Vaccine-associated axillary lymphadenopathy with a focus on COVID-19 vaccines

Axillary lymphadenopathy (LA) after COVID-19 vaccination is now known to be a common side effect. In these cases, malignancy cannot always be excluded on the basis of morphological imaging criteria.Narrative review for decision-making regarding control and follow-up intervals for axillary LA according to currently published research. This article provides a practical overview of the management of vaccine-associated LA using image examples and a flowchart and provides recommendations for follow-up intervals. A particular focus is on patients presenting for diagnostic breast imaging. The diagnostic criteria for pathological lymph nodes (LN) are explained.Axillary LA is a common adverse effect after COVID-19 vaccination (0.3-53%). The average duration of LA is more than 100 days. LA is also known to occur after other vaccinations, such as the seasonal influenza vaccine. Systematic studies on this topic are missing. Other causes of LA after vaccination (infections, autoimmune diseases, malignancies) should be considered for the differential diagnosis. If the LA persists for more than 3 months after COVID-19 vaccination, a primarily sonographic follow-up examination is recommended after another 3 months. A minimally invasive biopsy of the LA is recommended if a clinically suspicious LN persists or progresses. In the case of histologically confirmed breast cancer, a core biopsy without a follow-up interval is recommended regardless of the vaccination, as treatment appropriate to the stage should not be influenced by follow-up intervals. For follow-up after breast cancer, the procedure depends on the duration of the LA and the woman's individual risk of recurrence.Vaccination history should be well documented and taken into account when evaluating suspicious LN. Biopsy of abnormal, persistent, or progressive LNs is recommended. Preoperative staging of breast cancer should not be delayed by follow-up. The risk of false-positive findings is accepted, and the suspicious LNs are histologically examined in a minimally invasive procedure. · The vaccination history must be documented (vaccine, date, place of application).. · If axillary LA persists for more than 3 months after vaccination, a sonographic follow-up examination is recommended after 3 months.. · Enlarged LNs that are persistent, progressive in size, or are suspicious on control sonography should be biopsied.. · Suspicious LNs should be clarified before starting oncological therapy, irrespective of the vaccination status, according to the guidelines and without delaying therapy.. · Wilpert C, Wenkel E, Baltzer PA et al. Vaccine-associated axillary lymphadenopathy with a focus on COVID-19 vaccines. Fortschr Röntgenstr 2024; DOI 10.1055/a-2328-7536.

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

OBJECTIVE

To develop a nomogram for predicting axillary lymph node metastasis (ALNM) in patients with invasive breast cancer.

METHODS

We included 307 patients with clinicopathologically confirmed invasive breast cancer. The cohort was divided into a training group (n=215) and a validation group (n=92). Ultrasound images were used to extract radiomics features. The least absolute shrinkage and selection operator (LASSO) algorithm helped select pertinent features, from which Radiomics Scores (Radscores) were calculated using the LASSO regression equation. We developed three logistic regression models based on Radscores and 2D image features, and assessed the models' performance in the validation group. A nomogram was created from the best-performing model.

RESULTS

In the training set, the area under the curve (AUC) for the Radscore model, 2D feature model, and combined model were 0.76, 0.85, and 0.88, respectively. In the validation set, the AUCs were 0.71, 0.78, and 0.83, respectively. The combined model demonstrated good calibration and promising clinical utility.

CONCLUSION

Our ultrasound-based radiomics nomogram can accurately and non-invasively predict ALNM in breast cancer, suggesting potential clinical applications to optimize surgical and medical strategies.

ACR Appropriateness Criteria® Imaging of Invasive Breast Cancer

As the proportion of women diagnosed with invasive breast cancer increases, the role of imaging for staging and surveillance purposes should be determined based on evidence-based guidelines. It is important to understand the indications for extent of disease evaluation and staging, as unnecessary imaging can delay care and even result in adverse outcomes. In asymptomatic patients that received treatment for curative intent, there is no role for imaging to screen for distant recurrence. Routine surveillance with an annual 2-D mammogram and/or tomosynthesis is recommended to detect an in-breast recurrence or a new primary breast cancer in women with a history of breast cancer, and MRI is increasingly used as an additional screening tool in this population, especially in women with dense breasts. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

Development of an Intratumoral and Peritumoral Radiomics Nomogram Using Digital Breast Tomosynthesis for Preoperative Assessment of Lymphovascular Invasion in Invasive Breast Cancer

RATIONALE AND OBJECTIVES

This study aimed to create a nomogram model that combines clinical factors with radiomics analysis of both intra- and peritumoral regions extracted from preoperative digital breast tomosynthesis (DBT) images, in order to develop a reliable method for predicting the lymphovascular invasion (LVI) status in invasive breast cancer (IBC) patients.

MATERIALS AND METHODS

A total of 178 patients were randomly split into a training dataset (N = 124) and a validation dataset (N = 54). Comprehensive clinical data, encompassing DBT features, were gathered for all cases. Radiomics features were extracted and selected from intra- and peritumoral region to establish radiomics signature (Radscore). To construct the clinical model and nomogram model, univariate and multivariate logistic regression analyses were utilized to identify independent risk factors. To assess and validate these models, various analytical methods were employed, including receiver operating characteristic (ROC) curve analysis, calibration curve analysis, decision curve analysis (DCA), net reclassification improvement (NRI), and integrated discriminatory improvement (IDI).

RESULTS

The clinical model is constructed based on two independent risk factors: tumor margin and the DBT-reported lymph node metastasis (DBT_reported_LNM). Incorporating Radscore_Combine (utilizing both intra- and peritumoral radiomics features), tumor margin, and DBT_reported_LNM into the nomogram achieved a reliable predictive performance, with area under the curve (AUC) values of 0.906 and 0.905 in both datasets, respectively. The significant improvement demonstrated by the NRI and IDI indicates that the Radscore_Combine could be a valuable biomarker for effectively predicting the status of LVI.

CONCLUSION

The nomogram demonstrated a reliable ability to predict LVI in IBC patients.

Evaluation of traditional targeted axillary dissection eligibility criteria for node-positive breast cancer after neoadjuvant chemotherapy in a prospective multicenter registry

INTRODUCTION

Targeted axillary dissection (TAD) is performed after neoadjuvant systemic therapy (NST) to decrease the rate of non-therapeutic axillary dissection (ALND) for patients with node-positive breast cancer. In order to ensure the oncologic safety of TAD, eligibility criteria resulting in a low false negative rate (FNR) have been proposed. The purpose of this study was to evaluate the utility of the traditional criteria.

METHODS

Data was collected from a prospective multicenter registry. In order to ascertain FNRs, pathologic findings in the sentinel lymph nodes (LN)s, malignant clipped LN, and axillary contents were determined. The FNRs within TAD eligibility criterion groups were compared.

RESULTS

A total of 110 patients underwent TAD and ALND, and were therefore eligible for analysis. TAD retained a low FNR in advanced clinical T-N stage compared with earlier disease (T stage: 95% CI 0.00-11.93, p = 0.42; N stage: 95% CI 0.00-8.76, p = 0.31). Presentation with ≥4 abnormal LNs on axillary ultrasound did not predict a high TAD FNR (95% CI 0.00-5.37, p = 0.16). No significant differences were noted in TAD FNR when single was compared with dual tracer (blue dye vs dual tracer 95% CI 0.72-52.49, p = 0.13; radiotracer vs dual tracer 0.04-20.11, p = 0.51). Excision of the clipped LN and only one SLN was as accurate as excision of the clipped LN and ≥2 SLNs (95% CI 0.00-10.61, p = 0.38).

CONCLUSIONS

TAD retained a low FNR among patients traditionally considered ineligible for this technique. However, excision of the clipped LN and at least one SLN remained essential to a low FNR.

Prognostic effect of nodal status before and after neoadjuvant chemotherapy in breast cancer: a Dutch population-based study

PURPOSE

In breast cancer, neoadjuvant chemotherapy (NAC) can downstage the nodal status, and can even result in a pathological complete response, which is associated with improved prognosis. This study aimed to determine the prognostic effect of nodal status before and after NAC.

METHODS

Women with breast cancer treated with NAC were selected from the Netherlands Cancer Registry if diagnosed between 2005 and 2019, and classified based on nodal status before NAC: node-negative (cN0), or node-positive based on fine needle aspiration cytology or core needle biopsy (cN+). Subgroups were based on nodal status after NAC: absence (ypN0) or presence (ypN+) of nodal disease. Five-year overall survival (OS) was assessed with Kaplan-Meier survival analyses, also per breast cancer molecular subtype. To adjust for potential confounders, multivariable analyses were performed.

RESULTS

A total of 6,580 patients were included in the cN0 group, and 11,878 in the cN+ group. The 5-year OS of the cN0ypN0-subgroup was statistically significant better than that of the cN+ypN0-subgroup (94.4% versus 90.1%, p < 0.0001). In cN0 as well as cN+ disease, ypN+ had a statistically significant worse 5-year OS compared to ypN0. For hormone receptor (HR)+ human epidermal growth factor receptor 2 (HER2)-, HR+ HER2+, HR-HER2+, and triple negative disease, respectively, 5-year OS in the cN0ypN+-subgroup was 89.7%, 90.4%, 73.7%, and 53.6%, and in the cN+ypN+-subgroup 84.7%, 83.2%, 61.4%, and 48.8%. In multivariable analyses, cN+ and ypN+ disease were both associated with worse OS.

CONCLUSION

This study suggests that both cN-status and ypN-status, and molecular subtype should be considered to further improve prognostication.

Clinical study on the prediction of ALN metastasis based on intratumoral and peritumoral DCE-MRI radiomics and clinico-radiological characteristics in breast cancer

Objective

To investigate the value of predicting axillary lymph node (ALN) metastasis based on intratumoral and peritumoral dynamic contrast-enhanced MRI (DCE-MRI) radiomics and clinico-radiological characteristics in breast cancer.

Methods

A total of 473 breast cancer patients who underwent preoperative DCE-MRI from Jan 2017 to Dec 2020 were enrolled. These patients were randomly divided into training (n=378) and testing sets (n=95) at 8:2 ratio. Intratumoral regions (ITRs) of interest were manually delineated, and peritumoral regions of 3 mm (3 mmPTRs) were automatically obtained by morphologically dilating the ITR. Radiomics features were extracted, and ALN metastasis-related radiomics features were selected by the Mann-Whitney U test, Z score normalization, variance thresholding, K-best algorithm and least absolute shrinkage and selection operator (LASSO) algorithm. Clinico-radiological risk factors were selected by logistic regression and were also used to construct predictive models combined with radiomics features. Then, 5 models were constructed, including ITR, 3 mmPTR, ITR+3 mmPTR, clinico-radiological and combined (ITR+3 mmPTR+ clinico-radiological) models. The performance of models was assessed by sensitivity, specificity, accuracy, F1 score and area under the curve (AUC) of receiver operating characteristic (ROC), calibration curves and decision curve analysis (DCA).

Results

A total of 2264 radiomics features were extracted from each region of interest (ROI), 3 and 10 radiomics features were selected for the ITR and 3 mmPTR, respectively. 5 clinico-radiological risk factors were selected, including lesion size, human epidermal growth factor receptor 2 (HER2) expression, vascular cancer thrombus status, MR-reported ALN status, and time-signal intensity curve (TIC) type. In the testing set, the combined model showed the highest AUC (0.839), specificity (74.2%), accuracy (75.8%) and F1 Score (69.3%) among the 5 models. DCA showed that it had the greatest net clinical benefit compared to the other models.

Conclusion

The intra- and peritumoral radiomics models based on DCE-MRI could be used to predict ALN metastasis in breast cancer, especially for the combined model with clinico-radiological characteristics showing promising clinical application value.

Axilla View of Mammography in Preoperative Axillary Lymph Node Evaluation of Breast Cancer Patients: A Pilot Study

PURPOSE

This study aimed to explore a novel position of mammography named axilla view in axillary lymph node (ALN) evaluation in breast cancer.

PATIENTS AND METHODS

Patients were prospectively enrolled and scheduled for mammography before surgery. Investigated imaging patterns included mediolateral oblique (2D-MLO) and axilla view (2D-axilla) of mammography, and axilla view of digital breast tomosynthesis (3D-axilla). The correlation of ALN numbers between imaging and pathology was analyzed. Diagnostic performance was analyzed via AUC.

RESULTS

75 patients were included. A larger and clearer axillary region was displayed in axilla view. The total number of ALNs detected under 2D/3D-axilla view was significantly higher than that under 2D-MLO view (4.6 vs. 2.5, P < .001; 5.6 vs. 4.6, P = .034). Correlations between number of positive ALNs detected under 2D/3D-axilla view and pathologically confirmed metastatic ALNs were stronger than 2D-MLO view (Pearson correlation coefficients: 0.7084,0.7044 and 0.4744). The proportion of cases with ≥5 positive ALNs detected under 3D-axilla view was significantly higher than that under 2D-MLO (38.2% vs. 14.7%, P = .028). The overweight and obese group showed a higher AUC value than the underweight and lean group in ALN evaluation, although not significantly (2D-MLO: 0.7643 vs. 0.6458, P = .2656; 2D-axilla: 0.8083 vs. 0.6586, P = .1522; 3D-axilla: 0.8045 vs. 0.6615, P = .1874). This difference was more pronounced in axilla view.

CONCLUSION

Axilla view exhibited advantages over conventional MLO view in the extent of axilla displayed by mammography in breast cancer. Further studies with larger sample sizes are needed.

Lymph node metastases in breast cancer: Mechanisms and molecular imaging

Breast cancer is the most common malignant disease of women in the world. Breast cancer often metastasizes to axillary lymph nodes. Accurate assessment of the status of axillary lymph nodes is crucial to the staging and treatment of breast cancer. None of the methods used clinically for preoperative noninvasive examination of axillary lymph nodes can accurately identify cancer cells from a molecular level. In recent years, with the in-depth study of lymph node metastases, the mechanisms and molecular imaging of lymph node metastases in breast cancer have been reported. In this review, we highlight the new progress in the study of the main mechanisms of lymph node metastases in breast cancer. In addition, we analyze the advantages and disadvantages of traditional preoperative axillary lymph node imaging methods for breast cancer, and list molecular imaging methods that can accurately identify breast cancer cells in lymph nodes.

Attention-based Deep Learning for the Preoperative Differentiation of Axillary Lymph Node Metastasis in Breast Cancer on DCE-MRI

BACKGROUND

Previous studies have explored the potential on radiomics features of primary breast cancer tumor to identify axillary lymph node (ALN) metastasis. However, the value of deep learning (DL) to identify ALN metastasis remains unclear.

PURPOSE

To investigate the potential of the proposed attention-based DL model for the preoperative differentiation of ALN metastasis in breast cancer on dynamic contrast-enhanced MRI (DCE-MRI).

STUDY TYPE

Retrospective.

POPULATION

A total of 941 breast cancer patients who underwent DCE-MRI before surgery were included in the training (742 patients), internal test (83 patients), and external test (116 patients) cohorts.

FIELD STRENGTH/SEQUENCE

A 3.0 T MR scanner, DCE-MRI sequence.

ASSESSMENT

A DL model containing a 3D deep residual network (ResNet) architecture and a convolutional block attention module, named RCNet, was proposed for ALN metastasis identification. Three RCNet models were established based on the tumor, ALN, and combined tumor-ALN regions on the images. The performance of these models was compared with ResNet models, radiomics models, the Memorial Sloan-Kettering Cancer Center (MSKCC) model, and three radiologists (W.L., H.S., and F. L.).

STATISTICAL TESTS

Dice similarity coefficient for breast tumor and ALN segmentation. Accuracy, sensitivity, specificity, intercorrelation and intracorrelation coefficients, area under the curve (AUC), and Delong test for ALN classification.

RESULTS

The optimal RCNet model, that is, RCNet-tumor+ALN , achieved an AUC of 0.907, an accuracy of 0.831, a sensitivity of 0.824, and a specificity of 0.837 in the internal test cohort, as well as an AUC of 0.852, an accuracy of 0.828, a sensitivity of 0.792, and a specificity of 0.853 in the external test cohort. Additionally, with the assistance of RCNet-tumor+ALN , the radiologists' performance was improved (external test cohort, P < 0.05).

DATA CONCLUSION

DCE-MRI-based RCNet model could provide a noninvasive auxiliary tool to identify ALN metastasis preoperatively in breast cancer, which may assist radiologists in conducting more accurate evaluation of ALN status.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 2.

Radiomics nomogram based on digital breast tomosynthesis: preoperative evaluation of axillary lymph node metastasis in breast carcinoma

PURPOSE

This study aimed to establish a radiomics nomogram model based on digital breast tomosynthesis (DBT) images, to predict the status of axillary lymph nodes (ALN) in patients with breast carcinoma.

METHODS

The data of 120 patients with confirmed breast carcinoma, including 49 cases with axillary lymph node metastasis (ALNM), were retrospectively analyzed in this study. The dataset was randomly divided into a training group consisting of 84 patients (37 with ALNM) and a validation group comprising 36 patients (12 with ALNM). Clinical information was collected for all cases, and radiomics features were extracted from DBT images. Feature selection was performed to develop the Radscore model. Univariate and multivariate logistic regression analysis were employed to identify independent risk factors for constructing both the clinical model and nomogram model. To evaluate the performance of these models, receiver operating characteristic (ROC) curve analysis, calibration curve, decision curve analysis (DCA), net reclassification improvement (NRI), and integrated discriminatory improvement (IDI) were conducted.

RESULTS

The clinical model identified tumor margin and DBT_reported_LNM as independent risk factors, while the Radscore model was constructed using 9 selected radiomics features. Incorporating tumor margin, DBT_reported_LNM, and Radscore, the radiomics nomogram model exhibited superior performance with AUC values of 0.933 and 0.920 in both datasets, respectively. The NRI and IDI showed a significant improvement, suggesting that the Radscore may serve as a useful biomarker for predicting ALN status.

CONCLUSION

The radiomics nomogram based on DBT demonstrated effective preoperative prediction performance for ALNM in patients with breast cancer.

Non-invasive Assessment of Axillary Lymph Node Metastasis Risk in Early Invasive Breast Cancer Adopting Automated Breast Volume Scanning-Based Radiomics Nomogram: A Multicenter Study

OBJECTIVE

The aim of the work described here was to develop a non-invasive tool based on the radiomics and ultrasound features of automated breast volume scanning (ABVS), clinicopathological factors and serological indicators to evaluate axillary lymph node metastasis (ALNM) in patients with early invasive breast cancer (EIBC).

METHODS

We retrospectively analyzed 179 ABVS images of patients with EIBC at a single center from January 2016 to April 2022 and divided the patients into training and validation sets (ratio 8:2). Additionally, 97 ABVS images of patients with EIBC from a second center were enrolled as the test set. The radiomics signature was established with the least absolute shrinkage and selection operator. Significant ALNM predictors were screened using univariate logistic regression analysis and further combined to construct a nomogram using the multivariate logistic regression model. The receiver operating characteristic curve assessed the nomogram's predictive performance.

DISCUSSION

The constructed radiomics nomogram model, including ABVS radiomics signature, ultrasound assessment of axillary lymph node (ALN) status, convergence sign and erythrocyte distribution width (standard deviation), achieved moderate predictive performance for risk probability evaluation of ALNs in patients with EIBC. Compared with ultrasound, the nomogram model was able to provide a risk probability evaluation tool not only for the ALNs with positive ultrasound features but also for micrometastatic ALNs (generally without positive ultrasound features), which benefited from the radiomics analysis of multi-sourced data of patients with EIBC.

CONCLUSION

This ABVS-based radiomics nomogram model is a pre-operative, non-invasive and visualized tool that can help clinicians choose rational diagnostic and therapeutic protocols for ALNM.

High-accuracy prediction of axillary lymph node metastasis in invasive lobular carcinoma using focal cortical thickening on magnetic resonance imaging

BACKGROUND

Invasive lobular carcinoma (ILC) grows diffusely in a single-cell fashion, sometimes presenting only subtle changes in preoperative imaging; therefore, axillary lymph node (ALN) metastases of ILC are difficult to detect using magnetic resonance imaging (MRI). Preoperative underestimation of nodal burden occurs more frequently in ILC than in invasive ductal carcinoma (IDC), however, the morphological assessment for metastatic ALNs of ILC have not fully been investigated. We hypothesized that the high false-negative rate in ILC is caused by the discrepancy in the MRI findings of ALN metastases between ILC and IDC and aimed to identify the MRI finding with a strong correlation with ALN metastasis of ILC.

METHOD

This retrospective analysis included 120 female patients (mean ± standard deviation age, 57.2 ± 11.2 years) who underwent upfront surgery for ILC at a single center between April 2011 and June 2022. Of the 120 patients, 35 (29%) had ALN metastasis. Using logistic regression, we constructed prediction models based on MRI findings: primary tumor size, focal cortical thickening (FCT), cortical thickness, long-axis diameter (LAD), and loss of hilum (LOH).

RESULTS

The area under the curves were 0.917 (95% confidence interval [CI] 0.869-0.968), 0.827 (95% CI 0.758-0.896), 0.754 (95% CI 0.671-0.837), and 0.621 (95% CI 0.531-0.711) for the FCT, cortical thickness, LAD, and LOH models, respectively.

CONCLUSIONS

FCT may be the most relevant MRI finding for ALN metastasis of ILC, and although its prediction model may lead to less underestimation of the nodal burden, rigorous external validation is required.

Diagnostic Usefulness of Diffusion-Weighted MRI for Axillary Lymph Node Evaluation in Patients with Breast Cancer

This study aimed to determine whether apparent diffusion coefficient (ADC) and morphological features on diffusion-weighted MRI (DW-MRI) can discriminate metastatic axillary lymph nodes (ALNs) from benign in patients with breast cancer. Two radiologists measured ADC, long and short diameters, long-to-short diameter ratio, and cortical thickness and assessed eccentric cortical thickening, loss of fatty hilum, irregular margin, asymmetry in shape or number, and rim sign of ALNs on DW-MRI and categorized them into benign or suspicious ALNs. Pathologic reports were used as a reference standard. Statistical analysis was performed using the Mann-Whitney U test and chi-square test. Overall sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy of DW-MRI were calculated. The ADC of metastatic ALNs was 0.905 × 10^-3 mm²/s, and that of benign ALNs was 0.991 × 10^-3 mm²/s (p = 0.243). All morphologic features showed significant difference between the two groups. The sensitivity, specificity, PPV, NPV, and diagnostic accuracy of the final categorization on DW-MRI were 77.1%, 93.3%, 79.4%, 92.5%, and 86.2%, respectively. Our results suggest that morphologic evaluation of ALNs on DWI can discriminate metastatic ALNs from benign. The ADC value of metastatic ALNs was lower than that of benign nodes, but the difference was not statistically significant.

Diagnostic value of multiple ultrasound diagnostic techniques for axillary lymph node metastases in breast cancer: A systematic analysis and network meta-analysis

Background

Early diagnosis of axillary lymph node metastasis is very important for the recurrence and prognosis of breast cancer. Currently, Lymph node biopsy is one of the important methods to detect lymph node metastasis in breast cancer, however, its invasiveness might bring complications to patients. Therefore, this study investigated the diagnostic performance of multiple ultrasound diagnostic methods for axillary lymph node metastasis of breast cancer.

Materials and methods

In this study, we searched PubMed, Web of Science, CNKI and Wan Fang databases, conducted Bayesian network meta-analysis (NMA) on the studies that met the inclusion criteria, and evaluated the consistency of five different ultrasound imaging techniques in axillary lymph node metastasis of breast cancer. Funnel graph was used to evaluate whether it had publication bias. The diagnostic performance of each ultrasound imaging method was ranked using SUCRA.

Results

A total of 22 papers were included, US+CEUS showed the highest SUCRA values in terms of sensitivity (SEN) (0.874), specificity (SPE) (0.911), positive predictive value (PPV) (0.972), negative predictive value (NPV) (0.872) and accuracy (ACC) (0.990).

Conclusion

In axillary lymph node metastasis of breast cancer, the US+CEUS combined diagnostic method showed the highest SUCRA value among the five ultrasound diagnostic methods. This study provides a theoretical basis for preoperative noninvasive evaluation of axillary lymph node metastases in breast cancer patients and clinical treatment decisions.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42022351977.

Radiomics model of diffusion-weighted whole-body imaging with background signal suppression (DWIBS) for predicting axillary lymph node status in breast cancer

BACKGROUND

In breast cancer diagnosis and treatment, non-invasive prediction of axillary lymph node (ALN) metastasis can help avoid complications related to sentinel lymph node biopsy.

OBJECTIVE

This study aims to develop and evaluate machine learning models using radiomics features extracted from diffusion-weighted whole-body imaging with background signal suppression (DWIBS) examination for predicting the ALN status.

METHODS

A total of 100 patients with histologically proven, invasive, clinically N0 breast cancer who underwent DWIBS examination consisting of short tau inversion recovery (STIR) and DWIBS sequences before surgery were enrolled. Radiomic features were calculated using segmented primary lesions in DWIBS and STIR sequences and were divided into training (n = 75) and test (n = 25) datasets based on the examination date. Using the training dataset, optimal feature selection was performed using the least absolute shrinkage and selection operator algorithm, and the logistic regression model and support vector machine (SVM) classifier model were constructed with DWIBS, STIR, or a combination of DWIBS and STIR sequences to predict ALN status. Receiver operating characteristic curves were used to assess the prediction performance of radiomics models.

RESULTS

For the test dataset, the logistic regression model using DWIBS, STIR, and a combination of both sequences yielded an area under the curve (AUC) of 0.765 (95% confidence interval: 0.548-0.982), 0.801 (0.597-1.000), and 0.779 (0.567-0.992), respectively, whereas the SVM classifier model using DWIBS, STIR, and a combination of both sequences yielded an AUC of 0.765 (0.548-0.982), 0.757 (0.538-0.977), and 0.779 (0.567-0.992), respectively.

CONCLUSIONS

Use of machine learning models incorporating with the quantitative radiomic features derived from the DWIBS and STIR sequences can potentially predict ALN status.

A Model Incorporating Axillary Tail Position on Mammography for Preoperative Prediction of Non-sentinel Lymph Node Metastasis in Patients with Initial cN+ Breast Cancer after Neoadjuvant Chemotherapy

RATIONALE AND OBJECTIVES

This study aimed to develop a model incorporating axillary tail position on mammography (AT) for the prediction of non-sentinel Lymph Node (NSLN) metastasis in patients with initial clinical node positivity (cN+).

METHODS AND MATERIALS

The study reviewed a total of 257 patients with cN+ breast cancer who underwent both sentinel lymph node biopsy (SLNB) and axillary lymph node dissection (ALND) following neoadjuvant chemotherapy (NAC). A logistic regression model was developed based on these factors and the results of post-NAC AT and axillary ultrasound (AUS).

RESULTS

Four clinical factors with p<0.1 in the univariate analysis, including ycT0(odds ratio [OR]: 4.84, 95% confidence interval [CI]: 2.13-11.91, p<0.001), clinical stage before NAC (OR: 2.68, 95%CI: 1.15-6.58, p=0.025), estrogen receptor (ER) expression (OR: 3.29, 95%CI: 1.39-8.39, p=0.009), and HER2 status (OR: 0.21, 95%CI: 0.08-0.50, p=0.001), were independent predictors of NSLN metastases. The clinical model based on the above four factors resulted in the area under the curve (AUC) of 0.82(95%CI: 0.76-0.88) in the training set and 0.83(95% CI: 0.74-0.92) in the validation set. The results of post-NAC AUS and AT were added to the clinical model to construct a clinical imaging model for the prediction of NSLN metastasis with AUC of 0.87(95%CI: 0.81-0.93) in the training set and 0.89(95%CI: 0.82-0.96) in the validation set.

CONCLUSIONS

The study incorporated the results of post-NAC AT and AUS with other clinal factors to develop a model to predict NSLN metastasis in patients with initial cN+ before surgery. This model performed excellently, allowing physicians to select patients for whom unnecessary ALND could be avoided after NAC.

Assessing Whether Morphological Changes in Axillary Lymph Node Have Already Occurred Prior to Metastasis in Breast Cancer Patients by Ultrasound

Background and Objectives: Whether the morphological changes in axillary lymph node (ALN) have occurred prior to metastasis remains unclear in breast cancer (BC) patients. The aim of this study is to investigate the influence of BC for the morphology of non-metastasis ALN (N−) and, further, to improve the performance of ultrasound (US) examination for metastasis ALN (N+). Materials and Methods: In this retrospective study, 653 patients with breast mass were enrolled and divided into normal group of 202 patients with benign breast tumor, N− group of 233 BC patients with negative ALN and N+ group of 218 BC patients with positive ALN. US features of ALN were evaluated and analyzed according to long (L) and short (S) diameter, the (L/S) axis ratio, cortical thickness, lymph node edge, replaced hilum and color Doppler flow imaging (CDFI). Results: ALN US features of short diameter, replaced hilum, cortical thickness and CDFI have significant statistical differences in N− group comparing with normal group and N+ group, respectively (p < 0.05). Conclusions: Therefore, BC can affect ALN and lead to US morphological changes whether lymph node metastasis is present, which reduces the sensitivity of axillary US. The combination of US and other examination methods should be applied to improve the diagnostic performance of N+.

Factors Associated with Axillary Lymph Node Status in Clinically Node-Negative Breast Cancer Patients Undergoing Neoadjuvant Chemotherapy

Adequate axillary lymph node (ALN) staging is critical for patients with invasive breast cancer. However, neoadjuvant chemotherapy (NAC) was associated with a lower risk of ALN metastasis compared with those who underwent primary surgery among clinically node-negative (cN0) patients. This study aimed to investigate the factors associated with ALN status among patients with cN0 breast cancer undergoing NAC. A total of 222 consecutive patients with cN0 breast cancer undergoing NAC between January 2012 and December 2021 were reviewed. Univariate and multivariate analyses were performed to compare factors associated with positive ALN status. Seventeen patients (7.7%) had ALNs metastases. Here, 90 patients (40.5%) achieved pathologic complete response in the breast (breast-pCR), and all had negative ALN status. Lymphovascular invasion (odds ratio: 29.366, p < 0.0001) was an independent risk predictor of ALN metastasis in all study populations. Among patients without breast-pCR, mastectomies were performed more frequently in patients with ALN metastasis (52.9%) than in those without metastasis (20.9%) (p = 0.013). Our findings support the omission of axillary surgery in patients who achieve breast-pCR. Prospective studies are needed to confirm the feasibility of a future two-stage surgical plan for breast-conserving surgery in patients who are likely to achieve breast-pCR during clinical evaluation.

Clinical prediction model based on 18F-FDG PET/CT plus contrast-enhanced MRI for axillary lymph node macrometastasis

Purpose

Positron emission tomography/computed tomography (PET/CT) and magnetic resonance imaging (MRI) are useful for detecting axillary lymph node (ALN) metastasis in invasive ductal breast cancer (IDC); however, there is limited clinical evidence to demonstrate the effectiveness of the combination of PET/CT plus MRI. Further axillary surgery is not recommended against ALN micrometastasis (lesion ≤2 mm) seen in sentinel lymph nodes, especially for patients who received proper adjuvant therapy. We aimed to evaluate the efficacy of a prediction model based on PET/CT plus MRI for ALN macrometastasis (lesion >2 mm) and explore the possibility of risk stratification of patients using the preoperative PET/CT plus MRI and biopsy findings.

Materials and methods

We retrospectively investigated 361 female patients (370 axillae; mean age, 56 years ± 12 [standard deviation]) who underwent surgery for primary IDC at a single center between April 2017 and March 2020. We constructed a prediction model with logistic regression. Patients were divided into low-risk and high-risk groups using a simple integer risk score, and the false negative rate for ALN macrometastasis was calculated to assess the validity. Internal validation was also achieved using a 5-fold cross-validation.

Results

The PET/CT plus MRI model included five predictor variables: maximum standardized uptake value of primary tumor and ALN, primary tumor size, ALN cortical thickness, and histological grade. In the derivation (296 axillae) and validation (74 axillae) cohorts, 54% and 61% of patients, respectively, were classified as low-risk, with a false-negative rate of 11%. Five-fold cross-validation yielded an accuracy of 0.875.

Conclusions

Our findings demonstrate the validity of the PET/CT plus MRI prediction model for ALN macrometastases. This model may aid the preoperative identification of low-risk patients for ALN macrometastasis and provide helpful information for PET/MRI interpretation.

Nomogram models for stratified prediction of axillary lymph node metastasis in breast cancer patients (cN0)

Objectives

To determine the predictors of axillary lymph node metastasis (ALNM), two nomogram models were constructed to accurately predict the status of axillary lymph nodes (ALNs), mainly high nodal tumour burden (HNTB, > 2 positive lymph nodes), low nodal tumour burden (LNTB, 1-2 positive lymph nodes) and negative ALNM (N0). Accordingly, more appropriate treatment strategies for breast cancer patients without clinical ALNM (cN0) could be selected.

Methods

From 2010 to 2015, a total of 6314 patients with invasive breast cancer (cN0) were diagnosed in the Surveillance, Epidemiology, and End Results (SEER) database and randomly assigned to the training and internal validation groups at a ratio of 3:1. As the external validation group, data from 503 breast cancer patients (cN0) who underwent axillary lymph node dissection (ALND) at the Second Affiliated Hospital of Chongqing Medical University between January 2011 and December 2020 were collected. The predictive factors determined by univariate and multivariate logistic regression analyses were used to construct the nomograms. Receiver operating characteristic (ROC) curves and calibration plots were used to assess the prediction models' discrimination and calibration.

Results

Univariate analysis and multivariate logistic regression analyses showed that tumour size, primary site, molecular subtype and grade were independent predictors of both ALNM and HNTB. Moreover, histologic type and age were independent predictors of ALNM and HNTB, respectively. Integrating these independent predictors, two nomograms were successfully developed to accurately predict the status of ALN. For nomogram 1 (prediction of ALNM), the areas under the receiver operating characteristic (ROC) curve in the training, internal validation and external validation groups were 0.715, 0.688 and 0.876, respectively. For nomogram 2 (prediction of HNTB), the areas under the ROC curve in the training, internal validation and external validation groups were 0.842, 0.823 and 0.862. The above results showed a satisfactory performance.

Conclusion

We established two nomogram models to predict the status of ALNs (N0, 1-2 positive ALNs or >2 positive ALNs) for breast cancer patients (cN0). They were well verified in further internal and external groups. The nomograms can help doctors make more accurate treatment plans, and avoid unnecessary surgical trauma.

Evaluation of different imaging modalities for axillary lymph node staging in breast cancer patients to provide a personalized and optimized therapy algorithm

PURPOSE

The reliable detection of tumor-infiltrated axillary lymph nodes for breast cancer [BC] patients plays a decisive role in further therapy. We aimed to find out whether cross-sectional imaging techniques could improve sensitivity for pretherapeutic axillary staging in nodal-positive BC patients compared to conventional imaging such as mammography and sonography.

METHODS

Data for breast cancer patients with tumor-infiltrated axillary lymph nodes having received surgery between 2014 and 2020 were included in this study. All examinations (sonography, mammography, computed tomography [CT] and magnetic resonance imaging [MRI]) were interpreted by board-certified specialists in radiology. The sensitivity of different imaging modalities was calculated, and binary logistic regression analyses were performed to detect variables influencing the detection of positive lymph nodes.

RESULTS

All included 382 breast cancer patients had received conventional imaging, while 52.61% of the patients had received cross-sectional imaging. The sensitivity of the combination of all imaging modalities was 68.89%. The combination of MRI and CT showed 63.83% and the combination of sonography and mammography showed 36.11% sensitivity.

CONCLUSION

We could demonstrate that cross-sectional imaging can improve the sensitivity of the detection of tumor-infiltrated axillary lymph nodes in breast cancer patients. Only the safe detection of these lymph nodes at the time of diagnosis enables the evaluation of the response to neoadjuvant therapy, thereby allowing access to prognosis and improving new post-neoadjuvant therapies.

Efficient Axillary Lymph Node Detection Via Two-stage Spatial-information-fusion-based CNN

BACKGROUND AND OBJECTIVE

Preoperative imaging diagnosis of axillary lymph node (ALN) metastasis is particularly important for breast cancer patients. This paper focuses on developing non-invasive and automatic schemes for accurate localization and classification (metastasis prediction) of ALN via contrast-enhanced computed tomography (CECT) image and deep learning models.

METHODS

Based on a two-stage strategy, a novel detection neural network is proposed, where the convolutional block attention module is utilized to extract spacial information and the bottleneck feature fusion module is designed for feature fusion in different scales.

RESULTS

Owing to the two embedded modules, the proposed convolutional neural network (CNN) model outperforms Faster R-CNN, YOLOv3, and EfficientDet in the sense that the achieved mAP is 0.454, higher than 0.247, 0.335, and 0.329, respectively. In particular, considering the function of classification only, the proposed model reaches the best performance on most indices (accuracy of 0.968, positive predictive value of 0.972, negative predictive value of 0.966, specificity of 0.983), compared with the methods that have been frequently adopted to predict ALN. In addition, the proposed CNN model has the function of locating ALN, which is lacking in existing models.

CONCLUSIONS

In this paper, a supervised deep learning method is proposed to detect ALN in CECT images. The positive effect of new added modules are verified, and the benefits of spatial information in ALN detection are confirmed. Further, the two subtasks called localization and classification are evaluated separately, where the proposed model achieves the best performance on most indices. The source code mentioned in this article will be released later.

Optimal Selection of Imaging Examination for Lymph Node Detection of Breast Cancer With Different Molecular Subtypes

Objective

Axillary lymph node management is an important part of breast cancer surgery and the accuracy of preoperative imaging evaluation can provide adequate information to guide operation. Different molecular subtypes of breast cancer have distinct imaging characteristics. This article was aimed to evaluate the predictive ability of imaging methods in accessing the status of axillary lymph node in different molecular subtypes.

Methods

A total of 2,340 patients diagnosed with primary invasive breast cancer after breast surgery from 2013 to 2018 in Jiangsu Breast Disease Center, the First Affiliated Hospital with Nanjing Medical University were included in the study. We collected lymph node assessment results from mammography, ultrasounds, and MRIs, performed receiver operating characteristic (ROC) analysis, and calculated the sensitivity and specificity of each test. The C-statistic among different imaging models were compared in different molecular subtypes to access the predictive abilities of these imaging models in evaluating the lymph node metastasis.

Results

In Her-2 + patients, the C-statistic of ultrasound was better than that of MRI (0.6883 vs. 0.5935, p=0.0003). The combination of ultrasound and MRI did not raise the predictability compared to ultrasound alone (p=0.492). In ER/PR+HER2- patients, the C-statistic of ultrasound was similar with that of MRI (0.7489 vs. 0.7650, p=0.5619). Ultrasound+MRI raised the prediction accuracy compared to ultrasound alone (p=0.0001). In ER/PR-HER2- patients, the C-statistics of ultrasound was similar with MRI (0.7432 vs. 0.7194, p=0.5579). Combining ultrasound and MRI showed no improvement in the prediction accuracy compared to ultrasound alone (p=0.0532).

Conclusion

From a clinical perspective, for Her-2+ patients, ultrasound was the most recommended examination to assess the status of axillary lymph node metastasis. For ER/PR+HER2- patients, we suggested that the lymph node should be evaluated by ultrasound plus MRI. For ER/PR-Her2- patients, ultrasound or MRI were both optional examinations in lymph node assessment. Furthermore, more new technologies should be explored, especially for Her2+ patients, to further raise the prediction accuracy of lymph node assessment.

Detecting Abnormal Axillary Lymph Nodes on Mammograms Using a Deep Convolutional Neural Network

The purpose of this study was to determine the feasibility of a deep convolutional neural network (dCNN) to accurately detect abnormal axillary lymph nodes on mammograms. In this retrospective study, 107 mammographic images in mediolateral oblique projection from 74 patients were labeled to three classes: (1) “breast tissue”, (2) “benign lymph nodes”, and (3) “suspicious lymph nodes”. Following data preprocessing, a dCNN model was trained and validated with 5385 images. Subsequently, the trained dCNN was tested on a “real-world” dataset and the performance compared to human readers. For visualization, colored probability maps of the classification were calculated using a sliding window approach. The accuracy was 98% for the training and 99% for the validation set. Confusion matrices of the “real-world” dataset for the three classes with radiological reports as ground truth yielded an accuracy of 98.51% for breast tissue, 98.63% for benign lymph nodes, and 95.96% for suspicious lymph nodes. Intraclass correlation of the dCNN and the readers was excellent (0.98), and Kappa values were nearly perfect (0.93–0.97). The colormaps successfully detected abnormal lymph nodes with excellent image quality. In this proof-of-principle study in a small patient cohort from a single institution, we found that deep convolutional networks can be trained with high accuracy and reliability to detect abnormal axillary lymph nodes on mammograms.

ACR Appropriateness Criteria® Imaging of the Axilla

This publication reviews the current evidence supporting the imaging approach of the axilla in various scenarios with broad differential diagnosis ranging from inflammatory to malignant etiologies. Controversies on the management of axillary adenopathy results in disagreement on the appropriate axillary imaging tests. Ultrasound is often the appropriate initial imaging test in several clinical scenarios. Clinical information (such as age, physical examinations, risk factors) and concurrent complete breast evaluation with mammogram, tomosynthesis, or MRI impact the type of initial imaging test for the axilla. Several impactful clinical trials demonstrated that selected patient's population can received sentinel lymph node biopsy instead of axillary lymph node dissection with similar overall survival, and axillary lymph node dissection is a safe alternative as the nodal staging procedure for clinically node negative patients or even for some node positive patients with limited nodal tumor burden. This approach is not universally accepted, which adversely affect the type of imaging tests considered appropriate for axilla. This document is focused on the initial imaging of the axilla in various scenarios, with the understanding that concurrent or subsequent additional tests may also be performed for the breast. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.

A Nomogram Based on Molecular Biomarkers and Radiomics to Predict Lymph Node Metastasis in Breast Cancer

Background

The aim of this study was to explore the feasibility and efficacy of a non-invasive quantitative imaging evaluation model to assess the lymphatic metastasis of breast cancer based on a radiomics signature constructed using conventional T1-weighted image (T1WI) enhanced MRI and molecular biomarkers.

Methods

Patients with breast cancer diagnosed via lymph biopsies between June 2015 and June 2019 were selected for the study. All patients underwent T1WI contrast-enhancement before treatment; lymph biopsy after surgery; and simultaneous Ki-67, COX-2, PR, Her2 and proliferating cell nuclear antigen detection. All images were imported into ITK-SNAP for whole tumor delineation, and AK software was used for radiomics feature extraction. Next, the radiomics signature Rad-score was constructed after reduction of specific radiomic features. A multiple regression logistic model was built by combining the Rad-score and molecular biomarkers based on the minimum AIC.

Results

In all, 100 patients were enrolled in this study, including 45 with non-lymph node (LN) metastasis and 55 with LN metastasis. A total of 1,051 texture feature parameters were extracted, and LASSO was used to reduce the dimensionality of the radiomics features. The log(λ) was set to 0.002786, and 19 parameters were retained for the construction of the radiomics tag Rad-score. ROC was used to evaluate the diagnostic efficiency of Rad-score: the area under the ROC curve (AUC) of the Rad-score for identifying non-lymphatic and lymphatic metastases was 0.891 in the training cohort and 0.744 in the validation cohort. With the incorporation of tumor molecular markers, the AUCs of the training cohort and validation cohort of the nomogram were 0.936 and 0.793, respectively, which were notably higher than the AUCs of the clinical parameters in the training and validation cohorts (0.719 and 0.588, respectively).

Conclusion

The combined model constructed using the Rad-score and molecular biomarkers can be used as an effective non-invasive method to assess LN metastasis of breast cancer. Furthermore, it can be used to quantitatively evaluate the risk of breast cancer LN metastasis before surgery.

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.

Sentinel lymph node detection by combining nonradioactive techniques with contrast agents: State of the art and prospects

The status of sentinel lymph nodes (SLNs) has a substantial prognostic value because these nodes are the first place where cancer cells accumulate along their spreading route. Routine SLN biopsy ("gold standard") involves peritumoral injections of radiopharmaceuticals, such as technetium-99m, which has obvious disadvantages. This review examines the methods used as "gold standard" analogs to diagnose SLNs. Nonradioactive preoperative and intraoperative methods of SLN detection are analyzed. Promising photonic tools for SLNs detection are reviewed, including NIR-I/NIR-II fluorescence imaging, photoswitching dyes for SLN detection, in vivo photoacoustic detection, imaging and biopsy of SLNs. Also are discussed methods of SLN detection by magnetic resonance imaging, ultrasonic imaging systems including as combined with photoacoustic imaging, and methods based on the magnetometer-aided detection of superparamagnetic nanoparticles. The advantages and disadvantages of nonradioactive SLN-detection methods are shown. The review concludes with prospects for the use of conservative diagnostic methods in combination with photonic tools.

Imaging features of sentinel lymph node mapped by multidetector-row computed tomography lymphography in predicting axillary lymph node metastasis

INTRODUCTION

Accurately assessing axillary lymph node (ALN) status in breast cancer is vital for clinical decision making and prognosis. The purpose of this study was to evaluate the predictive value of sentinel lymph node (SLN) mapped by multidetector-row computed tomography lymphography (MDCT-LG) for ALN metastasis in breast cancer patients.

METHODS

112 patients with breast cancer who underwent preoperative MDCT-LG examination were included in the study. Long-axis diameter, short-axis diameter, ratio of long-/short-axis and cortical thickness were measured. Logistic regression analysis was performed to evaluate independent predictors associated with ALN metastasis. The prediction of ALN metastasis was determined with related variables of SLN using receiver operating characteristic (ROC) curve analysis.

RESULTS

Among the 112 cases, 35 (30.8%) cases had ALN metastasis. The cortical thickness in metastatic ALN group was significantly thicker than that in non-metastatic ALN group (4.0 ± 1.2 mm vs. 2.4 ± 0.7 mm, P < 0.001). Multi-logistic regression analysis indicated that cortical thickness of > 3.3 mm (OR 24.53, 95% CI 6.58-91.48, P < 0.001) had higher risk for ALN metastasis. The best sensitivity, specificity, negative predictive value(NPV) and AUC of MDCT-LG for ALN metastasis prediction based on the single variable of cortical thickness were 76.2%, 88.5%, 90.2% and 0.872 (95% CI 0.773-0.939, P < 0.001), respectively.

CONCLUSION

ALN status can be predicted using the imaging features of SLN which was mapped on MDCT-LG in breast cancer patients. Besides, it may be helpful to select true negative lymph nodes in patients with early breast cancer, and SLN biopsy can be avoided in clinically and radiographically negative axilla.

Prospective comparison of CT and 18F-FDG PET/MRI in N and M staging of primary breast cancer patients: Initial results

Objectives

To compare the diagnostic accuracy of contrast-enhanced thoraco-abdominal computed tomography and whole-body ¹⁸F-FDG PET/MRI in N and M staging in newly diagnosed, histopathological proven breast cancer.

Material and methods

A total of 80 consecutive women with newly diagnosed and histopathologically confirmed breast cancer were enrolled in this prospective study. Following inclusion criteria had to be fulfilled: (1) newly diagnosed, treatment-naive T2-tumor or higher T-stage or (2) newly diagnosed, treatment-naive triple-negative tumor of every size or (3) newly diagnosed, treatment-naive tumor with molecular high risk (T1c, Ki67 >14%, HER2neu over-expression, G3). All patients underwent a thoraco-abdominal ceCT and a whole-body ¹⁸F-FDG PET/MRI. All datasets were evaluated by two experienced radiologists in hybrid imaging regarding suspect lesion count, localization, categorization and diagnostic confidence. Images were interpreted in random order with a reading gap of at least 4 weeks to avoid recognition bias. Histopathological results as well as follow-up imaging served as reference standard. Differences in staging accuracy were assessed using Mc Nemars chi² test.

Results

CT rated the N stage correctly in 64 of 80 (80%, 95% CI:70.0–87.3) patients with a sensitivity of 61.5% (CI:45.9–75.1), a specificity of 97.6% (CI:87.4–99.6), a PPV of 96% (CI:80.5–99.3), and a NPV of 72.7% (CI:59.8–82.7). Compared to this, ¹⁸F-FDG PET/MRI determined the N stage correctly in 71 of 80 (88.75%, CI:80.0–94.0) patients with a sensitivity of 82.1% (CI:67.3–91.0), a specificity of 95.1% (CI:83.9–98.7), a PPV of 94.1% (CI:80.9–98.4) and a NPV of 84.8% (CI:71.8–92.4). Differences in sensitivities were statistically significant (difference 20.6%, CI:-0.02–40.9; p = 0.008). Distant metastases were present in 7/80 patients (8.75%). ¹⁸ F-FDG PET/MRI detected all of the histopathological proven metastases without any false-positive findings, while 3 patients with bone metastases were missed in CT (sensitivity 57.1%, specificity 95.9%). Additionally, CT presented false-positive findings in 3 patients.

Conclusion

¹⁸F-FDG PET/MRI has a high diagnostic potential and outperforms CT in assessing the N and M stage in patients with primary breast cancer.

The new perspective of PET/CT for axillary nodal staging in early breast cancer patients according to ACOSOG Z0011 trial PET/CT axillary staging according to Z0011

OBJECTIVE

Post Z0011 trial, axillary lymph node dissections (ALNDs) can be performed in patients with ≥3 positive axillary lymph nodes (ALNs). We investigated the diagnostic performance of 18F-fluorodeoxyglucose PET/computed tomography (FDG PET/CT) to predict ≥3 metastasis [high nodal burden (HNB)].

METHODS

We retrospectively analyzed preoperative FDG PET/CT from January 2010 to June 2012. Patients had clinical T1-2N0 primary invasive breast cancer and underwent breast-conserving surgery with sentinel lymph node biopsy ± ALND. All suspicious ALNs were counted considering FDG-avidity with morphologic changes. Images were considered positive if the axillary basin took up more FDG than the surrounding tissue. On CT, abnormal ALNs were round/ovoid or had cortical thickening with contrast enhancement. PET/CT results were compared with the histology and follow-up findings.

RESULTS

In total, 221 females with 224 axillae were enrolled; 161 had negative, 53 had 1-2 metastasis [low nodal burden (LNB)] and 10 had HNB. The sensitivity, specificity, negative predictive value and positive predictive value of PET/CT for HNB were 70, 100, 98.6 and 100%, respectively. There was a correlation between the number of suspicious ALNs on PET/CT and the metastatic nodes on final histology. There were no significant differences in age, tumor size and FDG-avidity between patients with negative or LNB and HNB. During follow-up, 25 patients had a recurrence. The three false-negative patients did not show recurrence.

CONCLUSION

Preoperative PET/CT predicts HNB with high accuracy and is useful for evaluating clinical T1-2N0 invasive breast cancer.

Diagnostic value of Platelet/lymphocyte Ratio (PLR) for predicting sentinel axillary lymph node positivity in early-stage breast cancer compared with ultrasonography

INTRODUCTION

In breast cancer, the most important prognostic factor is axillary lymph node metastasis. However, there is no method which can diagnose axillary lymph node metastasis preoperatively with high sensitivity. The aim of this study was to evaluate the relationship between platelet/lymphocyte ratio (PLR) and sentinel lymph node metastasis in early-stage breast cancer.

METHODS

In total, 202 cases which were operated under early-stage breast cancer diagnosis in Ankara City Hospital General Surgery Department were evaluated in retrospectively. We separated the patients into two categories according to their PLR. PLR groups were evaluated for relationship with sentinel lymph node metastasis. At the last part, sentinel lymph node positive sensitivity was evaluated in PLR and preoperative USG groups.

RESULTS

Results showed that patients above PLR cut-off value had 0.43 times more risk of having a positive SLN in comparison with patients who had a PLR lower than cut-off (OR = 0.435, 95%CI:0.221-0.856, P < .016). When the PLR and USG were used in combination, sensitivity goes up to 75.5% and specificity 96%.

CONCLUSION

The rate of success in diagnosing metastatic SLN in early-stage breast cancer is higher in PLR when compared with USG and other imaging methods.

A meta-analysis of the diagnostic performance of machine learning-based MRI in the prediction of axillary lymph node metastasis in breast cancer patients

Background

Despite that machine learning (ML)-based MRI has been evaluated for diagnosis of axillary lymph node metastasis (ALNM) in breast cancer patients, diagnostic values they showed have been variable. In this study, we aimed to assess the use of ML to classify ALNM on MRI and to identify potential covariates that might influence the diagnostic performance of ML.

Methods

A systematic research of PubMed, Embase, Web of Science, and the Cochrane Library was conducted until 27 December 2020 to collect the included articles. Subgroup analysis was also performed.

Findings

Fourteen studies assessing a total of 2247 breast cancer patients were included in the analysis. The overall AUC for ML in the validation set was 0.80 (95% confidence interval [CI] 0.76–0.83) with a negative predictive value of 0.83. The pooled sensitivity and specificity were 0.79 (95% CI 0.74–0.84) and 0.77 (95% CI 0.73–0.81), respectively. In the subgroup analysis of the validation set, T1-weighted contrast-enhanced (T1CE) imaging with ML yielded a higher sensitivity (0.80 vs. 0.67 vs. 0.76) than the T2-weighted fat-suppressed (T2-FS) imaging and diffusion-weighted imaging (DWI). Support vector machines (SVMs) had a higher specificity than linear regression (LR) and linear discriminant analysis (LDA) (0.79 vs. 0.78 vs. 0.75), whereas LDA showed a higher sensitivity than LR and SVM (0.83 vs. 0.70 vs. 0.77).

Interpretation

MRI sequences and algorithms were the main factors that affect the diagnostic performance of ML. Although its results were encouraging with the pooled sensitivity of around 0.80, it meant that 1 in 5 women that would go with undetected metastases, which may have a detrimental effect on the overall survival for 20% of patients with positive SLN status. Despite that a high NPV of 0.83 meant that ML could potentially benefit those with negative SLN, it might also translate to 1 in 5 tests being false negative. We would like to suggest that ML may not be yet usable in clinical routine especially when patient survival is used as a primary measurement of its outcome.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13244-021-01034-1.

Models for Predicting Sentinel and Non-sentinel Lymph Nodes Based on Pre-operative Ultrasonic Breast Imaging to Optimize Axillary Strategies

Axillary strategy decisions have become more complex and controversial in considering minimally traumatic therapy instead of sentinel lymph node biopsy, axillary lymph node dissection or regional nodal irradiation for people with breast cancer. The purpose of this study was to noninvasively predict sentinel lymph node (SLN) and non-sentinel lymph node (NSLN) status based on pre-operative sonographic and clinicopathologic features to determine optimal decisions regarding axillary therapy. In total, 701 patients with breast cancer from two independent centers were retrospectively analyzed. The SLN model (SLNM) for predicting SLN status and the NSLN model (NSLNM) for predicting NSLN status were trained based on a training set using the random-forest algorithm, and their performance was validated using an independent external test set. A receiver operating characteristic curve was drawn to obtain the area under the curve, which was used to assess performance. The area under the curve for the SLNM in the training and test, respectively, was 94.2% and 83.0%, and for the NSLNM, 99.5% and 92.7%. The SLNM and NSLNM accurately predicted that 61.46% (319/519) and 17.53% (91/519), respectively, of our participants were non-metastatic. The overall benefit of the three models was 78.99% in our participants. The two models for predicting SLN and NSLN status showed excellent application potential in optimizing axillary strategies.

Nomogram Based on Breast MRI and Clinicopathologic Features for Predicting Axillary Lymph Node Metastasis in Patients with Early-Stage Invasive Breast Cancer: A Retrospective Study

INTRODUCTION

To establish a nomogram for predicting axillary lymph node (ALN) involvement in patients with early-stage invasive breast cancer (BC) based on magnetic resonance imaging (MRI) features and clinicopathological characteristics.

MATERIALS AND METHODS

Patients with confirmed early-stage invasive BC between 03/2016 and 05/2017 were retrospectively reviewed at the National Cancer Center/Cancer Hospital. Risk factors for ALN metastasis (ALNM) were identified by univariable and multivariable logistic regression analysis. The independent risk factors were used to create a nomogram.

RESULTS

This study included 214 early-stage invasive BC patients, including 57 (26.6%) with positive ALNs. Tumor location (OR = 4.019, 95% CI: 1.304 -12.383, P = .015), tumor size (OR = 3.702, 95%CI: 1.517 -9.034, P = .004), multifocality (OR = 3.534, 95%CI: 1.249 -9.995, P = .017), MR-reported suspicious ALN (OR = 9.829, 95%CI: 4.132 -23.384, P <0.001), apparent diffusion coefficient (ADC) value (OR = 0.367, 95%CI: 0.158 -0.852, P = .020), and lymphovascular invasion (LVI) (OR = 3.530, 95%CI: 1.483 -8.400, P = .004) were identified as independent risk factors associated with ALNM. A nomogram was created for predicting the probability of ALNM by using these risk factors. The calibration curve of the nomogram showed that the nomogram predictions are consistent with the actual ALNM rate. The area under the curve was 0.88 (95% CI: 0.83 -0.93). The nomogram had a bootstrapped-concordance index of 0.88 and was well-calibrated.

CONCLUSION

The nomogram based on MRI and clinicopathologic features might be a useful tool for predicting ALNM in early-stage invasive BC and could help clinical decision-making.

Clinical Value of Three Combined Ultrasonography Modalities in Predicting the Risk of Metastasis to Axillary Lymph Nodes in Breast Invasive Ductal Carcinoma

Objective

The present study aimed to assess the clinical value of conventional ultrasound (C-US), ultrasound elastography (UE), percutaneous contrast-enhanced ultrasound (P-CUES), and the combination of these three ultrasonography modalities for evaluating the risk of axillary lymph node (ALN) metastasis in breast invasive ductal carcinoma (IDC).

Methods

This retrospective analysis included 120 patients with pathologically confirmed IDC who underwent sentinel lymph node biopsy (SLNB) or axillary lymph node dissection (ALND). Based on the gold standard of postoperative pathology, ALN pathology results were evaluated and compared with findings obtained using C-US, UE, P-CUES, and the three modalities combined.

Results

(1) There was a statistically significant difference between the histological grade of the tumor and the pathological condition of ALNs. (2) The difference between C-US parameters and UE score were statistically significant. The accuracy of P-CEUS localization of SLNs was 100% (96/96) when compared with localization guided by methylene blue. The difference in the distribution of the four SLN enhancement patterns was statistically significant. (3) The sensitivity, specificity, positive predictive value, and negative predictive value of C-US and UE were 75%, 71%, 58%, and 89%, and 71%, 72%, 50%, and 86%, respectively. The sensitivity, specificity, positive predictive value, and negative predictive value of P-CUES were 91%, 82%, 78%, 92%, respectively. When all three modalities were combined, the sensitivity, specificity, positive predictive value, and negative predictive value were 94%, 89%, 86%, and 95%, respectively. In the detection of ALN metastasis, there was a good correlation between histopathological results and evaluations based on the three combined ultrasonography modalities (kappa: 0.82, p<0.001).

Conclusions

When compared to C-US, UE, or P-CEUS alone, the combination of the three ultrasonography modalities was found to be superior in distinguishing metastatic and non-metastatic ALNs. This combined strategy may aid physicians in determining the most appropriate approach to ALN surgery as well as the prognosis of breast IDC.

Prediction of Metastasis in the Axillary Lymph Nodes of Patients With Breast Cancer: A Radiomics Method Based on Contrast-Enhanced Computed Tomography

Background

The use of traditional techniques to evaluate breast cancer is restricted by the subjective nature of assessment, variation across radiologists, and limited data. Radiomics may predict axillary lymph node metastasis (ALNM) of breast cancer more accurately.

Purpose

The aim was to evaluate the diagnostic performance of a radiomics model based on ALNs themselves that used contrast-enhanced computed tomography (CECT) to detect ALNM of breast cancer.

Methods

We retrospectively enrolled 402 patients with breast cancer confirmed by pathology from January 2016 to October 2019. Three hundred and ninety-six features were extracted for all patients from axial CECT images of 825 ALNs using Artificial Intelligent Kit software (GE Medical Systems, Version V3.1.0.R). Next, the radiomics model was trained, validated, and tested for predicting ALNM in breast cancer by using a support vector machine algorithm. Finally, the performance of the radiomics model was evaluated in terms of its classification accuracy and the value of the area under the curve (AUC).

Results

The radiomics model yielded the best classification accuracy of 89.1% and the highest AUC of 0.92 (95% CI: 0.91-0.93, p=0.002) for discriminating ALNM in breast cancer in the validation cohorts. In the testing cohorts, the model also demonstrated better performance, with an accuracy of 88.5% and an AUC of 0.94 (95% CI: 0.93-0.95, p=0.005) for predicting ALNM in breast cancer.

Conclusion

The radiomics model based on CECT images can be used to predict ALNM in breast cancer and has significant potential in clinical noninvasive diagnosis and in the prediction of breast cancer metastasis.

Imaging Updates to Breast Cancer Lymph Node Management

Metastatic lymph node involvement in breast cancer is a key determinant of the overall stage of disease and prognosis. Historically, lymph node status was determined by surgery first, with adjuvant treatments determined based on the results of the final surgical pathologic analysis. While this sequence is still applicable in many cases, neoadjuvant systemic treatment (NST) is increasingly being administered as the initial treatment. In cases that demonstrate good therapeutic response to drug therapies, NST may permit the option to perform less radical surgeries subsequently. Current breast cancer treatment has become multidisciplinary, with overlapping roles from the different disciplines. As surgery may be postponed, imaging and image-guided lymph node interventions have gained importance as the primary means of lymph node assessment. Imaging enables evaluation of all regional nodal basins, including locations where surgery is not usually performed. By differentiating limited versus extensive nodal involvement, imaging findings help determine whether initial treatment should be surgical or medical. If medical treatment with NST is indicated, imaging is performed to monitor the in vivo nodal response to drug therapy and ultimately to help determine the surgical technique to perform on the basis of the final imaging findings after NST. The authors discuss the imaging features of nodal metastases and the indications and techniques for the various image-guided procedures. The relative usefulness and shortcomings of the various imaging examinations are reviewed to discuss how they can be applied when biopsy results are not available. The role of imaging in the multidisciplinary team approach is emphasized based on past clinical trials of lymph node management and recent evolving knowledge of breast cancer staging. Online supplemental material is available for this article. ^©RSNA, 2021.

Development of MRI-based axillary numerical models and estimation of axillary lymph node dielectric properties for microwave imaging

PURPOSE

Microwave imaging (MWI) has been studied as a complementary imaging modality to improve sensitivity and specificity of diagnosis of axillary lymph nodes (ALNs), which can be metastasized by breast cancer. The feasibility of such a system is based on the dielectric contrast between healthy and metastasized ALNs. However, reliable information such as anatomically realistic numerical models and matching dielectric properties of the axillary region and ALNs, which are crucial to develop MWI systems, are still limited in the literature. The purpose of this work is to develop a methodology to infer dielectric properties of structures from magnetic resonance imaging (MRI), in particular, ALNs. We further use this methodology, which is tailored for structures farther away from MR coils, to create MRI-based numerical models of the axillary region and share them with the scientific community, through an open-access repository.

METHODS

We use a dataset of breast MRI scans of 40 patients, 15 of them with metastasized ALNs. We apply image processing techniques to minimize the artifacts in MR images and segment the tissues of interest. The background, lung cavity, and skin are segmented using thresholding techniques and the remaining tissues are segmented using a K-means clustering algorithm. The ALNs are segmented combining the clustering results of two MRI sequences. The performance of this methodology was evaluated using qualitative criteria. We then apply a piecewise linear interpolation between voxel signal intensities and known dielectric properties, which allow us to create dielectric property maps within an MRI and consequently infer ALN properties. Finally, we compare healthy and metastasized ALN dielectric properties within and between patients, and we create an open-access repository of numerical axillary region numerical models which can be used for electromagnetic simulations.

RESULTS

The proposed methodology allowed creating anatomically realistic models of the axillary region, segmenting 80 ALNs and analyzing the corresponding dielectric properties. The estimated relative permittivity of those ALNs ranged from 16.6 to 49.3 at 5 GHz. We observe there is a high variability of dielectric properties of ALNs, which can be mainly related to the ALN size and, consequently, its composition. We verified an average dielectric contrast of 29% between healthy and metastasized ALNs. Our repository comprises 10 numerical models of the axillary region, from five patients, with variable number of metastasized ALNs and body mass index.

CONCLUSIONS

The observed contrast between healthy and metastasized ALNs is a good indicator for the feasibility of a MWI system aiming to diagnose ALNs. This paper presents new contributions regarding anatomical modeling and dielectric properties' characterization, in particular for axillary region applications.

Invasive Lobular Breast Cancer: Data to Support Surgical Decision Making

BACKGROUND

Invasive lobular carcinoma (ILC) is thought be a unique entity with higher rates of multifocal/multicentric and bilateral disease. This study aimed to evaluate the true extent of the disease, risk of bilaterality, lymph node involvement, and impact of preoperative imaging to help guide surgical decision making.

METHODS

A retrospective analysis identified patients treated for ILC between 2004 and 2017. Clinical staging and pathologic results were compared. Follow-up details including local recurrence, contralateral breast cancer (CBC), and survival outcomes were evaluated.

RESULTS

The study identified 692 patients with ILC, including 43 patients (6%) with a diagnosis of CBC and 232 patients (33%) with a diagnosis of multifocal/multicentric disease at presentation. Preoperative magnetic resonance imaging (MRI) led to an identification of additional disease in 20% of the patients. Preoperative MRI resulted in a more accurate prediction of tumor size staging but did not improve the discordance between clinical and pathologic nodal staging. Overall, the rate of imaging occult lymph node disease was 24%. At the 6-year follow-up evaluation, a local recurrence had developed in 2.3%, a CBC in 2.3, and a distant metastasis in 9.4% of the patients. The overall survival rate was 96% at 3 years and 91% at 5 years.

CONCLUSIONS

Invasive lobular carcinoma is a distinct subset of cancer that poses a diagnostic staging challenge. The results of this study favor MRI for accurate tumor staging and for improving detection of multicentricity and bilaterality. However, clinicians should be aware of the higher likelihood of occult lymph node involvement with ILC and subsequent early metastasis.

The combination of contrast-enhanced ultrasonography with blue dye for sentinel lymph node detection in clinically negative node breast cancer

PURPOSE

The aim of this prospective study was to evaluate the value of the combination of contrast-enhanced ultrasonography (CEUS) and blue dye (BD) for SLN detection in patients with clinically negative node breast cancer.

METHODS

Patients with clinically negative node breast cancer were randomized into two cohorts for SLN biopsy (SLNB): the combination method cohort using CEUS and BD together, and the single BD method cohort. Standard axillary lymph node dissection was performed if any of the SLNs confirmed positive by pathology. The identification rate, the number of SLNs removed and recurrence-free survival (RFS) rates were evaluated between two cohorts. In addition, we assessed the sensitivity, specificity, accuracy, false-negative rate of CEUS for diagnosis of SLNs based on patterns of CEUS enhancement.

RESULTS

144 consecutive patients with clinically negative node breast cancer were randomized into two cohorts. Each cohort consisted of 72 cases. In the combination method cohort, contrast-enhanced lymphatic vessels were clearly visualized and SLNs were accurately localized in 72 cases. The identification rate and the mean number of SLNs detected by the combination method were 100% (72/72) and 3.26 (1-9), respectively. In contrast, in the single BD method cohort, SLNs in 69 cases were successfully identified. The identification rate and the mean number of SLNs using BD alone were 95.8% (69/72) and 2.21 (1-4), respectively. According to patterns of CEUS enhancement, the sensitivity, specificity, accuracy, and the FNR of CEUS for SLN diagnosis were 69.2%, 96.6%, 91.7%, and 30.8%, respectively. After a median follow-up of 50 months for the combination method cohort and 51 months for the blue dye alone cohort, five patients in the combination method cohort and nine in the blue dye alone cohort had recurrence. RFS rates showed no significant difference (P = 0.26) between two cohorts.

CONCLUSION

The combination of CEUS and BD is more effective than BD alone for SLNB in clinically negative node patients with an identification rate as high as 100%. Use of BD and CEUS in combination may provide the possibility of a non-radioactive alternative method for SLNB in centers without access to radioisotope.

Favorable outcome with sentinel lymph node biopsy alone after neoadjuvant chemotherapy in clinically node positive breast cancer at diagnosis: Turkish Multicentric NEOSENTI-TURK MF-18-02-study

PURPOSE

Factors affecting local outcome were evaluated in patients with clinically node-positive (cN+) breast cancer at diagnosis, who underwent sentinel lymph node biopsy (SLNB) alone after neoadjuvant chemotherapy (NAC).

METHODS

Between 2004 and 2018, 303 cytopathology-proven cN (+) patients in a multicentric registry, who received NAC and underwent SLNB alone were analysed. All patients had regional nodal irradiation.

RESULTS

Median age was 46 (23-70). Of those, 211 patients had ypN0 disease (69.6%), whereas 92 patients had ypN (+) disease including 19 (20.6%) isolated tumor cells (ITC), 33 micrometastases (35.9%) and 40 macrometastases (43.5%). At a median follow-up of 36 months (24-172), one patient (0.3%) with macrometastatic SLN was found to have locoregional recurrence as chest wall and supraclavicular LN metastases at the 60th month. Five-year disease-free survival (DFS) and disease specific survival (DSS) rates were 87% and 95%, respectively. Patients with cT3/4 (HR = 2.41, 95% CI; 1.14-5.07), non-luminal molecular pathology (HR = 2.60, 95% CI, 1.16-5.82), and non-pCR in the breast (HR = 2.11, 95% CI, 0.89-5.01) were found to have an increased HR compared to others in 5-year DFS. However, no difference could be found between ypN0 and ypN ITC and micrometastasis (HR = 1.23, 95% CI, 0.44-3.47), whereas there was a slight increase in HR of patients with ypN macrometastasis versus ypN0 (HR = 1.91, 95% CI, 0.63-5.79).

CONCLUSION

ALND could be avoided in meticulously selected cN (+) patients who underwent SLNB after NAC having breast and/or nodal pCR, cT1-2, or low volume residual nodal disease with luminal pathology, as long as axillary radiotherapy is provided.

Prediction of the number of metastatic axillary lymph nodes in breast cancer by radiomic signature based on dynamic contrast-enhanced MRI

BACKGROUND

The number of metastatic axillary lymph nodes (ALNs) play a crucial role in the staging, prognosis and therapy of patients with breast cancer.

PURPOSE

To predict the number of metastatic ALNs in breast cancer via radiomics.

MATERIAL AND METHODS

We enrolled 197 patients with breast cancer who underwent dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). A total of 3386 radiomic features were extracted from the early- and delayed-phase subtraction images. To classify the number of metastatic ALNs, logistic regression was used to develop a radiomic signature and nomogram.

RESULTS

The radiomic signature were constructed to distinguish the N₀ group from the N₊ (metastatic ALNs ≥ 1) group, which yielded area under the curve (AUC) values of 0.82 and 0.81 in the training and test group, respectively. Based on the radiomic signature and BI-RADS category, a nomogram was further developed and showed excellent predictive performance with AUC values of 0.85 and 0.89 in the training and test groups, respectively. Another radiomic signature was constructed to distinguish the N₁ (1-3 ALNs) group from the N_2-3 (≥4 metastatic ALNs) group and showed encouraging performance with AUC values of 0.94 and 0.84 in training and test group, respectively.

CONCLUSIONS

We developed a nomogram and a radiomic signature that can be used to predict ALN metastasis and distinguish the N₁ from the N_2-3 group. Both nomogram and radiomic signature may be potential tools to assist clinicians in assessing ALN metastasis in patients with breast cancer.

Determining the axillary nodal status with four current imaging modalities including 18F-FDG PET/MRI in newly diagnosed breast cancer: A comparative study using histopathology as reference standard

Purpose: To compare breast magnetic resonance imaging (MRI), thoracal MRI, thoracal 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET)/MRI and axillary sonography for the detection of axillary lymph node metastases in women with newly diagnosed breast cancer. Materials and Methods: This prospective double-center study included patients with newly diagnosed breast cancer between March 2018 and December 2019. Patients underwent thoracal (18F-FDG PET/)MRI, axillary sonography, and dedicated prone breast MRI. Datasets were evaluated separately regarding nodal status (nodal+ vs. nodal-). Histopathology served as reference standard in all patients. The diagnostic performance of breast MRI, thoracal MRI, thoracal PET/MRI and axillary sonography in detecting nodal positive patients was tested by creating receiver-operating-characteristic curves (ROC) with a calculated area under the curve (AUC). Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were calculated for all four modalities. A McNemar test was used to assess differences. Results: 112 female patients (mean age 53.04 ± 12.6 years) were evaluated. Thoracal PET/MRI showed the highest ROC-AUC with a value of 0.892. The AUC for breast MRI, thoracal MRI and sonography were 0.782, 0.814 and 0.834, respectively. Differences between thoracal PET/MRI and axillary sonography, thoracal MRI and breast MRI were statistically significant (PET/MRI vs. axillary sonography, P = 0.01; PET/MRI vs. thoracal MRI, P = 0.02; PET/MRI vs. breast MRI, P = 0.03). PET/MRI showed the highest sensitivity (81.8%, 36/44) (95%-CI: 67.29-91.81%) while axillary sonography had the highest specificity (98.5%, 65/66), 95%-CI: 91.84-99.96%). Conclusion: 18F-FDG PET/MRI outperforms axillary sonography, breast MRI and thoracal MRI in determining the axillary lymph node status. In a clinical setting, the combination of 18F-FDG PET/MRI and axillary sonography might be considered to provide even more accuracy in diagnosis.

Preoperative Axillary Ultrasound Helps in the Identification of a Limited Nodal Burden in Breast Cancer Patients

Since the Z0011 trial, the clinical evaluation of axillary status has been redirected to predicting nodal tumor burden rather than nodal metastases. Our study aimed to evaluate the value of clinicopathological factors and axillary ultrasound (US) for the prediction of a high nodal burden (≥3 metastatic lymph nodes) in breast cancer patients. A total of 532 consecutive patients who underwent preoperative axillary US and subsequent surgery for clinical T1-2 breast cancer with a final pathologic analysis were included. Clinical and pathologic variables were retrospectively evaluated. Univariate and multivariate statistical analyses were performed to identify the variables that were associated with a high nodal burden. Among the 532 patients, 110 (20.7%) had a high axillary nodal burden and 422 (79.3%) had a limited nodal burden. The multivariate analysis showed that suspicious axillary US findings (P < 0.001), clinical T2 stage (P = 0.011), the presence of lymphovascular invasion (P < 0.001), and estrogen receptor positivity (P < 0.001) were significantly associated with a high nodal burden. Patients with negative axillary US findings seldom had a high nodal burden, with a negative predictive value of 93.0% (294/316). Patients with suspicious axillary US findings, clinical T2 stage, lymphovascular invasion, and estrogen receptor positivity are more likely to have a high nodal burden, which may provide additional information for the treatment plan of breast cancer patients. Preoperative axillary US helps identify a limited nodal burden in breast cancer patients and has implications for axillary lymph node dissection and adjuvant treatment.

Radiomic features of axillary lymph nodes based on pharmacokinetic modeling DCE-MRI allow preoperative diagnosis of their metastatic status in breast cancer

OBJECTIVE

To study the feasibility of use of radiomic features extracted from axillary lymph nodes for diagnosis of their metastatic status in patients with breast cancer.

MATERIALS AND METHODS

A total of 176 axillary lymph nodes of patients with breast cancer, consisting of 87 metastatic axillary lymph nodes (ALNM) and 89 negative axillary lymph nodes proven by surgery, were retrospectively reviewed from the database of our cancer center. For each selected axillary lymph node, 106 radiomic features based on preoperative pharmacokinetic modeling dynamic contrast enhanced magnetic resonance imaging (PK-DCE-MRI) and 5 conventional image features were obtained. The least absolute shrinkage and selection operator (LASSO) regression was used to select useful radiomic features. Logistic regression was used to develop diagnostic models for ALNM. Delong test was used to compare the diagnostic performance of different models.

RESULTS

The 106 radiomic features were reduced to 4 ALNM diagnosis-related features by LASSO. Four diagnostic models including conventional model, pharmacokinetic model, radiomic model, and a combined model (integrating the Rad-score in the radiomic model with the conventional image features) were developed and validated. Delong test showed that the combined model had the best diagnostic performance: area under the curve (AUC), 0.972 (95% CI [0.947-0.997]) in the training cohort and 0.979 (95% CI [0.952-1]) in the validation cohort. The diagnostic performance of the combined model and the radiomic model were better than that of pharmacokinetic model and conventional model (P<0.05).

CONCLUSION

Radiomic features extracted from PK-DCE-MRI images of axillary lymph nodes showed promising application for diagnosis of ALNM in patients with breast cancer.