Axillary Lymph Node Metastasis Research Articles

Receptor expression testing of the synchronous axillary nodal metastasis in early-stage breast cancer: Comparison of primary tumor and its draining nodes.

e12542 Background: Early stage (Stage I-III) human epidermal receptor -2 (HER2) positive and triple negative breast cancers (TNBC) tend to behave more aggressively than hormone positive breast cancers with higher and earlier incidences of recurrences. Oftentimes, testing for receptor subtypes is done only on the primary tumor and not on the synchronous axillary metastasis, with the assumption that the axillary metastasis is congruous to the primary lesion. Current guidelines also do not specify a requisite to do this testing. Information about the receptor expression types is fundamental for the treatment of breast cancer. We aimed to determine if there is significant discordance between the primary and ipsilateral axillary metastasis in early-stage breast cancer. Methods: We conducted a retrospective analysis via electronic medical record review of a large institutional database from 2008 to 2020. Sample for this study included all patients with a diagnosis of early-stage breast cancer (Stage I - III) with triple negative or HER2 positive tumors. Data on demographics, comorbidities, receptor status, staging, and mortality were collected. Results: Electronic charts from 448 patients were reviewed. 140 patients had axillary node positive disease. 40 (29%) of the 140 patients underwent testing for receptors subtypes on both primary and axillary nodal metastasis. 8 (20%) of the 40 patients had discordance in their receptor subtype expressed in their respective axillary nodal metastasis. 32 (80%) of the 40 patients had axillary receptor expression congruous to the primary tumor. At the time of our review, there was no statistically significant mortality between discordant compared to concordant group (13% vs 20%; p=0.54). 2 patients were lost to follow up. Conclusions: Our data suggests that one in every five axillary metastatic breast cancers can have receptor subtypes discordant compared to the receptors expressed on the primary tumor. Knowledge about any such discordance between the primary and respective axillary metastasis has a consequential impact on the treatment approach for early-stage breast cancer. This is especially true if the patient’s nodal metastatic lesions indeed prove HER2 receptor positivity or do not express any receptors (TNBC). Consequently, patients may be being deprived of benefits from advances in treatment of these cancers, such as targeted therapies towards HER2 receptor, hormonal treatments when indicated, or the utilization of neoadjuvant chemotherapy. A strong case can be thus made to revise current standards to include receptor testing on all primary and synchronous axillary metastasis. Further testing is needed to validate our findings.

Intraoperative diagnosis of breast cancer metastasis in axillary lymph nodes: Cytological Smear vs. Frozen Section

BackgroundIntraoperative evaluation of axillary lymph nodes is sometimes required to determine the extent of surgery. In this study, we wished to assess the reliability of cytologic smear (CS) in determining lymph node involvement with tumor. Theoretically, CS provides more substance for examination than touch-imprint cytology and is faster to perform than frozen section (FS). We hypothesized that CS sensitivity for tumor cell detection in the lymph nodes would be similar to FS, at least 0.90. MethodsThis was a retrospective observational study at the Rambam Health Care Campus (January, 2013-June, 2020). Lymph nodes underwent intraoperative evaluation using either CS or FS, based on the availability of a cytologist at the time of the examination. Both intraoperative evaluations were compared to the final pathology following fixation with formalin. ResultsEighty-eight patients undergoing intraoperative analysis were analyzed (51 CS, 37 FS). False-negative tests were recorded in only 1 patient evaluated by each of the 2 methods. This resulted in sensitivity 0.91 (95%CI 0.59, 1.00) for CS and 0.88 (95%CI 0.47, 1.00) for FS, specificity 1.00 (95%CI 0.91, 1.00) for CS and 1.00 (95%CI 0.88, 1.00) for FS, positive predictive value 1.00 (95%CI 0.69, 1.00) for CS and 1.00 (95%CI 0.59, 1.00) for FS, and negative predictive value 0.98 (95%CI 0.87, 1.00) for CS and 0.97 (95%CI 0.83, 1.00) for FS. ConclusionsThe sensitivity of the CS in this study is comparable to that of FS and due to shorter analysis time required is the preferred method at our institution.

Meta-analysis on axillary lymph node metastasis rate in ductal carcinoma insitu with microinvasion.

To address the question of axillary lymph node staging in ductal carcinoma insitu with microinvasion (DCIS-MI), we retrospectively evaluated axillary lymph nodes metastasis (ALNM) rate in a cohort of postsurgical DCIS-MI patients. By analyzing these data, we aimed to generate clinically relevant insights to inform treatment decision-making for this patient population. A systematic search was conducted on PubMed, Web of Science, Embase, The Cochrane Library, CNKI, Wanfang Database, Wipe, and China Biomedical Literature Database to identify relevant publications in any language. All the analyses were performed using Stata 16.0 software. Among the 28 studies involving 8279 patients, the pooled analysis revealed an ALNM rate of 8% (95% CI, 7% to 10%) in patients with DCIS-MI. Furthermore, the rates of axillary lymph node macrometastasis, micrometastasis, and ITC in patients with DCIS-MI were 2% (95% CI, 2% to 3%), 3% (95% CI, 2% to 4%), and 2% (95% CI, 1% to 3%), respectively. Moreover, 13 studies investigated the non-sentinel lymph node (Non-SLN) metastasis rate, encompassing a total of 1236 DCIS-MI cases. The pooled analysis identified a Non-SLN metastasis rate of 33% (95% CI, 14% to 55%) in patients with DCIS-MI. The SLNB for patients with DCIS-MI is justifiable and could provide a novel therapeutic basis for systemic treatment decisions.

Deep learning-based quantitative assessment of tumor-infiltrating lymphocytes from hematoxylin and eosin-stained slides in triple-negative breast cancer: A prognostic study.

553 Background: Tumor-infiltrating lymphocytes (TILs) are pivotal in predicting responses to preoperative chemotherapy in triple-negative breast cancer. Yet, their clinical adoption as a diagnostic marker is limited by the challenge of achieving high accuracy with objective measures. This study introduces a novel AI-based method to quantitatively assess stromal TILs within the tumor bed using hematoxylin and eosin (H&E)-stained slides, examining their correlation with disease-free survival (DFS) and overall survival (OS). Methods: In this study, 68 cases of breast cancer with axillary lymph node metastasis at the time of resection were selected from a total of 3,902 resections conducted between 2002 and 2016. We then developed an AI-based model to detect the epithelium and lymphocytes (CD3/CD20) from H&E-stained slides. This model was trained using annotated datasets, which included 26,509 images of the epithelium and 12,273 images of lymphocytes. The tumor bed areas were precisely defined by pathologists. Subsequently, the AI model identified epithelial and lymphocyte regions within these predefined areas. The TILs score was then calculated as the ratio of the lymphocyte area to the total stromal area within the tumor bed, excluding the epithelial areas identified by the AI model. Finally, we assessed the prognostic significance of the TILs score for DFS and OS through ROC analysis and log-rank tests. Results: ROC analysis identified an optimal TILs score threshold of 0.0042 for both DFS and OS, with an AUC of 0.743 (95% CI, 0.622-0.864) for DFS and 0.686 (95% CI, 0.559-0.812) for OS. This threshold significantly stratified patients into groups with distinct DFS and OS outcomes (P=0.003 and P=0.008, respectively). Conclusions: AI quantification of TILs offers a promising prognostic tool in triple-negative breast cancer, enhancing the objectivity and clinical utility of pathological analyses. Accurate TILs measurement could revolutionize prognostication and treatment planning.

AI-based quantification of TILs using hematoxylin and eosin and immunohistochemistry-stained slides in triple-negative breast cancer.

e13608 Background: The importance of accurately understanding the status of tumor-infiltrating lymphocytes (TILs) in the preoperative chemotherapy context, for triple-negative breast cancer is underscored, highlighting the dual role of TILs in gauging treatment efficacy and prognostication. However, the difficulty in quantitatively and objectively assessing TILs in clinical practice is acknowledged, noting the barrier to its adoption as a standard diagnostic end-point. This study proposes a novel AI-based method to overcome these challenges, by quantitatively assessing TILs using hematoxylin and eosin (H&E) and immunohistochemistry (IHC) staining, and exploring their prognostic relationship. Methods: In this study, 68 cases of breast cancer with axillary lymph node metastasis at the time of resection, were selected from a total of 3,902 resections conducted between 2002 and 2016. To quantitatively assess TILs, we developed a quantitative assessment method, integrating an AI-based epithelial detection model and non-rigid image registration techniques to align H&E and IHC-stained images for precise measurement of immune markers (CD3, CD8, FOXP3, PD-L1) within the tumor stroma. Using an AI-based model trained on 12,273 annotated epithelial images, the stroma, excluding the epithelium, was identified. The tumor bed was also defined by a pathologist. Finally, we assessed the prognostic significance of the TILs for disease-free survival (DFS) through ROC analysis and log-rank tests. Results: Optimal thresholds for CD3 (0.152 mm²), CD8 (4.8 mm²), FOXP3 (0.25 mm²), and PD-L1 (0.115 mm²) were identified, with corresponding AUCs of 0.678, 0.625, 0.746, and 0.659. Patients with cell counts or areas above these thresholds demonstrated improved DFS (P<0.05 for all markers). Conclusions: These findings suggest that the quantification of immune cells within the tumor bed provides reliable prognostic indicators for recurrence. The AI-based TILs quantification offers a significant advancement in the prognostic assessment of triple-negative breast cancer. In addition, it improves the objectivity and clinical utility of pathological evaluations. This precise measurement of TILs could be transformative in patient prognostication and the formulation of treatment plans.

Efficacy and safety of recombinant humanized anti-HER2 antibody-MMAE conjugate (RC48) in neoadjuvant for HER2-positive breast cancer: A single-arm phase II study.

e12636 Background: Breast cancer patients who achieve pCR after neoadjuvant therapy have a relatively good prognosis. Whether antibody‐drug conjugates (ADCs) can replace traditional chemotherapy combined with dual-target therapy in neoadjuvant therapy still needs to be explored. RC48 is a novel recombinant human anti-HER2 monoclonal antibody conjugated with a microtubule inhibitor (MMAE) via a cleavable linker, which can effectively kill HER2-positive tumors as well as tumors with low or heterogeneous HER2 expression through the bystander effect. The purpose of this study was to evaluate the efficacy and safety of RC48 in neoadjuvant treatment of HER2-positive breast cancer. Methods: Patients with HER2-positive breast cancer (size>20mm) or confirmed axillary lymph node metastasis were eligible and received RC48 at a dose of 2.5mg/kg alone every 2 weeks for 6 cycles followed by surgery. EC was administered postoperatively every 21 days for 4 cycles. Trastuzumab combined with pertuzumab was administered for 1 year in the adjuvant setting. The primary endpoints were ORR and pCR rates. Results: 23 patients had been enrolled, and all of them had completed surgical treatment. The median age was 45(38-70) years. The current results showed that 13.0% (3/23) achieved pCR, 78.3% (18/23) achieved PR, and 8.7% (2/23) experienced PD. The ORR was 91.3%. 42% (5/12) of patients with initially positive axillary lymph nodes turned negative after surgery. 11.8% (2/17) HR-/HER2+ patients achieved pCR. According to the proportion of target lesion reduction, patients were divided into mild response (0-50%) and moderate-to-severe response (51%-100%). In the HR-/HER2+ subtype group, 23.5% (4/17) had a mild response, and 76.5% (13/17) had a moderate-to-severe response. 6 patients with HR+ have been enrolled, and 16.7% (1/6) of them achieved pCR. All of HR+ subtypes had a moderate-to-severe response. No relevant serious (grade 3/4) toxicity has been reported. Conclusions: The present data showed an acceptable pCR rate with RC48 in HER2-positive breast cancer. Although the pCR rate was lower than the traditional chemotherapy combined with targeted therapy, the effect of rapid reduction of tumor volume was better (the moderate-severe remission rate was 82.6%). For HR+/HER2+ breast cancer, the ORR in this study reached 100%, indicating that the novel ADC had a significant inhibitory effect on the crosstalk between ER and HER2. And it was expected that the Destiny Breast 11 study would show similar results. This scheme had a good negative conversion rate for patients with positive axillary lymph nodes. Finally, RC48 provided a neoadjuvant treatment option with low toxicity and high patient compliance for HER2-positive breast cancer. Clinical trial information: NCT05134519 .

A prospective, open-label, single-arm phase II clinical study of inetetamab in combination with pyrotinib and albumin-bound paclitaxel for the neoadjuvant treatment of patients with HER2+ early and locally advanced breast cancer.

e12635 Background: Chemotherapy combined with dual anti-HER2 target therapy has become the standard neoadjuvant therapy regimen for HER2+ breast cancer. Patients achieving pCR have a relatively better prognosis. Inetetamab (Septin) is an innovative HER2 monoclonal antibody drug developed in China. It has been proven to delay the progression of HER2+ metastatic breast cancer patients and bring survival benefits. The purpose of this study was to explore the efficacy and safety of Inetetamab, Pyrotinib and Albumin-bound Paclitaxel in the neoadjuvant treatment of HER2+ breast cancer. Methods: This phase II trial included patients with HER2+ early or locally advanced breast cancer whose tumor size was>20mm or confirmed axillary lymph node metastasis. Patients receive Inetetamab initial dose of 8 mg/kg over 90 minutes IV infusion, followed 6 mg/kg over 30 to 90 minutes IV infusion q3w, Pyrotinib 400 mg orally every day and Albumin-bound Paclitaxel 125mg/m2, IV, D1/8/15, q3w. Patients receive above treatment every 3 weeks for a total of 4 cycles, followed by surgery. After surgery, EC (90mg/m2 of Epirubicin Hydrochloride, 600mg/m2 of Cyclophosphamide, q21 days, 4 cycles) was administered, followed by maintenance treatment with trastuzumab combined with pertuzumab for 1 year. And provide radiotherapy and endocrine therapy to the patient according to the specific situation. The primary endpoint was pathological complete response (pCR). Results: Until February 4, 2024, 18 patients were enrolled, of which 15 had completed surgical treatment. The median age of enrolled patients was 49 years (35-60 years). 66.7% (10 cases) of patients achieved RCB grade 0 (pCR), 26.7% (4 cases) achieved RCB grade I, 6.7% (1 case) achieved RCB grade II. Of the 15 patients, 73.3% (11 cases) were HR+/HER2+, with 72.7% (8 cases) achieved pCR. Among the 4 HR-/HER2+ patients, 50% (2 case) achieved pCR. HR+/HER2+ patients were more likely to achieve pCR than HR-/HER2+ patients. No severe (grade 3/4) toxicity was observed in any patients. Conclusions: Our current data show that the pCR rate in HER2+ breast cancer patients reaches 66.7%, and even 72.7% in HR+ patients. In the previous Neosphere and PEONY studies of the H+P dual target regimen, the pCR of HR+/HER2+patients increased by only 6% and 8.3%, while in the NeoALTTO and PHEDRA studies of the H+L or H+Py regimen, the pCR increased by 18.9% and 17.7%, indicating that the combination of large and small molecules with dual target neoadjuvant therapy for HR+/HER2+ breast cancer was significantly better than that of the monoclonal antibody dual target therapy. Therefore, Inetetamab, Pyrotinib and albumin-bound paclitaxel is a potential effective new adjuvant treatment for HER2+ breast cancer, especially for HR+ patients. Clinical trial information: NCT054444998 .

Axillary Node Metastases of Medullary Thyroid Cancer: A Hallmark of Terminal Disease.

Little is known about axillary node metastasis of medullary thyroid cancer (MTC). To address this, a comparative study of patients with and without axillary node metastases of MTC was conducted. Among 1215 consecutive patients with MTC, 482 patients had node-negative MTC and 733 patients node-positive MTC. Among the 733 patients with node-positive MTC, 4 patients (0.5%) had axillary node metastases, all of which were ipsilateral. Patients with axillary node metastases had 5.7-6.9-fold more node metastases removed, both at the authors' institution (medians of 34.5 vs. 5 metastases; p=0.011) and in total (medians of 57 vs. 10 metastases; p=0.013), developed more frequently distant metastases (3 of 4 vs. 178 of 729 patients, or 75 vs. 24%; p=0.049), specifically to bone (2 of 4 vs. 67 of 729 patients, or 50 vs. 9%; p=0.046) and brain (1 of 4 vs. 4 of 729 patients, or 25 vs. 0.5%; p=0.027), and more often succumbed to cancer-specific death (3 of 4 vs. 52 of 729 patients, or 75 vs. 14%; p=0.005). Altogether, patients with axillary node metastases revealed 4-8-fold more node metastases in the ipsilateral lateral neck (medians of 11 vs. 3 metastases; p=0.021) and in the ipsilateral central neck (medians of 8 vs. 1 metastases; p=0.079) patients without axillary node metastases. Cancer-specific survival of patients with vs. patients without axillary node metastases of MTC was significantly shorter (means of 41 vs. 224 months; plog-rank<0.001). These findings show that patients with axillary node metastases of MTC have massive metastatic dissemination with poor survival.

A nomogram model for predicting the risk of axillary lymph node metastasis in patients with early breast cancer and cN0 status.

Axillary staging is commonly performed via sentinel lymph node biopsy for patients with early breast cancer (EBC) presenting with clinically negative axillary lymph nodes (cN0). The present study aimed to investigate the association between axillary lymph node metastasis (ALNM), clinicopathological characteristics of tumors and results from axillary ultrasound (US) scanning. Moreover, a nomogram model was developed to predict the risk for ALNM based on relevant factors. Data from 998 patients who met the inclusion criteria were retrospectively reviewed. These patients were then randomly divided into a training and validation group in a 7:3 ratio. In the training group, receiver operating characteristic curve analysis was used to identify the cutoff values for continuous measurement data. R software was used to identify independent ALNM risk variables in the training group using univariate and multivariate logistic regression analysis. The selected independent risk factors were incorporated into a nomogram. The model differentiation was assessed using the area under the curve (AUC), while calibration was evaluated through calibration charts and the Hosmer-Lemeshow test. To assess clinical applicability, a decision curve analysis (DCA) was conducted. Internal verification was performed via 1000 rounds of bootstrap resampling. Among the 998 patients with EBC, 228 (22.84%) developed ALNM. Multivariate logistic analysis identified lymphovascular invasion, axillary US findings, maximum diameter and molecular subtype as independent risk factors for ALNM. The Akaike Information Criterion served as the basis for both nomogram development and model selection. Robust differentiation was shown by the AUC values of 0.855 (95% CI, 0.817-0.892) and 0.793 (95% CI, 0.725-0.857) for the training and validation groups, respectively. The Hosmer-Lemeshow test yielded P-values of 0.869 and 0.847 for the training and validation groups, respectively, and the calibration chart aligned closely with the ideal curve, affirming excellent calibration. DCA showed that the net benefit from the nomogram significantly outweighed both the 'no intervention' and the 'full intervention' approaches, falling within the threshold probability interval of 12-97% for the training group and 17-82% for the validation group. This underscores the robust clinical utility of the model. A nomogram model was successfully constructed and validated to predict the risk of ALNM in patients with EBC and cN0 status. The model demonstrated favorable differentiation, calibration and clinical applicability, offering valuable guidance for assessing axillary lymph node status in this population.

Machine Learning Model for Predicting Axillary Lymph Node Metastasis in Clinically Node Positive Breast Cancer Based on Peritumoral Ultrasound Radiomics and SHAP Feature Analysis.

This study seeks to construct a machine learning model that merges clinical characteristics with ultrasound radiomic analysis-encompassing both the intratumoral and peritumoral-to predict the status of axillary lymph nodes in patients with early-stage breast cancer. The study employed retrospective methods, collecting clinical information, ultrasound data, and postoperative pathological results from 321 breast cancer patients (including 224 in the training group and 97 in the validation group). Through correlation analysis, univariate analysis, and Lasso regression analysis, independent risk factors related to axillary lymph node metastasis in breast cancer were identified from conventional ultrasound and immunohistochemical indicators, and a clinical feature model was constructed. Additionally, features were extracted from ultrasound images of the intratumoral and its 1-5 mm peritumoral to establish a radiomics feature formula. Furthermore, by combining clinical features and ultrasound radiomics features, six machine learning models (Logistic Regression, Decision Tree, Support Vector Machine, Extreme Gradient Boosting, Random Forest, and K-Nearest Neighbors) were compared for diagnostic efficacy, and constructing a joint prediction model based on the optimal ML algorithm. The use of Shapley Additive Explanations (SHAP) enhanced the visualization and interpretability of the model during the diagnostic process. Among the 321 breast cancer patients, 121 had axillary lymph node metastasis, and 200 did not. The clinical feature model had an AUC of 0.779 and 0.777 in the training and validation groups, respectively. Radiomics model analysis showed that the model including the Intratumor +3 mm peritumor area had the best diagnostic performance, with AUCs of 0.847 and 0.844 in the training and validation groups, respectively. The joint prediction model based on the XGBoost algorithm reached AUCs of 0.917 and 0.905 in the training and validation groups, respectively. SHAP analysis indicated that the Rad Score had the highest weight in the prediction model, playing a significant role in predicting axillary lymph node metastasis in breast cancer. The predictive model, which integrates clinical features and radiomic characteristics using the XGBoost algorithm, demonstrates significant diagnostic value for axillary lymph node metastasis in breast cancer. This model can provide significant references for preoperative surgical strategy selection and prognosis evaluation for breast cancer patients, helping to reduce postoperative complications and improve long-term survival rates. Additionally, the utilization of SHAP enhancing the global and local interpretability of the model.

A non-invasive preoperative prediction model for predicting axillary lymph node metastasis in breast cancer based on a machine learning approach: combining ultrasonographic parameters and breast gamma specific imaging features.

The most common route of breast cancer metastasis is through the mammary lymphatic network. An accurate assessment of the axillary lymph node (ALN) burden before surgery can avoid unnecessary axillary surgery, consequently preventing surgical complications. In this study, we aimed to develop a non-invasive prediction model incorporating breast specific gamma image (BSGI) features and ultrasonographic parameters to assess axillary lymph node status. Cohorts of breast cancer patients who underwent surgery between 2012 and 2021 were created (The training set included 1104 ultrasound images and 940 BSGI images from 235 patients, the test set included 568 ultrasound images and 296 BSGI images from 99 patients) for the development of the prediction model. six machine learning (ML) methods and recursive feature elimination were trained in the training set to create a strong prediction model. Based on the best-performing model, we created an online calculator that can make a linear predictor in patients easily accessible to clinicians. The receiver operating characteristic (ROC) and calibration curve are used to verify the model performance respectively and evaluate the clinical effectiveness of the model. Six ultrasonographic parameters (transverse diameter of tumour, longitudinal diameter of tumour, lymphatic echogenicity, transverse diameter of lymph nodes, longitudinal diameter of lymph nodes, lymphatic color Doppler flow imaging grade) and one BSGI features (axillary mass status) were selected based on the best-performing model. In the test set, the support vector machines' model showed the best predictive ability (AUC = 0.794, sensitivity = 0.641, specificity = 0.8, PPV = 0.676, NPV = 0.774 and accuracy = 0.737). An online calculator was established for clinicians to predict patients' risk of ALN metastasis ( https://wuqian.shinyapps.io/shinybsgi/ ). The result in ROC showed the model could benefit from incorporating BSGI feature. This study developed a non-invasive prediction model that incorporates variables using ML method and serves to clinically predict ALN metastasis and help in selection of the appropriate treatment option.

How to Optimize Deimplementation of Sentinel Lymph Node Biopsy?

Background. The omission of sentinel lymph node biopsy in low-risk elderly breast cancer patients has been introduced in several guidelines. Despite evidence to support its safety, this recommendation has not been implemented by many clinicians. We have examined two aspects of this recommendation that may explain why sentinel lymph node biopsy continues to be performed in most of these patients. Firstly, we quantified the proportion of patients diagnosed with axillary metastases postoperatively. Secondly, we examined adherence to antihormonal therapy in the same group of patients. Methods. In this single-centre retrospective cohort study, the study population comprised 98 patients with breast cancer. Patients were aged ≥70 years and diagnosed with hormone receptor positive breast cancers less than 20 mm (T1). All patients underwent surgery and were subsequently prescribed five years of adjuvant antihormonal treatment. Results. Axillary lymph node metastases, as confirmed by the postoperative histology report, were seen in 36.3%. Nonadherence was seen in 33.7% of the patients. Primary nonadherence, that is, patients that never collect their first or subsequent prescriptions at the pharmacy, comprised 11.2% of the total study population. Conclusion. The high proportion of axillary metastases demonstrated suggests that clinical examination of the axilla alone is not sufficient in the preoperative assessment of the axilla. The less-than-optimal adherence rates show that adherence in these patients cannot be taken for granted. We suggest that these factors reflect some of the reluctance among clinicians to omit the sentinel lymph node procedure in these patients.

3D CT Radiomic Analysis Improves Detection of Axillary Lymph Node Metastases Compared to Conventional Features in Patients With Locally Advanced Breast Cancer.

Preoperative detection of axillary lymph node metastases (ALNMs) from breast cancer is suboptimal; however, recent work suggests radiomics may improve detection of ALNMs. This study aims to develop a 3D CT radiomics model to improve detection of ALNMs compared to conventional imaging features in patients with locally advanced breast cancer. Retrospective chart review was performed on patients referred to a specialty breast cancer center between 2015 and 2020 with US-guided biopsy-proven ALNMs and pretreatment chest CT. One hundred and twelve patients (224 lymph nodes) met inclusion and exclusion criteria and were assigned to discovery (n = 150 nodes) and testing (n = 74 nodes) cohorts. US-biopsy images were referenced in identifying ALNMs on CT, with contralateral nodes taken as negative controls. Positive and negative nodes were assessed for conventional features of lymphadenopathy as well as for 107 radiomic features extracted following 3D segmentation. Diagnostic performance of individual and combined radiomic features was evaluated. The strongest conventional imaging feature of ALNMs was short axis diameter ≥ 10 mm with a sensitivity of 64%, specificity of 95%, and area under the curve (AUC) of 0.89 (95% CI, 0.84-0.94). Several radiomic features outperformed conventional features, most notably energy, a measure of voxel density magnitude. This feature demonstrated a sensitivity, specificity, and AUC of 91%, 79%, and 0.94 (95% CI, 0.91-0.98) for the discovery cohort. On the testing cohort, energy scored 92%, 81%, and 0.94 (95% CI, 0.89-0.99) for sensitivity, specificity, and AUC, respectively. Combining radiomic features did not improve AUC compared to energy alone (P = .08). 3D radiomic analysis represents a promising approach for noninvasive and accurate detection of ALNMs.

Axillary lymph node metastasis in pure mucinous carcinoma of breast: clinicopathologic and ultrasonographic features

BackgroundThe purpose of this research is to study the sonographic and clinicopathologic characteristics that associate with axillary lymph node metastasis (ALNM) for pure mucinous carcinoma of breast (PMBC).MethodsA total of 176 patients diagnosed as PMBC after surgery were included. According to the status of axillary lymph nodes, all patients were classified into ALNM group (n = 15) and non-ALNM group (n = 161). The clinical factors (patient age, tumor size, location), molecular biomarkers (ER, PR, HER2 and Ki-67) and sonographic features (shape, orientation, margin, echo pattern, posterior acoustic pattern and vascularity) between two groups were analyzed to unclose the clinicopathologic and ultrasonographic characteristics in PMBC with ALNM.ResultsThe incidence of axillary lymph node metastasis was 8.5% in this study. Tumors located in the outer side of the breast (upper outer quadrant and lower outer quadrant) were more likely to have lymphatic metastasis, and the difference between the two group was significantly (86.7% vs. 60.3%, P = 0.043). ALNM not associated with age (P = 0.437). Although tumor size not associated with ALNM(P = 0.418), the tumor size in ALNM group (32.3 ± 32.7 mm) was bigger than non-ALNM group (25.2 ± 12.8 mm). All the tumors expressed progesterone receptor (PR) positively, and 90% of all expressed estrogen receptor (ER) positively, human epidermal growth factor receptor 2 (HER2) were positive in two cases of non-ALNM group. Ki-67 high expression was observed in 36 tumors in our study (20.5%), and it was higher in ALNM group than non-ALNM group (33.3% vs. 19.3%), but the difference wasn’t significantly (P = 0.338).ConclusionsTumor location is a significant factor for ALNM in PMBC. Outer side location is more easily for ALNM. With the bigger size and/or Ki-67 higher expression status, the lymphatic metastasis seems more likely to present.

Dynamic change of cancer genome profiling in metachronous bilateral breast cancer with BRCA pathogenic variant.

A 61-year-old woman with BRCA2 pathogenic variant had been treated for 20years and showed dynamic changes in the genomic profile of her metachronous bilateral breast cancer and metastases. She underwent right breast conservation surgery at age 42-Genome 1, lung metastasis and left axillary lymph node metastasis at age 51, partial excision under local anesthesia for left breast cancer at age 53-Genome 2, left axillary lymph node dissection was added 6month later-Genome 3. Then, olaparib was administered, and subsequently, left mastectomy was performed for the recurrence of left breast cancer at age 59-Genome 4. Genomic profile of the tumor was analyzed at four points (Genome 1-3 were analyzed by in house breast cancer panel, and Genome 4 was analyzed by Foundation One CDx). Two interesting findings emerged from these analyses. First, the genomic profile revealed that the left axillary lymph node metastasis, considered histologically from right breast cancer, was a metastasis from the left breast cancer. The second finding is that as the disease progressed, mutation profile became more diverse. The profile of the left breast cancer removed after olaparib and other treatments showed reversion mutation of BRCA2 and was diagnosed as tumor mutation burden high. Subsequent response to pembrolizumab was favorable.

A nomogram based on conventional and contrast-enhanced ultrasound radiomics for the noninvasively prediction of axillary lymph node metastasis in breast cancer patients.

This study aimed to investigate whether quantitative radiomics features extracted from conventional ultrasound (CUS) and contrast-enhanced ultrasound (CEUS) of primary breast lesions can help noninvasively predict axillary lymph nodes metastasis (ALNM) in breast cancer patients. A total of 111 breast cancer patients with 111 breast lesions were prospectively enrolled. All the included patients received presurgical CUS screening and CEUS examination and were randomly assigned to the training and validation sets at a ratio of 7:3 (n = 78 versus 33). Radiomics features were respectively extracted based on CUS and CEUS using the PyRadiomics package. The max-relevance and min-redundancy (MRMR) and least absolute shrinkage and selection operator (LASSO) analyses were used for feature selection and radiomics score calculation in the training set. The variance inflation factor (VIF) was performed to check the multicollinearity among selected predictors. The best performing model was selected to develop a nomogram using binary logistic regression analysis. The calibration and clinical utility of the nomogram were assessed. The model combining CUS reported ALN status, CUS radiomics score (CUS-radscore) and CEUS radiomics score (CEUS-radscore) exhibited the best performance. The areas under the curves (AUC) of our proposed nomogram in the training and external validation sets were 0.845 [95% confidence interval (CI), 0.739-0.950] and 0.901 (95% CI, 0.758-1). The calibration curves and decision curve analysis (DCA) demonstrated the nomogram's robust consistency and clinical utility. The established nomogram is a promising prediction tool for noninvasive prediction of ALN status. The radiomics features based on CUS and CEUS can help improve the predictive performance.

Longitudinal ultrasound-based AI model predicts axillary lymph node response to neoadjuvant chemotherapy in breast cancer: a multicenter study

ObjectivesDeveloping a deep learning radiomics model from longitudinal breast ultrasound and sonographer’s axillary ultrasound diagnosis for predicting axillary lymph node (ALN) response to neoadjuvant chemotherapy (NAC) in breast cancer.MethodsBreast cancer patients undergoing NAC followed by surgery were recruited from three centers between November 2016 and December 2022. We collected ultrasound images for extracting tumor-derived radiomics and deep learning features, selecting quantitative features through various methods. Two machine learning models based on random forest were developed using pre-NAC and post-NAC features. A support vector machine integrated these data into a fusion model, evaluated via the area under the curve (AUC), decision curve analysis, and calibration curves. We compared the fusion model’s performance against sonographer’s diagnosis from pre-NAC and post-NAC axillary ultrasonography, referencing histological outcomes from sentinel lymph node biopsy or axillary lymph node dissection.ResultsIn the validation cohort, the fusion model outperformed both pre-NAC (AUC: 0.899 vs. 0.786, p < 0.001) and post-NAC models (AUC: 0.899 vs. 0.853, p = 0.014), as well as the sonographer’s diagnosis of ALN status on pre-NAC and post-NAC axillary ultrasonography (AUC: 0.899 vs. 0.719, p < 0.001). Decision curve analysis revealed patient benefits from the fusion model across threshold probabilities from 0.02 to 0.98. The model also enhanced sonographer’s diagnostic ability, increasing accuracy from 71.9% to 79.2%.ConclusionThe deep learning radiomics model accurately predicted the ALN response to NAC in breast cancer. Furthermore, the model will assist sonographers to improve their diagnostic ability on ALN status before surgery.Clinical relevance statementOur AI model based on pre- and post-neoadjuvant chemotherapy ultrasound can accurately predict axillary lymph node metastasis and assist sonographer’s axillary diagnosis.Key PointsAxillary lymph node metastasis status affects the choice of surgical treatment, and currently relies on subjective ultrasound.Our AI model outperformed sonographer’s visual diagnosis on axillary ultrasound.Our deep learning radiomics model can improve sonographers’ diagnosis and might assist in surgical decision-making.

Abstract PS01-04: To dissect or not to dissect? The surgeon’s perspective on the prediction of ≥ 4 axillary lymph node metastasis in cN0 T1-2 breast cancer: A comparative analysis of the per-protocol population of the SINODAR-ONE clinical trial

Abstract Objectives The role of axillary surgery in the management of breast cancer (BC) has evolved considerably over the past decades, with only a few routine indications for axillary lymph node dissection (ALND) remaining in clinical practice. However, de-escalation of axillary surgery, especially in BC patients with 1-3 positive sentinel lymph nodes (SLNs) challenges the recently established criteria for adjuvant treatment (i.e., combination therapy with abemaciclib, endocrine therapy, and chemotherapy in patients with ≥ 4 positive nodes). The question remains as to whether these patients should undergo further ALND to determine whether ≥ 4 nodes are positive. To further investigate the latest controversies in axillary management of BC patients and predict the presence of ≥ 4 axillary lymph node metastasis, we evaluated and compared patients ≥ 4 positive nodes in the per-protocol population of the SINODAR-ONE clinical trial. Patients in the standard arm (ALND) of the per-protocol population were evaluated, and a comparison of characteristics between patients with ≥ 4 metastatic lymph nodes versus patients with 1-3 metastatic lymph nodes was performed. Categorical variables were compared using the chi-square test or Fisher’s exact test, as appropriate. Multivariable analysis was performed using a logistic regression model to identify independent predictors of ≥4 axillary lymph node metastasis. Results: Overall, 403 cN0 T1-2 BC patients in the per-protocol population were randomized to receive ALND. Of these, 65 and 338 patients presented with ≥ 4 or 1-3 axillary lymph node metastasis, respectively. Invasive lobular BC (26.2% versus 14.5% if other histology, odds ratio (OR)=4.185, 95% confidence interval (95%CI)= 1.284-1.443, p= 0.041), G3 (38.5% versus 21.3% if G1-2, OR=5.930, 95%CI= 2.134-2.289, p= 0.015), pT2 (46.2% versus 30.5% if pT1, OR=5.260, 95%CI= 15.330-16.346, p= 0.022), and 2 positive SLNs (32.3% versus 13.6% if 1 positive SLN, OR=13.188, 95%CI= 1.179-1.280, p&amp;lt; 0.0001) were found to significantly increase the probability to present ≥4 axillary lymph node metastasis at definitive histopathological evaluation. Conclusions: The introduction of abemaciclib and other combination therapies has the potential to impact the surgical management of the axilla. Our results suggest that a minority of cN0 T1-2 BC patients may be understaged if ALND is not performed. However, the improvements and increasing effectiveness of combination therapies may sufficiently control and treat the axillary tumor-burden left behind, potentially reducing the need for extensive axillary surgery, as demonstrated by the promising 3-year oncological outcomes of the SINODAR-ONE trial. Although ALND may still be considered, after multidisciplinary team discussion, in individual patients presenting with specific risk factors for additional axillary disease (lobular, G3, pT2 BC with 2 positive SLNs), our suggestion is that routine ALND is not indicated for systemic therapy decision-making in the upfront surgical setting. Citation Format: Damiano Gentile, Wolfgang Gatzemeier, Andrea Sagona, Erika Barbieri, Alberto Bottini, Alberto Testori, Valentina Errico, Simone Di Maria Grimaldi, Giulia Caraceni, Shadya Darwish, Giuseppe Canavese, Corrado Tinterri. To dissect or not to dissect? The surgeon’s perspective on the prediction of ≥ 4 axillary lymph node metastasis in cN0 T1-2 breast cancer: A comparative analysis of the per-protocol population of the SINODAR-ONE clinical trial [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS01-04.

Abstract PO4-07-10: Validation of An Ultrasound-Based Scoring System of Axillary Metastasis in Breast Cancer

Abstract Introduction: Ultrasound is the imaging modality of choice in the evaluation of axillary involvement in breast cancer. Our group previously created a scoring method to predict axillary lymph node metastasis (ALNM) based on ultrasound characteristics. In this study we validated the model and tested it among different Memorial Sloan Kettering Breast Cancer Sentinel Lymph Node Metastasis Nomogram1 (MSK) subgroups. Methods: The ultrasound score (table 1) was previously developed using data collected at a single institution from 2019 – 2021 by allocating points based on the regression coefficients of variables found to significantly predict ALNM. Subsequent evaluation of the discrimination of this model found it was robust to different patient demographic and tumor characteristics based on receiver operating characteristic curve analysis2. In this study, we validated the test statistics of our score at an outside institution. We also pooled patients from both institutions and evaluated the score performance in different MSK subgroups by the likelihood for sentinel node metastasis based off of clinical and pathologic data without accounting for imaging findings. Results: Between 2019-2021, 140 patients with breast cancer were analyzed for validation of the axillary ultrasound scoring system and when combined with the dataset of patients from the index institution, 358 pooled patients were stratified by predicted ALNM positivity according to MSK. In the validation cohort, the NPV for low risk (0-1) scores was 87%, while the PPV for high-risk (5+) scores was 71%. Overall in the combined cohort, 241 (67%) patients had low risk (0-1) axillary ultrasound scores and 33 (9%) had high risk (5+) scores. In this combined cohort, NPV was 84% (203/241 low-risk score patients were node negative), while PPV for high-risk scores was 85% (28/33 high risk score patients were node positive). When analyzed according to level of MSK predicted ALNM rates, for patients with &amp;lt; 50% predicted ALNM positivity, the NPV of low-risk scores was 87-89% and the PPV for high-risk axillary scores was 100%. For patients with &amp;gt;50% predicted ALNM positivity, the NPV of low-risk scores was 66% and the PPV of high-risk scores was 82%. Conclusions: A scoring system to predict ALNM among biopsy proven breast cancer patients undergoing upfront surgery was successfully developed from a multivariate model based on axillary ultrasound characteristics. This model was validated at a separate institution. The scoring system shows advantageous negative and positive predictive values for axillary node metastasis, especially among patients with &amp;lt; 50% predicted nodal involvement based on the MSK nomogram. This data may help foster better communication about ALNM risk between radiologists and treating clinicians to inform treatment decisions. Table. Scoring System for Likelihood of ALNM based on Ultrasound Characteristics Citation Format: Spencer Van Decar, Elizabeth Carpenter, Alexandra Adams, Jason Shore, Iulian Dragusin, Erika Davis, Craig Tork, Robert Krell, Troy Graybeal, Katherine Clifton, Arianna Buckley, Guy Clifton. Validation of An Ultrasound-Based Scoring System of Axillary Metastasis in Breast Cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-07-10.

Abstract PO3-23-03: Radar reflectors for marking of target lymph nodes in patients receiving neoadjuvant chemotherapy for breast cancer – a subgroup analysis of the prospective AXSANA (EUBREAST-03) trial

Abstract Background: Surgical staging procedures of the axilla in breast cancer patients converting from a clinically positive (cN+) to a clinically negative (ycN0) lymph node status during neoadjuvant chemotherapy (NACT) vary across countries and within individual countries. The international prospective AXSANA (EUBREAST-03) study aims at comparatively evaluating long-term outcomes of different staging procedures such as axillary lymph node dissection (ALND), sentinel lymph node biopsy (SLNB), target lymph node biopsy (TLNB) and targeted axillary dissection (TAD). The comparison of marking techniques for the target lymph node (TLN) is a secondary endpoint. In this subgroup analysis, we report on the largest yet prospective cohort of patients receiving a radar reflector for marking of the TLN prior to NACT. Methods: The AXSANA study is an international prospective cohort study including cN+ patients converting to ycN0 status and treated with different axillary staging techniques according to the standard at their treating institution. The study was initiated by the EUBREAST network and started enrollment in August 2020. Eligible patients have cT1-4c tumors, initially present with axillary lymph node metastasis and receive neoadjuvant chemotherapy. Patients converting to ycN0 status are followed up for 5 years irrespectively of the ypN status. In the present subgroup analysis, only patients with a TLN marked by a radar reflector were included. We prospectively examined the retrieval rate of radar reflectors and the identification rate of the TLN (defined as unequivocal removal of the lymph node, i.e., the presence of lymphatic tissue in the TLN specimen and/or pathological confirmation of post-NACT changes typical for metastatic lymph nodes responding to treatment). Results: A TLN was marked using a radar reflector in 74 patients prior to NACT. The median age was 52 years (range: 32-77). The most common subtype was no special type in 68 (92%) patients and invasive lobular cancer in 7 (7%) patients. Most patients (n = 44, 59%) had one suspicious node at time of diagnosis, followed by two (n = 15, 20%), three (n = 10, 14%) or ≥ four suspicious nodes (n = 5, 5%). Only one TLN was marked in 73 (99%) patients and two nodes were marked in one patient (1%). Fifteen patients (20%) had a MRI between marker placement and surgery. In 4 out of 15 cases (27%), MRI artifacts were described. However, the assessment of MRI was limited due to artifacts only in one out of these four patients (25%). Out of the 74 patients with radar reflector marked TLN, 45 (61%) had undergone final surgery at the time of analysis and 29 (39%) are still under NACT. 36 out of 45 patients (80%) who received final surgery converted to ycN0. Most patients had undergone a TAD (n = 41, 91%), followed by TLNB in 7% (n = 3) and upfront ALND in 2% of cases (n = 1). All radar reflectors were successfully removed. In 44 out of 45 patients (98%) the TLN was unequivocally identified and removed. In one patient the tissue specimen containing radar reflector consisted of fat tissue and no lymphatic tissue was identifiable, so it remains unclear whether the TLN was excised, or the reflector might have dislocated. 29 patients (65%) had converted to ypN0 status. Conclusion: To the best of our knowledge this is the largest prospective series of patients receiving a radar reflector for the marking of TLN prior to NACT for breast cancer. The removal rate of the marker and the detection rate of the target node were very high. Our data demonstrate that radar reflectors are a reliable tool for marking of target lymph nodes before neoadjuvant treatment. Citation Format: Maggie Banys-Paluchowski, Steffi Hartmann, Jana de Boniface, Oreste Davide Gentilini, Timo Basali, Elmar Stickeler, Michael Untch, Franziska Ruf, Sarah Fröhlich, Marc Thill, Michael P. Lux, Florentia Peintinger, Guldeniz Karadeniz Cakmak, Isabel Rubio, Michalis Kontos, Rosa Di Micco, Dawid Murawa, Ellen Schlichting, Bilge Aktas Sezen, Achim Rody, Dagmar Langanke, Nana Bündgen, Jose Ignacio Sanchez-Mendez, Renu Buss-Steidle, Christine Mau, Nana Buchmann, Sabine Riemer, Marit Helene Hansen, Nina Ditsch, Thorsten Kühn. Radar reflectors for marking of target lymph nodes in patients receiving neoadjuvant chemotherapy for breast cancer – a subgroup analysis of the prospective AXSANA (EUBREAST-03) trial [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-23-03.