Assessment of Ultrasound Features Predicting Axillary Nodal Metastasis in Breast Cancer: The Impact of Cortical Thickness

Construction and validation of a nomogram prediction model for axillary lymph node metastasis of cT1 invasive breast cancer

OBJECTIVE

Based on the ultrasonic characteristics of the breast mass and axillary lymph nodes as well as the clinicopathological information, a model was developed for predicting axillary lymph node metastasis in cT1 breast cancer, and relevant features associated with axillary lymph node metastasis were identified.

METHODS

Our retrospective study included 808 patients with cT1 invasive breast cancer treated at the Second Affiliated Hospital and the Cancer Hospital Affiliated with Harbin Medical University from February 2012 to August 2021 (250 cases in the positive axillary lymph node group and 558 cases in the negative axillary lymph node group). We allocated 564 cases to the training set and 244 cases to the verification set. R software was used to compare clinicopathological data and ultrasonic features between the two groups. Based on the results of multivariate logistic regression analysis, a nomogram prediction model was developed and verified for axillary lymph node metastasis of cT1 breast cancer.

RESULTS

Univariate and multivariate logistic regression analysis indicated that palpable lymph nodes ( P  = 0.003), tumor location ( P  = 0.010), marginal contour ( P  < 0.001), microcalcification ( P  = 0.010), surrounding tissue invasion ( P  = 0.046), ultrasonic detection of lymph nodes ( P  = 0.001), cortical thickness ( P  < 0.001) and E-cadherin ( P  < 0.001) are independently associated with axillary lymph node metastasis. Using these features, a nomogram was developed for axillary lymph node metastasis. The training set had an area under the curve of 0.869, while the validation set had an area under the curve of 0.820. Based on the calibration curve, the model predicted axillary lymph node metastases were in good agreement with reality ( P  > 0.05). Nomogram's net benefit was good based on decision curve analysis.

CONCLUSION

The nomogram developed in this study has a high negative predictive value for axillary lymph node metastasis in invasive cT1 breast c ancer. Patients with no axillary lymph node metastases can be accurately screened using this nomogram, potentially allowing this group of patients to avoid invasive surgery.

Dynamically changed HSP70 after reperfusion following cerebral infarction in human and rats: correlation with p38 MAPK

We aimed to clarify the correlation between dynamic change of blood HSP70 and the prognosis of thrombolysis in human and rats, so as to explain the neuroprotection and early warning role of HSP70 in cerebral ischemia-reperfusion. Forty-two patients with acute ischemic stroke were divided into two groups according to the time from onset to thrombolytic therapy: 0 h-3 h (27 patients) and 3-4.5 h group (15 patients). The level of HSP70 in serum before and after thrombolysis was detected by ELISA. Furthermore, a rat model was also used to mimic the ischemic stroke and reperfusion. Peripheral blood of rat samples was collected to detect the level of HSP70 using Elisa. Several signal proteins from MAPK signaling pathway including JNK, p38, ERK (p42/44) were detected at different time points by Western blot of brain tissue. Patients who underwent thrombolytic therapy within 0-3 h had the highest HSP70 level at 1 h after thrombolysis. The higher HSP70 after thrombolysis, the better the patient prognosis. NIHSS scores showed HSP70 was positively correlated with cerebral ischemia. The levels of ERK family (p42/44 MAPK) and p-JNK were decreased gradually along with the time suffering cerebral ischemia. P-ERK, JNK, p-p38 had dynamic changes with increased ischemic time in the middle cerebral artery occlusion model. Dynamic change of HSP70 level in blood may be a biological index that reflects the functional condition of cell survival for cerebral ischemia and estimating the prognostic conditions. Importantly, HSP70 levels in blood were positively correlated with the p38 MAPK pathway in brain tissue.

Male Breast Imaging Uncovers Lymphoma

Background

A 36-year-old man presented with a palpable mass in the right axillary tail for four months. He was referred to breast imaging for diagnostic work-up. He does not have a family history of breast cancer.

Aim

Breast imaging work-up for diagnosis of lymphoma is unusual and even more so in a male patient.

Case presentation

After Breast Mammography and targeted Ultrasound of the axillary tail and axilla, Magnetic Resonance Imaging (MRI) was performed and suggested lymphoproliferative disorder. Excisional biopsy was performed after the breast MRI with removal of right axillary tissue measuring 15.0 × 5.5 × 2.0 cm and containing multiple lymph nodes. Excisional biopsy revealed Classic Hodgkin lymphoma of nodular sclerosis type. Staging [18F]-FDG PET/CT revealed early stage of disease.

Conclusion

The presentation and diagnostic elements of Hodgkin Lymphoma are described in this case report emphasizing the significance of breast imaging in multiple populations.

Deep Learning Mechanism for Predicting the Axillary Lymph Node Metastasis in Patients with Primary Breast Cancer

The second largest cause of mortality worldwide is breast cancer, and it mostly occurs in women. Early diagnosis has improved further treatments and reduced the level of mortality. A unique deep learning algorithm is presented for predicting breast cancer in its early stages. This method utilizes numerous layers to retrieve significantly greater amounts of information from the source inputs. It could perform automatic quantitative evaluation of complicated image properties in the medical field and give greater precision and reliability during the diagnosis. The dataset of axillary lymph nodes from the breast cancer patients was collected from Erasmus Medical Center. A total of 1050 images were studied from the 850 patients during the years 2018 to 2021. For the independent test, data samples were collected for 100 images from 95 patients at national cancer institute. The existence of axillary lymph nodes was confirmed by pathologic examination. The feed forward, radial basis function, and Kohonen self-organizing are the artificial neural networks (ANNs) which are used to train 84% of the Erasmus Medical Center dataset and test the remaining 16% of the independent dataset. The proposed model performance was determined in terms of accuracy (Ac), sensitivity (Sn), specificity (Sf), and the outcome of the receiver operating curve (Roc), which was compared to the other four radiologists' mechanism. The result of the study shows that the proposed mechanism achieves 95% sensitivity, 96% specificity, and 98% accuracy, which is higher than the radiologists' models (90% sensitivity, 92% specificity, and 94% accuracy). Deep learning algorithms could accurately predict the clinical negativity of axillary lymph node metastases by utilizing images of initial breast cancer patients. This method provides an earlier diagnostic technique for axillary lymph node metastases in patients with medically negative changes in axillary lymph nodes.

Ultrasound Features to Differentiate COVID-19 Vaccine-Induced Benign Adenopathy from Breast Cancer Related Malignant Adenopathy

Rationale and Objective

To identify nodal features used to distinguish coronavirus disease 2019 (COVID-19) vaccine-Induced benign reactive adenopathy from malignant adenopathy.

Materials and Methods

This IRB-approved, single-institution, retrospective study compared features of 77 consecutive patients with benign adenopathy secondary to a messenger RNA COVID-19 vaccine with 76 patients with biopsy-proven malignant adenopathy from breast cancer. Patient demographics and nodal features were compared between the two groups using univariate and multivariate logistic regression models. A receiver operating characteristic analysis with the maximum value of Youden's index was performed for the cutoff value of cortical thickness for predicting nodal status.

Results

The mean cortical thickness was 5.1 mm ± 2.8 mm among benign nodes and 8.9 mm ± 4.5 mm among malignant nodes (p < 0.001). A cortical thickness ≥3.0 mm had a sensitivity of 100% and a specificity of 21% (area under the curve [AUC] = 0.60, 95% confidence interval [CI]: 0.52-0.68). When the cutoff for cortical thickness was increased to ≥5.4 mm, the sensitivity decreased to 74%, while the specificity increased to 69% (AUC = 0.77, 95% CI: 0.70-0.84).Cortical thickness correlated with nodal morphology type (r² = 0.57). An axillary node with generalized lobulated cortical thickening had a 7.5 odds ratio and a node with focal cortical lobulation had a 123.0 odds ratio compared to one with diffuse, uniform cortical thickening only (p < 0.001).

Conclusion

Cortical thickness and morphology are predictive of malignancy. Cortical thickness cutoff of ≥5.4 mm demonstrates higher specificity and improved accuracy for detecting malignant adenopathy than a cutoff of ≥3.0 mm.

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.

Selective Extirpation of Tattooed Lymph Node in Combination with Sentinel Lymph Node Biopsy in the Management of Node-Positive Breast Cancer Patients after Neoadjuvant Systemic Therapy

Introduction

Axillary dissection has little diagnostic and therapeutic benefit in node-positive breast cancer patients in whom axillary disease has been completely eradicated after neoadjuvant chemotherapy (ypN0). We sought to assess the efficacy of an algorithm used for the identification of the ypN0 patient consisting of intraoperative evaluation of sentinel and tattooed (initially positive) lymph nodes.

Methods

Included were T1 and T2 breast cancer patients with 1-3 positive axillary lymph nodes marked with carbon who were referred for neoadjuvant chemotherapy followed by a surgery. Axillary dissection was performed only in the patients with residual axillary disease after neoadjuvant chemotherapy on ultrasound or with metastases described in the sentinel or tattooed lymph nodes either intraoperatively or in the final histology.

Results

Out of 62 initially included node-positive patients, 15 (24%) were spared axillary dissection. The detection rate of tattooed lymph nodes after neoadjuvant chemotherapy was 81%. The ypN0 patients were identified with 91% sensitivity and 38% specificity using ultrasound and intraoperative assessment of both sentinel and tattooed lymph node according to the final histology.

Discussion/Conclusion

Lymph node marking with carbon dye is a useful and cost-effective method, which can be successfully implemented in order to reduce the number of patients undergoing axillary dissection. Low specificity of the presented algorithm was caused mostly by the overestimation of residual axillary disease on ultrasound.

Clinical and ultrasound features correlated with a heavy axillary nodal tumor burden in colon cancer

Aim: This study aimed to investigate the correlation between the pathologic and ultrasound (US) characteristics of colon cancer and the heavy axillary nodal burden. Methods: In total, 631 patients diagnosed with invasive colon cancer were recruited with ethical ratification. Results: The unitary pathologic features correlated with heavy axillary lymph nodal burden included the age of patient (p = 0.035), tumor size (p = 0.001), lymph node metastasis (p = 0.001), lymphovascular invasion (p = 0.020) and pathology type (p = 0.012). The independent US characteristics correlated with heavy axillary nodal burden included posterior acoustic enhancement (p = 0.006). Heavy axillary nodal burden was correlated with tumor size, lymph node metastasis, lymphovascular invasion and pathology type. Conclusion: Tumor size, lymph node metastasis and posterior acoustic can be used to predict the axillary lymph node tumor burden.

Axillary Nodal Metastases in Invasive Lobular Carcinoma Versus Invasive Ductal Carcinoma: Comparison of Node Detection and Morphology by Ultrasound

Background: Invasive lobular carcinoma is more subtle on imaging compared with invasive ductal carcinoma; nodal metastases may also differ on imaging between these. Objective: To determine whether invasive lobular carcinoma and invasive ductal carcinoma differ in the detection rate by ultrasound (US) of metastatic axillary nodes and in metastatic nodes' US characteristics. Methods: This retrospective study included 695 women (median age 53 years) with breast cancer in a total of 723 breasts (76 lobular, 586 ductal, 61 mixed), with biopsy-proven axillary nodal metastases and who underwent pretreatment US. A single breast radiologist reviewed US images in patients with suspicious nodes on US and classified node number, size, and morphology. Morphologic assessment used a previously described classification based on the relationship between node cortex and hilum. Nodal findings were compared between lobular and ductal carcinoma. A second radiologist independently classified node morphology in 241 cancers to assess interreader agreement. Results: A total of 99 metastatic axillary nodes (15 lobular, 66 ductal, 18 mixed) were not visualized on US and were diagnosed by surgical biopsy. The remaining 624 metastatic nodes (61 lobular, 520 ductal, 43 mixed) were visualized on US and diagnosed by US-guided FNA. Thus, US detected the metastatic nodes in 80.3% for lobular carcinoma versus 88.7% for ductal carcinoma (p=.04). Among metastatic nodes detected by US, retrospective review identified ≥3 abnormal nodes in 50.8% of lobular carcinoma versus 69.2% of ductal carcinoma (p=.003); node size was ≤2.0 cm in 65.6% for lobular carcinoma versus 47.3% for ductal carcinoma (p=.03); morphology was type III/IV (diffuse cortical thickening without hilar mass effect) rather than type V/VI (marked cortical thickening with hilar mass effect) in 68.9% for lobular carcinoma versus 28.8% for ductal carcinoma (p<.001). Interreader agreement assessment for morphology exhibited kappa coefficient of 0.63 (95% CI, 0.54-0.73). Conclusion: US detects a lower percentage of nodal metastases in lobular than ductal carcinoma. Nodal metastases in lobular carcinoma more commonly show diffuse cortical thickening and with less hilar mass effect. Clinical Impact: A lower threshold may be warranted to recommend biopsy of suspicious axillary nodes detected on US in patients with lobular carcinoma.

Is pre-operative axillary ultrasound alone sufficient to determine need for axillary dissection in early breast cancer patients?

Pre-operative status of axillary lymph node (ALN) in early breast cancer is usually initially assessed by pre-operative ultrasound, followed by ultrasound-guided needle biopsy (UNB) confirmation. Patients with positive nodal status will undergo axillary lymph node dissection (ALND), while those with negative nodal status will have sentinel lymph node biopsy. ALND is associated with higher morbidity than Sentinel lymph node biopsy. The objective of this study is to determine if axillary ultrasound alone without UNB is predictive enough to assign patients to ALND and to identify ultrasound features that are significantly associated with pathologically positive ALN.383 newly diagnosed primary breast cancer patients between 2012 and 2014, and who had undergone pre-operative axillary ultrasound in University Malaya Medical Centre with a complete histopathology report of the axillary surgery were retrospectively reviewed. ALN was considered positive if it had any of these features: cortical thickening > 3 mm, loss of fatty hilum, hypoechoic solid node, mass-like appearance, round shape and lymph node size > 5 mm. Post-operative histopathological reports were then analyzed for nodal involvement.The overall sensitivity, specificity, and accuracy of pre-operative axillary ultrasound in detecting diseased nodes were 45.5%, 80.7%, and 60.3% respectively. The positive (PPV) and negative predictive values were 76.5% and 51.8%. Round shape, loss of fatty hilum and mass-like appearance had the highest PPVs of 87%, 83% and 81.6% respectively and significant odds ratios (ORs) of 5.22 (95% confidence interval [CI]: 1.52 - 17.86), ORs of 4.77 (95% CI: 2.62 - 8.70) and ORs of 4.26 (95% CI: 2.37 - 7.67) respectively (P-value < .05). Cortical thickness of > 3 mm was identified to have low PPV at 69.1%, ORs of 1.71 (95% CI: 0.86 - 3.41, P = .126).There are features on axillary ultrasound that confer high PPV for axillary involvement i.e. round shape, loss of fatty hilum, and mass-like appearance. In a low resource setting, these features may benefit from ALND without further pre-operative biopsies. However, pre-operative UNB for features with low PPV that is, cortical thickness > 3 mm should be considered to obviate the unnecessary morbidity associated with ALND.

Sentinel lymph node biopsy with one-step nucleic acid assay relegates the need for preoperative ultrasound-guided biopsy staging of the axilla in patients with early stage breast cancer

Avoiding axillary node clearance in patients with early stage breast cancer and low-burden node-positive axillary disease is an emerging practice. Informing the decision to adopt axillary conservation is examined by comparing routine preoperative axillary staging using ultrasound (AUS) ± AUS biopsy (AUSB) with intraoperative staging using sentinel lymph node biopsy (SLNB) and a one-step nucleic acid cytokeratin-19 amplification assay (OSNA). A single-centre, retrospective cohort study of 1,315 consecutive new diagnoses of breast cancer in 1,306 patients was undertaken in the present study. An AUS ± AUSB was performed on all patients as part of their initial assessment. Patients who had a normal ultrasound (AUS-) or negative biopsy (AUSB-) followed by SLNB with OSNA ± axillary lymph node dissection (ALND), and those with a positive AUSB (AUSB+), were assessed. Tests for association were determined using a χ² and Fisher's Exact test. A total of 266 (20.4%) patients with cT1-3 cN0 staging received 271 AUSBs. Of these, 205 biopsies were positive and 66 were negative. The 684 patients with an AUS-/AUSB-assessment proceeded to SLNB with OSNA. AUS sensitivity and negative predictive value (NPV) were 0.53 [0.44-0.62; 95% confidence interval (CI)] and 0.58 (0.53-0.64, 95% CI), respectively. Using a total tumour load cut-off of 15,000 copies/µl to predict ≥2 macro-metastases, the sensitivity and NPV for OSNA were 0.82 (0.71-0.92, 95% CI) and 0.98 (0.97-0.99, 95% CI) (OSNA vs. AUS P<0.0001). Of the AUSB+ patients, 51% had ≤2 positive nodes following ALND and were potentially over-treated. Where available, SLNB with OSNA should replace AUSB for axillary assessment in cT1-2 cN0 patients with ≤2 indeterminate nodes seen on AUS.

Survival in Cytologically Proven Node-Positive Breast Cancer Patients with Nodal Pathological Complete Response after Neoadjuvant Chemotherapy

BACKGROUND

It is unknown whether patients with cytologically proven axillary node-positive breast cancer who achieve axillary pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) have comparable prognosis to patients with axillary pathological node-negative disease (pN-) without NAC.

METHODS

We retrospectively reviewed the data of patients with cytologically proven axillary node-positive disease who received NAC and those with axillary pN- without NAC for control between January 2007 and December 2012. We compared outcomes according to response in the axilla to NAC and between patients with axillary pCR and matched pairs with axillary pN- without NAC using propensity scores.

RESULTS

We included 596 patients with node-positive breast cancer who received NAC. The median follow-up period was 64 months. Patients with axillary pCR showed significantly better distant disease-free survival (DDFS) and overall survival (OS) than patients with residual axillary disease (both p < 0.01). There was no significant difference in DDFS and OS between patients with axillary pCR and matched pairs with axillary pN- without NAC.

CONCLUSION

Axillary pCR was associated with improved prognosis. Patients with axillary pCR and matched pairs with axillary pN- without NAC had comparable outcomes. This information will be useful when considering the intensity of follow-up and adjuvant therapy.

Establishment of Simple Nomograms for Predicting Axillary Lymph Node Involvement in Early Breast Cancer

Purpose

Axillary lymph node (ALN) involvement is an important prognostic factor of early invasive breast cancer. The objective of this study was to establish simple nomograms for predicting ALN involvement based on ultrasound (US) characteristics and evaluate the predictive value of US in the detection of ALN involvement.

Patients and Methods

A total of 1328 patients with cT1-2N0 breast cancer by physical exam were retrospectively analyzed. Univariate analysis was used for the comparison of variables, and multivariate analysis was performed by binary logistic regression analysis. The R software was used to establish simple nomograms based on the US characteristics alone. The receiver operating characteristic (ROC) curves of the prediction model and the verification group were drawn, and the area under the curve (AUC) was calculated to evaluate the discrimination of the prediction model. A calibration curve was plotted to assess the nomogram predictions vs the actual observations of the ALN metastasis rate and axillary tumor burden rate.

Results

The ALN metastasis rates of the training group and the validation group were 35.1% and 34.1%, respectively. Multivariate analysis showed that molecular subtype, lymphovascular invasion, mass descriptors (size, margin, microcalcification and blood flow signal) and LN descriptors (shape, cortical thickness and long-to-short ratio) were independent impact factors in early breast cancer. The AUC of ALN metastasis rate of prediction model based on US features was 0.802, the AUC of high tumor burden rate was 0.873, and the AUC of external validation group was 0.731 and 0.802, respectively. The calibration curve of the nomogram showed that the nomogram predictions are consistent with the actual metastasis rate and the high tumor burden rate. The results showed that preoperative US had a sensitivity of 59.4% and a specificity of 88.9% for predicting the ALN metastasis rate.

Conclusion

The successfully established nomograms based on US characteristics to predict ALN metastasis rate and high axillary tumor burden rate in early breast cancer can achieve individual prediction. Compared with other nomogram predictions, it is more intuitive, and can help clinical decision-making; thus, it should be promoted. However, at this time US features alone are insufficient to replace sentinel lymph node biopsy.

Lymph Node Metastasis Prediction from Primary Breast Cancer US Images Using Deep Learning

Background Deep learning (DL) algorithms are gaining extensive attention for their excellent performance in image recognition tasks. DL models can automatically make a quantitative assessment of complex medical image characteristics and achieve increased accuracy in diagnosis with higher efficiency. Purpose To determine the feasibility of using a DL approach to predict clinically negative axillary lymph node metastasis from US images in patients with primary breast cancer. Materials and Methods A data set of US images in patients with primary breast cancer with clinically negative axillary lymph nodes from Tongji Hospital (974 imaging studies from 2016 to 2018, 756 patients) and an independent test set from Hubei Cancer Hospital (81 imaging studies from 2018 to 2019, 78 patients) were collected. Axillary lymph node status was confirmed with pathologic examination. Three different convolutional neural networks (CNNs) of Inception V3, Inception-ResNet V2, and ResNet-101 architectures were trained on 90% of the Tongji Hospital data set and tested on the remaining 10%, as well as on the independent test set. The performance of the models was compared with that of five radiologists. The models' performance was analyzed in terms of accuracy, sensitivity, specificity, receiver operating characteristic curves, areas under the receiver operating characteristic curve (AUCs), and heat maps. Results The best-performing CNN model, Inception V3, achieved an AUC of 0.89 (95% confidence interval [CI]: 0.83, 0.95) in the prediction of the final clinical diagnosis of axillary lymph node metastasis in the independent test set. The model achieved 85% sensitivity (35 of 41 images; 95% CI: 70%, 94%) and 73% specificity (29 of 40 images; 95% CI: 56%, 85%), and the radiologists achieved 73% sensitivity (30 of 41 images; 95% CI: 57%, 85%; P = .17) and 63% specificity (25 of 40 images; 95% CI: 46%, 77%; P = .34). Conclusion Using US images from patients with primary breast cancer, deep learning models can effectively predict clinically negative axillary lymph node metastasis. Artificial intelligence may provide an early diagnostic strategy for lymph node metastasis in patients with breast cancer with clinically negative lymph nodes. Published under a CC BY 4.0 license. Online supplemental material is available for this article. See also the editorial by Bae in this issue.

[Contrast-enhanced ultrasound in the diagnostic workup of lymph nodes]

CLINICAL/METHODICAL ISSUE

Daily tasks in sonographic diagnostics include detection and characterization of peripheral and abdominal lymph nodes.

STANDARD RADIOLOGICAL METHODS

In addition to the B‑mode methods, color-coded Doppler sonography (CCDS) plays an important role in the evaluation of lymph nodes.

METHODICAL INNOVATIONS

Contrast-enhanced ultrasound (CEUS) has become a standard procedure in vascular and organ diagnostics. Tissue perfusion can be recorded visually and retrospectively in real time using time-dependent intensity analysis. The contrast agent dosage depends primarily on the location of the lymph nodes and the type and frequency of the transducer. Vascular and tumor cell density, intranodal pressure due to increased vascular permeability and preservation or destruction of the capsule must be taken into account when interpreting the findings.

PERFORMANCE

The indication for CEUS results from the B‑mode and CCDS findings and plays an important role especially in the verification of vitality before and after therapy. Uneven or apparently non-perfused areas allow a targeted puncture of vital tumor tissue.

ACHIEVEMENTS

Especially in abdominal lymph nodes, CEUS has a high diagnostic reliability. It is not always possible to differentiate between inflamed lymph nodes and lymph nodes altered by lymphoma filtration.

PRACTICAL RECOMMENDATIONS

CEUS helps to better assess the dignity of lymph nodes by visualizing their micro- and macrovascularization. After frustrated puncture, vital areas can be specifically biopsied. CEUS is particularly valuable in assessing the success of therapy.

Clinicopathologic and Ultrasound Variables Associated With a Heavy Axillary Nodal Tumor Burden in Invasive Breast Carcinoma

OBJECTIVES

To identify clinicopathologic and ultrasound (US) variables that were associated with a heavy nodal tumor burden, which was defined as 3 or more lymph nodes involved with metastasis to the axilla after invasive breast carcinoma.

METHODS

With ethical approval, 621 patients with a pathologic diagnosis of invasive breast carcinoma were retrospectively analyzed for clinical, pathologic, and US data. Pathologic findings were ascertained by the final paraffin pathologic analysis. Ultrasound characteristics were evaluated on the basis of the American College of Radiology's Breast Imaging Reporting and Data System (BI-RADS). Univariate and multivariate logistic regression analyses were used to assess the clinicopathologic and US variables that were associated with a heavy nodal tumor burden at the axilla.

RESULTS

There were 107 cases (17.2%) of invasive breast carcinoma with a heavy tumor burden at the axilla. The independent clinicopathologic variables for a heavy tumor burden at the axilla included a tumor size of 2 to 5 cm (odds ratio [OR], 1.86; P = .036), the presence of lymphovascular invasion (OR, 23.52; P < .001), the presence of papillary invasion (OR, 2.93; P = .043), and a non-triple-negative subtype (OR, 2.34; P = .04). The independent US features of breast tumors that were associated with a heavy tumor burden at the axilla included BI-RADS category 5 (OR, 5.50; P = .024) and a posterior acoustic shadow (OR, 1.94; P = .024).

CONCLUSIONS

A large tumor size, lymphovascular invasion, papillary invasion, and a non-triple-negative subtype on the pathologic analysis as well as BI-RADS category 5 and a posterior acoustic shadow on a US assessment were associated with a heavy nodal tumor burden at the axilla. These US characteristics of the primary breast carcinoma might provide additional information to axillary US for the prediction of axillary nodal tumor loads.

Correlation of Ultrasound Findings and Fine-Needle Aspiration Cytology for the Diagnosis of Axillary Lymph Node Metastasis in Patients with Breast Carcinoma

OBJECTIVE

Ultrasound-guided fine-needle aspiration cytology (USG-FNAC) is used for the detection of axillary lymph node (ALN) metastasis in patients with breast carcinoma (BC). US findings have a good diagnostic accuracy with high sensitivity and specificity. The aim of this study is to correlate the detection of ALN metastases on US with FNAC in BC patients.

STUDY DESIGN

In 75 BC patients, over a period of 9 months (January to September 2017), the size, cortical thickness (CT), presence or absence of hilar fat, and length/width ratio of ALN on US were reviewed and correlated with FNAC findings.

RESULTS

The age range was 29-78 (mean 52) years. There were 38 patients with a single ALN and 37 with multiple ALNs. ALNs with a maximum length of > 2.5 cm were malignant in 100% of cases while those ≥1.5 cm were malignant in 80.4%. ALNs with a CT of > 3 mm had metastasis in 78.1% cases. ALNs with absent hilar fat showed tumour in 87.5% cases. A length/width ratio of < 2 showed a metastatic tumour in 66.7% of aspirates.

CONCLUSION

An association was seen between metastatic carcinoma on FNAC and axillary US features of a maximum length of ≥1.5 cm, the absence of hilar fat, and a CT of > 3 mm (p < 0.05).

Association between US features of primary tumor and axillary lymph node metastasis in patients with clinical T1-T2N0 breast cancer

Background Most patients with early-stage breast cancer have clinically negative lymph nodes (LNs). However, 15-20% of patients have axillary nodal metastasis based on the sentinel LN biopsy. Purpose To assess whether ultrasound (US) features of a primary tumor are associated with axillary LN metastasis in patients with clinical T1-T2N0 breast cancer. Material and Methods This retrospective study included 138 consecutive patients (median age = 51 years; age range = 27-78 years) who underwent breast surgery with axillary LN evaluation for clinically node-negative T1-T2 breast cancer. Three radiologists blinded to the axillary surgery results independently reviewed the US images. Tumor distance from the skin and distance from the nipple were determined based on the US report. Association between US features of a breast tumor and axillary LN metastasis was assessed using a multivariate logistic regression model after controlling for clinicopathologic variables. Results Of the 138 patients, 28 (20.3%) had nodal metastasis. At univariate analysis, tumor distance from the skin ( P = 0.019), tumor size on US ( P = 0.023), calcifications ( P = 0.036), architectural distortion ( P = 0.001), and lymphovascular invasion ( P = 0.049) were associated with axillary LN metastasis. At multivariate analysis, shorter skin-to-tumor distance (odds ratio [OR] = 4.15; 95% confidence interval [CI] = 1.01-16.19; P = 0.040) and masses with associated architectural distortion (OR = 3.80; 95% CI = 1.57-9.19; P = 0.003) were independent predictors of axillary LN metastasis. Conclusion US features of breast cancer can be promising factors associated with axillary LN metastasis in patients with clinically node-negative early-stage breast cancer.

Preoperative prediction nomogram based on primary tumor miRNAs signature and clinical-related features for axillary lymph node metastasis in early-stage invasive breast cancer

More than half patients who undergo axillary lymph node (ALN) surgery are ALN negative in early-stage invasive breast cancer (EIBC). Thus, to avoid excessive treatment, we aim to establish and validate a novel nomogram model for the preoperative diagnosis of ALN status in patients with EIBC. In total, 864 patients with EIBC from two independent centers were enrolled in our study. For the discovery set, miRNAs expression profiling with functional roles in ALN metastasis was discovered by microarray analysis and validated by quantitative polymerase chain reaction (PCR). For the training and validation cohorts, we used PCR to quantify miRNAs expression in a model development cohort and assessed miRNAs signature in an internal validation cohort and external independent validation cohort. Multivariable logistic regression analyses were used to establish a nomogram model for the likelihood of ALN metastasis from miRNAs signature and clinical variables. A signature of nine-miRNA was significantly associated with ALN status. The predictive ability of our nomogram that included miRNAs signature and clinical-related variables (age, tumor size, tumor location and axillary ultrasound-reported ALN status) was significantly greater than a model that only considered clinical-related factors (concordance index: 0.856, 0.796) and also performed well in the two validation cohorts (concordance index: 0.841, 0.747). Our nomogram is a reliable prediction method that can be conveniently used to preoperatively predict ALN status in patients with EIBC. Therefore, after further confirmation in prospective and multicenter clinical trial, omission of axillary surgery may be feasible for some patients with EIBC in the future.