Evaluating Radar Reflector Localisation in Targeted Axillary Dissection in Patients Undergoing Neoadjuvant Systemic Therapy for Node-Positive Early Breast Cancer: A Systematic Review and Pooled Analysis

SAVI SCOUT® or radar reflector localisation (RRL) has proven accurate in localising non-palpable breast and axillary lesions, with minimal interference with MRI. Targeted axillary dissection (TAD), combining marked lymph node biopsy (MLNB) and sentinel lymph node biopsy (SLNB), is becoming a standard post-neoadjuvant systemic therapy (NST) for node-positive early breast cancer. Compared to SLNB alone, TAD reduces the false negative rate (FNR) to below 6%, enabling safer axillary surgery de-escalation. This systematic review evaluates RRL's performance during TAD, assessing localisation and retrieval rates, the concordance between MLNB and SLNB, and the pathological complete response (pCR) in clinically node-positive patients post-NST. Four studies (252 TAD procedures) met the inclusion criteria, with a 99.6% (95% confidence [CI]: 98.9-100) successful localisation rate, 100% retrieval rate, and 81% (95% CI: 76-86) concordance rate between SLNB and MLNB. The average duration from RRL deployment to surgery was 52 days (range:1-202). pCR was observed in 42% (95% CI: 36-48) of cases, with no significant migration or complications reported. Omitting MLNB or SLNB would have under-staged the axilla in 9.7% or 3.4% (p = 0.03) of cases, respectively, underscoring the importance of incorporating MLNB in axillary staging post-NST in initially node-positive patients in line with the updated National Comprehensive Cancer Network (NCCN) guidelines. These findings underscore the excellent efficacy of RRL in TAD for NST-treated patients with positive nodes, aiding in accurate axillary pCR identification and the safe omission of axillary dissection in strong responders.

Ultrasound Radiomics Features to Identify Patients With Triple-Negative Breast Cancer: A Retrospective, Single-Center Study

OBJECTIVES

Patients with triple-negative breast cancer (TNBC) exhibit a fast tumor growth rate and poor survival outcomes. In this study, we aimed to develop and compare intelligent algorithms using ultrasound radiomics features in addition to clinical variables to identify patients with TNBC prior to histopathologic diagnosis.

METHODS

We used single-center, retrospective data of patients who underwent ultrasound before histopathologic verification and subsequent neoadjuvant systemic treatment (NAST). We developed a logistic regression with an elastic net penalty algorithm using pretreatment ultrasound radiomics features in addition to patient and tumor variables to identify patients with TNBC. Findings were compared to the histopathologic evaluation of the biopsy specimen. The main outcome measure was the area under the curve (AUC).

RESULTS

We included 1161 patients, 813 in the development set and 348 in the validation set. Median age was 50.1 years and 24.4% (283 of 1161) had TNBC. The integrative model using radiomics and clinical information showed significantly better performance in identifying TNBC compared to the radiomics model (AUC: 0.71, 95% confidence interval [CI]: 0.65-0.76 versus 0.64, 95% CI: 0.57-0.71, P = .004). The five most important variables were cN status, shape surface volume ratio (SA:V), gray level co-occurrence matrix (GLCM) correlation, gray level dependence matrix (GLDM) dependence nonuniformity normalized, and age. Patients with TNBC were more often categorized as BI-RADS 4 than BI-RADS 5 compared to non-TNBC patients (P = .002).

CONCLUSION

A machine learning algorithm showed promising potential to identify patients with TNBC using ultrasound radiomics features and clinical information prior to histopathologic evaluation.

Applying Explainable Machine Learning Models for Detection of Breast Cancer Lymph Node Metastasis in Patients Eligible for Neoadjuvant Treatment

BACKGROUND

Due to recent changes in breast cancer treatment strategy, significantly more patients are treated with neoadjuvant systemic therapy (NST). Radiological methods do not precisely determine axillary lymph node status, with up to 30% of patients being misdiagnosed. Hence, supplementary methods for lymph node status assessment are needed. This study aimed to apply and evaluate machine learning models on clinicopathological data, with a focus on patients meeting NST criteria, for lymph node metastasis prediction.

METHODS

From the total breast cancer patient data (n = 8381), 719 patients were identified as eligible for NST. Machine learning models were applied for the NST-criteria group and the total study population. Model explainability was obtained by calculating Shapley values.

RESULTS

In the NST-criteria group, random forest achieved the highest performance (AUC: 0.793 [0.713, 0.865]), while in the total study population, XGBoost performed the best (AUC: 0.762 [0.726, 0.795]). Shapley values identified tumor size, Ki-67, and patient age as the most important predictors.

CONCLUSION

Tree-based models achieve a good performance in assessing lymph node status. Such models can lead to more accurate disease stage prediction and consecutively better treatment selection, especially for NST patients where radiological and clinical findings are often the only way of lymph node assessment.

Nanoparticle albumin-bound paclitaxel is superior to liposomal paclitaxel in the neoadjuvant treatment of breast cancer

Aim: The present study aimed to retrospectively compare the efficacy and safety between liposomal paclitaxel (Lps-P) and nanoparticle albumin-bound paclitaxel (Nab-P) in neoadjuvant systemic treatment (NST) of breast cancer. Materials & methods: 235 patients who were diagnosed with invasive breast cancer and then received dose-dense NST with epirubicin and cyclophosphamide followed by paclitaxel were enrolled. Results: Nab-P has an advantage in improving the total and axillary-only pathologic complete response rate over Lps-P. Although Nab-P can cause a higher incidence and severity of peripheral sensory neuropathy (PSN), most symptoms are temporary and reversible. In the Lps-P group, the proportion of patients with residual irreversible PSN is larger. Conclusion: Nab-P might be superior to Lps-P in NST of breast cancer.

Retrospective comparisons of nanoparticle albumin-bound paclitaxel and docetaxel neoadjuvant regimens for breast cancer

Aim: To compare the efficacy and safety of 2-weekly nanoparticle albumin-bound paclitaxel (nP) and 3-weekly docetaxel regimens as neoadjuvant systemic therapy (NST) for breast cancer. Materials & methods: Patients (n = 201) received NST comprising either dose-dense epirubicin and cyclophosphamide followed by 2-weekly nP (n = 104) or 3-weekly courses of epirubicin and cyclophosphamide followed by docetaxel (n = 97). Results: Higher pathological complete response rates were achieved by the nP group. Subgroup analysis showed that the nP-based regimen achieved higher pathological complete response rates in patients with triple-negative tumor cells and high Ki67 levels. However, grades 3-4 peripheral sensory neuropathies were more frequent in the nP group. Conclusion: The 2-weekly nP-based regimen might be a better choice of NST for patients with breast cancer.

Feasibility of radioguided occult lesion localization of clip-marked lymph nodes for tailored axillary treatment in breast cancer patients treated with neoadjuvant systemic therapy

Background

Selective removal of initially tumor-positive axillary lymph nodes in breast cancer patients who underwent neoadjuvant systemic treatment (NST) improves the accuracy of nodal staging and provides the opportunity for more tailored axillary treatment. This study evaluated whether radioguided occult lesion localization (ROLL) of clip-marked lymph nodes is feasible in clinical practice.

Methods

Prior to NST, a clip marker was placed inside a proven tumor-positive lymph node in all breast cancer patients (cTis-4N1-3 M0). After NST, technetium-99m-labeled macroaggregated albumin was injected in the clip-marked lymph nodes. The next day, these ROLL-marked nodes were selectively removed at surgery to evaluate the pathological response of the axilla.

Results

Thirty-seven patients (38 axillae) underwent clip insertion. After NST, the clip was visible by ultrasound in 36 procedures (95%). In the other two patients, the ROLL-node injection was performed in a sonographically suspicious unclipped node (1), and near the clip under computed tomography guidance (1). Initial surgery successfully identified the ROLL-marked node with clip in 33 procedures (87%). Removed specimens in the other five procedures contained only the sonographically suspicious tumor-positive unclipped node (1), a node with signs of complete response but no clip (2), a clip without node (1), and tissue without node nor clip, and a second successful ROLL-node procedure was performed (1). Overall, 10 ROLL-marked nodes had no residual disease.

Conclusions

This study demonstrates that the ROLL procedure to identify clip-marked lymph nodes is feasible. This facilitates selective removal at surgery and may tailor axillary treatment in patients treated with NST.