of the slope of the spectral Hounsfield unit curve measured at the venous phase was used as the baseline approach in comparison to our approach. In additional, a logistic model with radiomic features was also compared to our approach. The area under ROC curve (AUC) was used as the figure of merit to evaluate the classification performance.

RESULTS

By introducing spectral convolution and focal loss, AUC on test set could be improved by 0.15 and 0.01 separately. Compared to the slope of the spectral curve with the average AUC of 0.611 and radiomic model with AUC of 0.825, the proposed approach demonstrates a considerably better performance, with test set AUC value of 0.837, by using decoupling spectral and spatial convolution together with focal loss function.

CONCLUSIONS

We presented a new decoupling neural network based quantification method for DECT analysis, which might have potential as a noninvasive tool to predict metastasis lymph node status for breast cancer in clinical practice.

TGF-β1: is it related to the stiffness of breast lesions and can it predict axillary lymph node metastasis?

Background

This study aimed to explore whether transforming growth factor β1 (TGF-β1) is correlated with the stiffness of breast lesions and if it can predict axillary lymph node (ALN) metastasis.

Methods

A retrospective analysis was performed in our hospital. A total of 135 breast lesions in 130 patients who were to undergo vacuum-assisted excisional biopsy (VAEB) or surgery were enrolled between April 2018 and October 2018. Ultrasound (US) and shear wave elastography (SWE) examinations were performed for every lesion before VAEB or surgery. Pathology results obtained by VAEB or surgery were regarded as gold criteria. The elastic parameters and TGF-β1 expression level of malignant breast lesions were compared with those of benign lesions; the relationship between TGF-β1 expression level in breast lesions and the elastic parameters was analyzed; the TGF-β1 expression level in breast lesions with or without ALN metastasis were compared; and the efficacy of TGF-β1 expression level in predicting ALN metastasis was analyzed.

Results

The malignant breast lesions were different from benign lesions in the maximum and mean elasticity (Emax, Emean), standard deviation of elasticity (ESD), elastic ratio of the lesions to the peripheral tissue (Eratio), and the occurrence rate of "stiff rim sign" (P<0.001). The expression level of TGF-β1 in benign breast lesions was significantly lower than that in malignant lesions (P<0.001), and the TGF-β1 expression level was positively correlated with Emax, Emean, ESD, and Eratio (r=0.869, 0.840, 0.834, and 0.734, respectively). The expression level of TGF-β1 in breast lesions with or without "stiff rim sign" was significantly different (P<0.001), and the TGF-β1 expression level in malignant breast lesions with ALN metastasis was significantly higher than that in malignant lesions without ALN metastasis (P=0.0009). When TGF-β1 expression level >0.3138 was taken as the cut-off value, its efficacy in predicting ALN metastasis was 0.853, with a sensitivity of 86.67%, and a specificity 83.33%.

Conclusions

The expression level of TGF-β1 was positively correlated with the elastic parameters of breast lesions, and it could be useful for predicting ALN metastasis, especially for negative ALN diagnosis clinically.

Diagnostic Accuracy of Four Levels of Manual Compression Applied in Supersonic Shear Wave Elastography of the Breast

PURPOSE

To investigate the diagnostic accuracy of applying four levels of manual pressure in Shear Wave Elastography (SWE) of the breast and to assess inter-rater reliability.

MATERIALS AND METHODS

Single-center prospective preliminary study including patients receiving ultrasound examination of breast lesions as part of routine clinical practice. SWE was performed on 60 breast masses (26 benign and 34 malignant) in 54 patients by a breast fellowship trained radiologist. Stiffness values were compared between benign and malignant masses at four levels of manual compression: none, mild, moderate, and marked. Accuracy of SWE was assessed using receiving operating characteristics analysis at each level. In 18 patients, a second radiologist repeated the SWE acquisitions to evaluate reproducibility. Reproducibility was assessed using intraclass correlation coefficient.

RESULTS

Without compression, we observed no significant difference in stiffness (p > 0.99) between benign and malignant lesions, and SWE demonstrated low accuracy (area under the curve = 0.64). Stiffness was higher in malignant lesions at all levels of compression (p < 0.001). SWE demonstrated good accuracy at all three levels of compression (from area under the curve = 0.71 to 0.84 across Emax and Emean), with high interobserver agreement.

CONCLUSION

This preliminary study suggests that not using compression during SWE for breast lesion characterization offers suboptimal results. On the contrary, application of compression yields high diagnostic performance with good interobserver agreement and, as such, should be included in routine clinical practice.

3D bioprinting of engineered breast cancer constructs for personalized and targeted cancer therapy

The bioprinting technique with specialized tissue production allows the study of biological, physiological, and behavioral changes of cancerous and non-cancerous tissues in response to pharmacological compounds in personalized medicine. To this end, to evaluate the efficacy of anticancer drugs before entering the clinical setting, tissue engineered 3D scaffolds containing breast cancer and derived from the especially patient, similar to the original tissue architecture, can potentially be used. Despite recent advances in the manufacturing of 3D bioprinted breast cancer tissue (BCT), many studies still suffer from reproducibility primarily because of the uncertainty of the materials used in the scaffolds and lack of printing methods. In this review, we present an overview of the breast cancer environment to optimize personalized treatment by examining and identifying the physiological and biological factors that mimic BCT. We also surveyed the materials and techniques related to 3D bioprinting, i.e, 3D bioprinting systems, current strategies for fabrication of 3D bioprinting tissues, cell adhesion and migration in 3D bioprinted BCT, and 3D bioprinted breast cancer metastasis models. Finally, we emphasized on the prospective future applications of 3D bioprinted cancer models for rapid and accurate drug screening in breast cancer.

High-throughput functional profiling of single adherent cells via hydrogel drop-screen

Single-adherent-cell phenotyping on an extracellular matrix (ECM) is essential to determine cellular biological functions, such as morphological adaptations and biomolecule secretions, correlated to medical treatments and metastasis, yet there is no available platform for such high-throughput screening. Here, a novel hydrogel drop-screen device was developed to rapidly measure large-scale single-cell morphologies and multiple secretions on substrates for phenotype profiling. Single cells were first anchored to microfluidically fabricated gelatin particles providing mechanical stimulations similar to those from ECM in vivo. The cellular morphologies were then examined by quantifying the amount of cytoskeleton expressed on the particles. With droplet encapsulation, adherent single-cell multiplexed secretion analysis of a disintegrin and metalloproteinases (ADAMs) and matrix metalloproteinases (MMPs) was conducted at a throughput of ∼102 cells per second, revealing distinct functional heterogeneities associated with extracellular mechanical stimulations. The level of cell heterogeneity increased with increasing substrate stuffiness. Moreover, because of the promising screening capability, a database related to both nontumorigenic and tumorigenic breast cells (MCF10A, MCF-7, and MDA-MB-231) was constructed. The respective cell distributions and heterogeneities based on the morphologies and secreted bioindicators, such as MMP-2, MMP-3, MMP-9, and ADAM-8, were measured and found to correspond to the progress of tumor metastasis.

Breast Elasticity Imaging Techniques: Comparison of Strain Elastography and Shear-Wave Elastography in the Same Population

Our purpose was to compare the diagnostic performances of strain elastography (SE) and shear-wave elastography (SWE) in differentiating breast lesions by combining with conventional ultrasound (US). A total of 198 patients with 203 breast lesions underwent conventional US, SE and SWE examination using MyLab 90 and Aixplorer US systems. The SE parameters were SEscore, fat-to-lesion ratio, gland-to-lesion ratio, muscle-to-lesion ratio and SEmean, and the SWE parameters were Emax, Emean, Emin and Esd. Conventional US had the best diagnostic performance, with an area under the curve (AUC) of 0.896. Among all SE parameters, the AUCs of SEscore, fat-to-lesion ratio and SEmean were 0.802, 0.810 and 0.833. For SWE parameters, they were 0.845, 0.746 and 0.845, respectively, for Emax, Emean and Esd. When combined with US, the sensitivity and AUC of SWE seemed to be better than those of SE (96.55% vs. 93.10%, 0.958 vs. 0.947), but no statistically significant difference existed between them.

Axillary Nodal Evaluation in Breast Cancer: State of the Art

Axillary lymph node (LN) metastasis is the most important predictor of overall recurrence and survival in patients with breast cancer, and accurate assessment of axillary LN involvement is an essential component in staging breast cancer. Axillary management in patients with breast cancer has become much less invasive and individualized with the introduction of sentinel LN biopsy (SLNB). Emerging evidence indicates that axillary LN dissection may be avoided in selected patients with node-positive as well as node-negative cancer. Thus, assessment of nodal disease burden to guide multidisciplinary treatment decision making is now considered to be a critical role of axillary imaging and can be achieved with axillary US, MRI, and US-guided biopsy. For the node-positive patients treated with neoadjuvant chemotherapy, restaging of the axilla with US and MRI and targeted axillary dissection in addition to SLNB is highly recommended to minimize the false-negative rate of SLNB. Efforts continue to develop prediction models that incorporate imaging features to predict nodal disease burden and to select proper candidates for SLNB. As methods of axillary nodal evaluation evolve, breast radiologists and surgeons must work closely to maximize the potential role of imaging and to provide the most optimized treatment for patients.

Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer

Accurate identification of axillary lymph node (ALN) involvement in patients with early-stage breast cancer is important for determining appropriate axillary treatment options and therefore avoiding unnecessary axillary surgery and complications. Here, we report deep learning radiomics (DLR) of conventional ultrasound and shear wave elastography of breast cancer for predicting ALN status preoperatively in patients with early-stage breast cancer. Clinical parameter combined DLR yields the best diagnostic performance in predicting ALN status between disease-free axilla and any axillary metastasis with areas under the receiver operating characteristic curve (AUC) of 0.902 (95% confidence interval [CI]: 0.843, 0.961) in the test cohort. This clinical parameter combined DLR can also discriminate between low and heavy metastatic burden of axillary disease with AUC of 0.905 (95% CI: 0.814, 0.996) in the test cohort. Our study offers a noninvasive imaging biomarker to predict the metastatic extent of ALN for patients with early-stage breast cancer.

Breast cancer is frequently diagnosed using ultrasound. Here, the authors show that, in addition to ultrasound, shear wave elastography can be used to diagnose breast cancer and, in conjunction with deep learning and radiomics, can predict whether the disease has spread to axillary lymph nodes.

Quantitative shear wave elastography in primary invasive breast cancers, based on collagen-S100A4 pathology, indicates axillary lymph node metastasis

Background

The purpose of this study was to evaluate the value of quantitative shear wave elastography (SWE) in indicating the axillary lymph node metastasis (LNM) of invasive breast cancers (IBCs) and to investigate if S100A4 plays a key role in promoting metastasis and increasing stiffness in IBC.

Methods

The differences in SWE of 223 IBC patients were compared between the LNM+ and LNM- groups and the optimal cutoff values of SWE for diagnosing LNM were calculated. We searched the gene expression omnibus (GEO) to determine whether S100A4 was more highly expressed in IBCs that were LNM+ than in those that were LNM-. Sirius red and immunohistochemical staining were used to examine the collagen deposition and S100A4 expression of included tissue samples, and correlations of SWE and S100A4 expression with collagen deposition were analyzed.

Results

The optimal cutoff values for Emax (the maximum stiff value), Emean (the mean stiff value), and EmeanR (the ratio of Emean between mass and parenchyma) for diagnosing axillary LNM were 111.05 kPa, 79.80 kPa, and 6.89, respectively. GSE9893 exhibited more increased S100A4 expression in IBCs that were LNM+ than in those that were LNM-. Collagen volume fraction (CVF) and the average optical density of S100A4 (AOD_S100A4) in the LNM+ group were significantly higher than those in the LNM- group. Emax, Emean, EmeanR, and AOD_S100A4 were all positively correlated with CVF.

Conclusions

SWE in primary IBC could be useful for indicating axillary LNM. S100A4 may be a factor that regulates cancer-associated collagen deposition and metastasis; however, prospective molecular biological studies are needed.

Predictive value of comb-push ultrasound shear elastography for the differentiation of reactive and metastatic axillary lymph nodes: A preliminary investigation

Objectives

To evaluate the predictive performance of comb-push ultrasound shear elastography for the differentiation of reactive and metastatic axillary lymph nodes.

Methods

From June 2014 through September 2018, 114 female volunteers (mean age 58.1±13.3 years; range 28–88 years) with enlarged axillary lymph nodes identified by palpation or clinical imaging were prospectively enrolled in the study. Mean, standard deviation and maximum shear wave elastography parameters from 117 lymph nodes were obtained and compared to fine needle aspiration biopsy results. Mann-Whitney U test and ROC curve analysis were performed.

Results

The axillary lymph nodes were classified as reactive or metastatic based on the fine needle aspiration outcomes. A statistically significant difference between reactive and metastatic axillary lymph nodes was observed based on comb-push ultrasound shear elastography (CUSE) results (p<0.0001) from mean and maximum elasticity values. Mean elasticity showed the best separation with a ROC analysis resulting in 90.5% sensitivity, 94.4% specificity, 0.97 area under the curve, 95% positive predictive value, and 89.5% negative predictive value with a 30.2-kPa threshold.

Conclusions

CUSE provided a quantifiable parameter that can be used for the assessment of enlarged axillary lymph nodes to differentiate between reactive and metastatic processes.

Role of sonoelastography in assessment of axillary lymph nodes in breast cancer: a systematic review and meta-analysis

AIM

To evaluate the effectiveness of shear-wave elastography (SWE) and strain elastography (SE) for axillary lymph nodes (ALNs).

MATERIALS AND METHODS

PubMed, Embase, and Cochrane Library databases were searched until September 2018. Weighted mean difference was calculated for continuous variables. The accuracy of sonoelastography was assessed by calculating pooled sensitivity, specificity, area under the curve (AUC), positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR). All data were analysed using Stata 12.0.

RESULTS

Ten studies with 1,038 ALNs were included in the meta-analysis. Five studies evaluated the use of SE, and the other five evaluated the SWE. The SWE stiffness values of malignant ALNs were significantly higher than those of benign nodes. Both SE and SWE have relatively high specificity and sensitivity. The max stiffness in SWE showed the highest specificity (0.94; 95% confidence interval [CI], 0.81-0.98), PLR (12.1; 95% CI, 4-36.5), NLR (0.29; 95% CI, 0.12-0.69), AUC (0.94; 95% CI, 0.91-0.96), and DOR (42; 95% CI, 12-154); in contrast, the mean stiffness showed the highest sensitivity (0.80; 95% CI, 0.61-0.91).

CONCLUSION

Sonoelastography demonstrated high sensitivity and specificity for differentiating between malignant and benign ALNs. The max and mean stiffness on SWE appeared to exhibit the highest accuracy. Thus, SWE is an effective accompaniment to sentinel node biopsy, and is appropriate for preoperative assessment of ALNs in the post-Z0011 era.

A nomogram constructed using intraoperative ex vivo shear-wave elastography precisely predicts metastasis of sentinel lymph nodes in breast cancer

OBJECTIVE

To develop a nomogram and validate its use for the intraoperative evaluation of nodal metastasis using shear-wave elastography (SWE) elasticity values and nodal size METHODS: We constructed a nomogram to predict metastasis using ex vivo SWE values and ultrasound features of 228 axillary LNs from fifty-five patients. We validated its use in an independent cohort comprising 80 patients. In the validation cohort, a total of 217 sentinel LNs were included.

RESULTS

We developed the nomogram using the nodal size and elasticity values of the development cohort to predict LN metastasis; the area under the curve (AUC) was 0.856 (95% confidence interval (CI), 0.783-0.929). In the validation cohort, 15 (7%) LNs were metastatic, and 202 (93%) were non-metastatic. The mean stiffness (23.54 and 10.41 kPa, p = 0.005) and elasticity ratio (3.24 and 1.49, p = 0.028) were significantly higher in the metastatic LNs than those in the non-metastatic LNs. However, the mean size of the metastatic LNs was not significantly larger than that of the non-metastatic LNs (8.70 mm vs 7.20 mm, respectively; p = 0.123). The AUC was 0.791 (95% CI, 0.668-0.915) in the validation cohort, and the calibration plots of the nomogram showed good agreement.

CONCLUSIONS

We developed a well-validated nomogram to predict LN metastasis. This nomogram, mainly based on ex vivo SWE values, can help evaluate nodal metastasis during surgery.

KEY POINTS

• A nomogram was developed based on axillary LN size and ex vivo SWE values such as mean stiffness and elasticity ratio to easily predict axillary LN metastasis during breast cancer surgery. • The constructed nomogram presented high predictive performance of sentinel LN metastasis with an independent cohort. • This nomogram can reduce unnecessary intraoperative frozen section which increases the surgical time and costs in breast cancer patients.

[Breast cancer: News tools in imaging]

Breast cancer imaging is always improving for the last 20 years in spite of digitalization and computer development. News tools in mammography (Digital Breast Tomosynthesis, Contrast enhanced mammography), sonography (elastography, Automated echography), MRI (Diffusion, abbreviated MRI) and Nuclear medicine has the great potential to be the future of breats imaging. But true revolution will be to use the huge volume of "hidden" imaging data, by Intelligence Artificial process or Biological progress (in genomics, proteiomics) to purpose to our patient a personalized imaging.

Lymph Node Imaging in Patients with Primary Breast Cancer: Concurrent Diagnostic Tools

The detection of lymph node metastasis affects the management of patients with primary breast cancer significantly in terms of staging, treatment, and prognosis. The main goal for the radiologist is to determine and detect the presence of metastatic disease in nonpalpable axillary lymph nodes with a positive predictive value that is high enough to initially select patients for upfront axillary lymph node dissection. Features that are suggestive of axillary adenopathy may be seen with different imaging modalities, but ultrasound is the method of choice for evaluating axillary lymph nodes and for performing image-guided lymph node interventions. This review aims to provide a comprehensive overview of the available imaging modalities for lymph node assessment in patients diagnosed with primary breast cancer. IMPLICATIONS FOR PRACTICE: The detection of lymph node metastasis affects the management of patients with primary breast cancer. The main goal for the radiologist is to detect lymph node metastasis in patients to allow for the selection of patients who should undergo upfront axillary lymph node dissection. Features that are suggestive of axillary adenopathy may be seen with mammography, computed tomography, and magnetic resonance imaging, but ultrasonography is the imaging modality of choice for evaluating axillary lymph nodes. A normal axillary lymph node is characterized by a reniform shape, a maximal cortical thickness of 3 mm without focal bulging, smooth margins, and, depending on size, a discernable central fatty hilum.

Risk factors for axillary lymph node metastases in clinical stage T1-2N0M0 breast cancer patients

Axillary lymph node metastasis (ALNM) is commonly the earliest detectable clinical manifestation of breast cancer when distant metastasis emerges. This study aimed to explore the influencing factors of ALNM and develop models that can predict its occurrence preoperatively.Cases of sonographically visible clinical stage T1-2N0M0 breast cancers treated with breast and axillary surgery at West China Hospital were retrospectively reviewed. Univariate and multivariate logistic regression analyses were performed to evaluate associations between ALNM and variables. Decision tree analyses were performed to construct predictive models using the C5.0 packages.Of the 1671 tumors, 541 (32.9%) showed axillary lymph node positivity on final surgical histopathologic analysis. In multivariate logistic regression analysis, tumor size (P < .001), infiltration of subcutaneous adipose tissue (P < .001), infiltration of the interstitial adipose tissue (P = .031), and tumor quadrant locations (P < .001) were significantly correlated with ALNM. Furthermore, the accuracy in the decision tree model was 69.52%, and the false-negative rate (FNR) was 74.18%. By using the error-cost matrix algorithm, the FNR significantly decreased to 14.75%, particularly for nodes 5, 8, and 13 (FNR: 11.4%, 9.09%, and 14.29% in the training set and 18.1%,14.71%, and 20% in the test set, respectively).In summary, our study demonstrated that tumor lesion boundary, tumor size, and tumor quadrant locations were the most important factors affecting ALNM in cT1-2N0M0 stage breast cancer. The decision tree built using these variables reached a slightly higher FNR than sentinel lymph node dissection in predicting ALNM in some selected patients.

Evaluation of breast stiffness measured by ultrasound and breast density measured by MRI using a prone-supine deformation model

Background

This study evaluated breast tissue stiffness measured by ultrasound elastography and the percent breast density measured by magnetic resonance imaging to understand their relationship.

Methods

Magnetic resonance imaging and whole breast ultrasound were performed in 20 patients with suspicious lesions. Only the contralateral normal breasts were analyzed. Breast tissue stiffness was measured from the echogenic homogeneous fibroglandular tissues in the central breast area underneath the nipple. An automatic, computer algorithm-based, segmentation method was used to segment the whole breast and fibroglandular tissues on three dimensional magnetic resonanceimaging. A finite element model was applied to deform the prone magnetic resonance imaging to match the supine ultrasound images, by using the inversed gravity loaded transformation. After deformation, the tissue level used in ultrasound elastography measurement could be estimated on the deformed supine magnetic resonance imaging to measure the breast density in the corresponding tissue region.

Results

The mean breast tissue stiffness was 2.3 ± 0.8 m/s. The stiffness was not correlated with age (r = 0.29). Overall, there was no positive correlation between breast stiffness and breast volume (r = - 0.14), or the whole breast percent density (r = - 0.09). There was also no correlation between breast stiffness and the local percent density measured from the corresponding region (r = - 0.12).

Conclusions

The lack of correlation between breast stiffness measured by ultrasound and the whole breast or local percent density measured by magnetic resonance imaging suggests that breast stiffness is not solely related to the amount of fibroglandular tissue. Further studies are needed to investigate whether they are dependent or independent cancer risk factors.

Quantitative Evaluation of Shear Wave Elastography on Radiation-Induced Neck Fibrosis in Patients With Nasopharyngeal Carcinoma

ABSTRACT

The value of shear wave elastography (SWE) for quantitatively assessing neck fibrosis induced by radiotherapy (RT) in patients with nasopharyngeal carcinoma was evaluated over time. We prospectively observed 56 patients with nasopharyngeal carcinoma before and after therapeutic neck irradiation. The elasticity parameters including Emax and Emean were used to measure the stiffness of the bilateral sternocleidomastoid muscles. Twenty-seven patients completed a 1.5-year follow-up, with examinations beginning at 3, 6, 12, and 18 months after RT. Forty controls were recruited for reliability tests (along with the patients) and measurement comparisons. The consistency of SWE measurements with the Late Effects Normal Tissue Task Force-Subjective, Objective, Management and Analytic (LENT-SOMA) scale was tested. The intraclass correlation coefficients of elasticity indices for both patients and controls were higher than 0.75. The Emax and Emean of bilateral sternocleidomastoid muscles in the pre-RT patient group were comparable with those of the controls, and increased with increasing postirradiation duration (r = 0.514-0.555; P < 0.01). Significant increases in the Emax and Emean were observed 18 months after RT. The SWE correlated well with the LENT-SOMA score when assessing radiation-induced neck fibrosis 1.5 years after RT (r = 0.557-0.649; P < 0.01). Furthermore, both the Emax and Emean in the LENT-SOMA grade 0 subtype were higher 18 months after RT than before RT (P < 0.01). Because of its high reliability and good consistency with the LENT-SOMA score and better stiffness reflection at grade 0, SWE may be used to objectively and quantitatively evaluate the variation trend of radiation-induced neck fibrosis.

The Tumor Microenvironment as a Regulator of Endocrine Resistance in Breast Cancer

Estrogen receptor positive breast neoplasias represent over 70% of diagnosed breast cancers. Depending on the stage at which the tumor is detected, HER2 status and genomic risk, endocrine therapy is combined with either radio, chemo and/or targeted therapy. A growing amount of evidence supports the notion that components of the tumor microenvironment play specific roles in response to treatment and that strategies targeting these key interactions with tumor cells could pave the way to a new generation of therapies. In this review, we analyze the evidence suggesting different components of the tumor microenvironment play a role in hormone receptor positive breast cancer progression. In particular we focus on the immune system, carcinoma associated fibroblasts and the extracellular matrix. Further insight into the cross talk between these constituents of the microenvironment and the tumor cells may lead to therapies that eliminate disseminated metastatic cells early on, and thus reduce distant disease relapse which is the leading cause of death for patients who are diagnosed with this illness.

Pre-operative Conventional Ultrasound and Sonoelastography Evaluation for Predicting Axillary Lymph Node Metastasis in Patients with Malignant Breast Lesions

The objective of our study was to evaluate the association between the sonoelastography features of breast tumor and axillary lymph node metastasis (ALNM) in patients with breast cancer. In a cohort of 106 women with breast cancer, the conventional ultrasound features and elasticity parameters by elasticity imaging and Virtual Touch Tissue Imaging & Quantification (VTIQ) were retrospectively analyzed. Ultrasound and elastography findings were compared with pathologic axillary lymph node status. Receiver operating characteristic curve analysis was used to evaluate diagnostic performance. Pathologically, the overall incidence of ALNM was 39.6% (42/106) in the final analysis. ALNM was significantly more frequent in tumors with elasticity imaging scores >4.5, maximal shear wave velocity values (Smax) >6.42 m/s and mean shear wave velocity values (Smean) >5.66 m/s, respectively. The sensitivity, specificity and accuracy were 78.6%, 54.7% and 64.2% for elasticity imaging score; 85.7%, 54.7% and 67.0% for Smax; and 59.5%, 79.7% and 71.7% for Smean, respectively Elastography features, including elasticity imaging score and VTIQ, can be used to supplement conventional ultrasound to predict ALNM in patients with breast cancers.