Non-invasive predictors of axillary lymph node burden in breast cancer: a single-institution retrospective analysis

Sentinel Lymph Node Biopsy Predicts Non-Sentinel Lymph Node Metastases and Supports Omission of Axillary Lymph Node Dissection in Breast Cancer Patients

Background: Current international guidelines recommend omitting axillary lymph node dissection (ALND) based on sentinel lymph node biopsy (SLNB) in early-stage breast cancer patients. However, the evolving landscape of axillary management highlights the need to balance diagnostic accuracy with minimizing invasiveness. The possibility of omitting SLNB itself should also be considered. Methods: In this study, we have evaluated the feasibility of omitting SLNB in a total of 1044 clinically node-negative (cN (-)) breast cancer patients whose SLN status was determined by histopathology and one-step nucleic acid amplification (OSNA) after SLNB. We also analyzed SLN-positive cases to explore the association between non-SLN (NSLN) metastatic status and various biomarkers. We predicted the metastatic status of NSLNs based on patient data using a nomogram and further assessed the prevalence of macro- and micro-metastatic SLN, along with the NSLN status in SLNB cases. Results: Of the 644 cN (-) cases, approximately 70% of SLN-positive cases were NSLN negative, suggesting that ALND could be omitted. SLN (+) was detected approximately 7% more often by OSNA than by histopathology, suggesting that OSNA detection may be an overdiagnosis. Although NSLN-positive cases represented only 5.9% of the 581 cN (-) cases and, therefore, ALND could be omitted, it may be difficult to omit the SLNB itself as the SLN macro-metastasis was 12.5%. Biomarker analysis showed a significant correlation between total tumor load and metastatic SLN copy number with NSLN metastatic status. Based on these tumor characteristics, the nomogram predicted NSLN-positive rates very well. Conclusions: Thus, omitting SLNB itself carries the risk of missing high-frequency macro-metastatic SLN-positive cases and losing important SLN-related information that can predict NSLN metastases. Therefore, SLNB, which provides not only SLN status but also NSLN metastases, is necessary for reassurance in omitting ALND.

Predictive value of ultrasound assessment of axillary and brachial artery parameters for lymph node metastasis in breast cancer patients

OBJECTIVE

This study aimed to assess the predictive value of ultrasound assessment of axillary and brachial artery parameters for lymph node metastasis (LNM) in breast cancer (BRCA) patients.

METHODS

The clinical data of 172 cancer patients were reviewed, and the patients were stratified into two groups based on the presence or absence of axillary LNM. Ultrasound assessment was employed to evaluate axillary and brachial artery parameters using specific techniques, and arterial characteristics were analyzed.

RESULTS

Significant differences were observed in the ultrasound parameters of both axillary and brachial arteries between the non-LNM and LNM groups. Specifically, axillary and brachial artery diameters and resistive index exhibited significant differences and correlations with axillary LNM. Furthermore, molecular markers such as human epidermal growth factor receptor 2 (HER2) status, estrogen receptor (ER) status, and progesterone receptor (PR) status were found to be significantly correlated with LNM. Additionally, a nomogram was constructed, demonstrating the predictive value of the integrated arterial parameters. The combined model, incorporating axillary and brachial artery parameters, exhibited a higher predictive capability for axillary LNM compared to individual arterial parameters (AUC = 0.984).

CONCLUSION

Ultrasound assessment of axillary and brachial artery parameters, in conjunction with molecular markers, holds promise as a non-invasive tool for predicting LNM in BRCA patients. The observed correlations provide insights into the potential clinical relevance of arterial parameters in risk stratification and treatment planning. Further research in larger, prospective cohorts is warranted to validate the findings and enhance the precision of BRCA management.

Human Leukocyte Antigen Class I Expression and Natural Killer Cell Infiltration and Its Correlation with Prognostic Features in Luminal Breast Cancers

Purpose

The aim of this study was to determine whether low HLA-I expression and NK cells infiltration are related to prognostic features in breast cancer, as observed in cancers in other locations and non-hormone dependent breast cancers. Particularly, we explored their relation to infiltrated axillary lymph nodes (ALNs), with the aim of finding new predictors helping to decide the extent of axillary surgery.

Patients and Methods

We conducted a retrospective correlational analysis of 35 breast cancers from 35 breast cancer patients showing axillary infiltration at diagnosis and with upfront surgery. HLA-I H-score and the number of NK cells x 50 high power fields (HPF) in the biopsy specimen were correlated with pathological variables of the surgical specimen: number of infiltrated ALNs, tumor size, histological type, the presence of ductal carcinoma in situ, focality, histological grade, necrosis, lymphovascular and perineural invasion, Her2Neu status, and the percentages of tumor-infiltrating lymphocytes (TILs), estrogen receptor, progesterone receptor, ki67, and p53.

Results

All tumors showed hormone receptor expression and three of them Her2Neu positivity. A positive correlation (p=0.001**) was found between HLA-I H-score and TILs and Ki67 expression. HLA H-score increased with histological grade and was higher in unifocal than in multifocal disease (p=0.044 and p=0.011, respectively). No other correlations were found.

Conclusion

High HLA-I H-score values correlated with features of poor prognosis in this cohort of luminal breast tumors, but not with infiltrated ALNs. This finding highlights the differences between luminal breast cancer, and cancers in other locations and non-hormone dependent breast cancers, in which low HLA-I expression tends to be associated with poor prognostic features.

Non-invasive prediction model of axillary lymph node status in patients with early-stage breast cancer: a feasibility study based on dynamic contrast-enhanced-MRI radiomics

OBJECTIVES

Accurate axillary evaluation plays an important role in prognosis and treatment planning for breast cancer. This study aimed to develop and validate a dynamic contrast-enhanced (DCE)-MRI-based radiomics model for preoperative evaluation of axillary lymph node (ALN) status in early-stage breast cancer.

METHODS

A total of 410 patients with pathologically confirmed early-stage invasive breast cancer (training cohort, N = 286; validation cohort, N = 124) from June 2018 to August 2022 were retrospectively recruited. Radiomics features were derived from the second phase of DCE-MRI images for each patient. ALN status-related features were obtained, and a radiomics signature was constructed using SelectKBest and least absolute shrinkage and selection operator regression. Logistic regression was applied to build a combined model and corresponding nomogram incorporating the radiomics score (Rad-score) with clinical predictors. The predictive performance of the nomogram was evaluated using receiver operator characteristic (ROC) curve analysis and calibration curves.

RESULTS

Fourteen radiomic features were selected to construct the radiomics signature. The Rad-score, MRI-reported ALN status, BI-RADS category, and tumour size were independent predictors of ALN status and were incorporated into the combined model. The nomogram showed good calibration and favourable performance for discriminating metastatic ALNs (N + (≥1)) from non-metastatic ALNs (N0) and metastatic ALNs with heavy burden (N + (≥3)) from low burden (N + (1-2)), with the area under the ROC curve values of 0.877 and 0.879 in the training cohort and 0.859 and 0.881 in the validation cohort, respectively.

CONCLUSIONS

The DCE-MRI-based radiomics nomogram could serve as a potential non-invasive technique for accurate preoperative evaluation of ALN burden, thereby assisting physicians in the personalized axillary treatment for early-stage breast cancer patients.

ADVANCES IN KNOWLEDGE

This study developed a potential surrogate of preoperative accurate evaluation of ALN status, which is non-invasive and easy-to-use.

Machine Learning-Based Breast Tumor Ultrasound Radiomics for Pre-operative Prediction of Axillary Sentinel Lymph Node Metastasis Burden in Early-Stage Invasive Breast Cancer

OBJECTIVE

The aim of the work described here was to assess the application of ultrasound (US) radiomics with machine learning (ML) classifiers to the prediction of axillary sentinel lymph node metastasis (SLNM) burden in early-stage invasive breast cancer (IBC).

METHODS

In this study, 278 early-stage IBC patients with at least one SLNM (195 in the training set and 83 in the test set) were studied at our institution. Pathologic SLNM burden was used as the reference standard. The US radiomics features of breast tumors were extracted by using 3D-Slicer and PyRadiomics software. Four ML classifiers-linear discriminant analysis (LDA), support vector machine (SVM), random forest (RF) and decision tree (DT)-were used to construct radiomics models for the prediction of SLNM burden. The combined clinicopathologic-radiomics models were also assessed with respect to sensitivity, specificity, accuracy and areas under the curve (AUCs).

RESULTS

Among the US radiomics models, the SVM classifier achieved better predictive performance with an AUC of 0.920 compared with RF (AUC = 0.874), LDA (AUC = 0.835) and DT (AUC = 0.800) in the test set. The clinicopathologic model had low efficacy, with AUCs of 0.678 and 0.710 in the training and test sets, respectively. The combined clinicopathologic (C) factors and SVM classifier (C + SVM) model improved the predictive ability with an AUC of 0.934, sensitivity of 86.7%, specificity of 89.9% and accuracy of 91.0% in the test set.

CONCLUSION

ML-based US radiomics analysis, as a novel and promising predictive tool, is conducive to a precise clinical treatment strategy.

Predictive value for axillary lymph node metastases in early breast cancer: Based on contrast-enhanced ultrasound characteristics of the primary lesion and sentinel lymph node

OBJECTIVE

To evaluate the value of contrast-enhanced ultrasound (CEUS) characteristics based on primary lesion combined with lymphatic contrast-enhanced ultrasound (LCEUS) patterns of SLN in predicting axillary lymph node metastasis (ALNM) with T1-2N0 breast cancer.

METHODS

A retrospective study was conducted in 118 patients with clinically confirmed T1-2N0 breast cancer. Conventional ultrasound (CUS) and CEUS characteristics of the primary lesion and enhancement patterns of SLN were recorded. The risk factors associated with ALNM were selected by univariate and binary logistic regression analysis, and the receiver operating characteristic (ROC) curve was drawn for the evaluation of predictive ALNM metastasis performance.

RESULTS

Univariate analysis showed that age, HER-2 status, tumor size, nutrient vessels, extended range of enhancement lesion, and the enhancement patterns of SLN were significant predictive features of ALNM. Further binary logistic regression analysis indicated that the extended range of enhancement lesion (p <  0.001) and the enhancement patterns of SLN (p <  0.001) were independent risk factors for ALNM. ROC analysis showed that the AUC of the combination of these two indicators for predicting ALNM was 0.931 (95% CI: 0.887-0.976, sensitivity: 75.0%, specificity: 99.8%).

CONCLUSION

The CEUS characteristics of primary lesion combined with enhancement patterns of SLN are highly valuable in predicting ALNM and can guide clinical axillary surgery decision-making in early breast cancer.