Axillary Node Status Research Articles

Trastuzumab biosimilars interchangeability: Preliminary real-world data in neoadjuvant breast cancer setting.

e23305 Background: Interchangeability (IC) among biosimilar compounds is still an issue not completely addressed, due to concerns on adverse events, immunogenicity, and efficacy. Many regulatory agencies around the world have adopted IC aside of appropriate clinical trials evaluation. Brazil’s Public Health System (SUS) has centrally provided trastuzumab (T) for breast cancer (BC) and firstly adopted biosimilars in 2020, after their regulatory approval 2 years before. After T biossimilar adoption there was an improvement of access for its all clinical indications, however different compounds had been offered to patients (pts) along their treatment. The aim of this study is evaluating the impact of these exchanges of types of T on patient-based registry. Methods: We reviewed all pharma and clinical data between 2020 and 2022 from all pts, stage III, Her2-Positive, who received neoadjuvant treatment based on chemotherapy plus trastuzumab in a Comprehensive Cancer Center in Southern Brazil. The main objective is measuring the rates of changes among T biosimilar compounds, and its impact in pathological response (pCR), toxicity and treatment-failure rates. Chi-square, Fisher’s Exact, t-test, or Mann-Whitney testing, as appropriate, was used to compare events between groups. Results: Along the studied period, SUS had offered 3 T biosimilars: ABP 980 (Amgen), CT-P6 (Celltrion), SB3 (Samsung), plus reference trastuzumab (Roche). The standard regimen was ACq21 4cycles followed by Taxane (Docetaxel q21 or weekly Paclitaxel) plus T 4cycles. 146 pts were eligible to analysis. 83 pts received only one type of T compound (non-interchangeability group – NICG), 63 pts received at least 2 different T compounds along the treatment (Interchangeability Group – ICG). Both groups were comparable in terms of clinical features (age, ER/PR status, menopausal status, PS, tumor size, and staging of axillary lymph nodes). Rate of IC along the time was 11%, 53% and 33% in 2020, 2021 and 2022, respectively. pCR was 32,5% in NICG versus 33,3% in ICG (p:NS). In total, 3pts had their treatments interrupted due to toxicity (1pt cardiac, 2pts hematologic). Conclusions: Although IC among biosimilars compound had been globally accepted, it was not clinically fully tested. Brazilian Ministry of Health, as the only provider, has a policy based on lowest cost for trastuzumab compound, causing constant changes along pts treatments. Furthermore, it is expected that Brazil has the potential to evaluate the IC in thousands of pts prospectively next years. Hereby, we could demonstrate preliminary based on retrospective data that exchange among different biosimilar compounds did not interfere significantly on pCR, nor raised any new safety or clinical concerns.

Development of a Preoperative Prediction Model Based on Spectral CT to Evaluate Axillary Lymph Node With Macrometastases in Clinical T1/2N0 Invasive Breast Cancer

ObjectivesTo develop a prediction model based on spectral computed tomography (CT) to evaluate axillary lymph node (ALN) with macrometastases in clinical T1/2N0 invasive breast cancer. MethodsA total of 217 clinical T1/2N0 invasive breast cancer patients who underwent spectral CT scans were retrospectively enrolled and categorized into a training cohort (n = 151) and validation cohort (n = 66). These patients were classified into ALN nonmacrometastases (stage pN0 or pN0 [i+] or pN1mi) and ALN macrometastases (stage pN1-3) subgroups. The morphologic criteria and quantitative spectral CT parameters of the most suspicious ALN were measured and compared. Least absolute shrinkage and selection operator (Lasso) was used to screen predictive indicators to build a logistic model. The receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate the models. ResultsThe combined arterial-venous phase spectral CT model yielded the best diagnostic performance in discrimination of ALN nonmacrometastases and ALN macrometastases with the highest AUC (0.963 in the training cohort and 0.945 in validation cohorts). Among single phase spectral CT models, the venous phase spectral CT model showed the best performance (AUC = 0.960 in the training cohort and 0.940 in validation cohorts). There was no significant difference in AUCs among the 3 models (DeLong test, P > .05 for each comparison). ConclusionA Lasso-logistic model that combined morphologic features and quantitative spectral CT parameters based on contrast-enhanced spectral imaging potentially be used as a noninvasive tool for individual preoperative prediction of ALN status in clinical T1/2N0 invasive breast cancers.

Intraoperative ultrasound guided wire (IOUS-wire) localization biopsy with frozen section versus preoperative fine needle aspiration cytology (FNAC) for the evaluation of clinically positive nodes in patients with early breast cancer in China real-world clinical practice.

e12600 Background: Appropriate biopsy procedure for clinically positive (suspicious) lymph nodes (LN) plays a key role in accurate axillary staging of early breast cancer. Fine needle aspiration (FNA) is a recommended method for clinically suspicious nodes in the National Comprehensive Cancer Network (NCCN) guidelines. However, the false negative rate (FNR) of FNA remains too high. Therefore, it is valuable to explore an alternative and appropriate procedure in clinical practice. We conducted a retrospective review to compare the feasibility and diagnostic efficiency of using a novel procedure, the intraoperative ultrasound guided wire (IOUS-wire) localization biopsy with frozen section, to FNA for assessment of clinically positive nodes in patients with early breast cancer. Methods: Data on consecutive primary breast cancer patients with documented axillary ultrasound results who had clinically positive lymph nodes (cN1) between January 2015 and September 2023 either utilization of IOUS-wire localization or ultrasonography-guided fine-needle aspiration cytology (US-FNAC) for clinically positive lymph nodes were retrospectively analyzed. Sentinel lymph node biopsy (SLNB) was performed in patients with negative histopathological or cytological results. The false negative rate (FNR) of histopathology between two groups was calculated. Results: Eligible patients (n=198) referred to axillary biopsy by IOUS-wire localization biopsy(n=100) and US-FNAC(n=98) in this study. The FNR of clinically positive lymph nodes biopsy was 12.8 percent in the IOUS-wire localization group and 87.5 percent in the US-FNAC group.IOUS-wire failed in 5 patients. Among the remaining 32 successful identifications, 26 were IOUS-wire-node positive by intraoperative frozen section. 42 patients with additional lymph node metastases were found in FNAC-LN-negative patients by SLNB. The accuracy rate was respectively 95% and 57.1%. No significant difference was observed in complications and median number of SLNs harvested (5.3 versus 5.5; P=0.631) between the groups. Sufficient tissue samples can be obtained for intraoperative frozen pathology so the FNR is potentially reduced. In addition, the interval of waiting time for pathological results is reduced. Conclusions: Intraoperative US-guided wire localization biopsy is a feasible and alternative procedure compared to preoperative FNA for clinically node-positive patients with early breast cancer. This novel method is suitable for China's national conditions and should be further pursued as a potential biopsy method for evaluation of axillary node status in clinical practice.

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 novel nomogram and survival analysis for different lymph node status in breast cancer based on the SEER database

IntroductionThe axillary lymph node status (ALNS) and internal mammary lymph nodes (IMLN) expression associated with breast cancer are closely linked to prognosis. This study aimed to establish a nomogram to predict survival at 3, 5, and 10 years in patients with various lymph node statuses.MethodsWe obtained data from patients with breast cancer between 2004 and 2015 from the Surveillance, Epidemiology, and End Results (SEER database). Chi-square analysis was performed to test for differences in the pathological characteristics of the groups, and Kaplan–Meier analysis and the log-rank test were used to plot and compare the correlation between overall survival (OS) and breast cancer specific survival (BCSS). The log-rank test was used for the univariate analysis, and statistically significant characteristics were included in the multivariate and Cox regression analyses. Finally, Independent factor identification was included in constructing the nomogram using R studio 4.2.0; area under curve (AUC) values were calculated, and receiver operating characteristic (ROC) curve, calibration, and decision curve analysis (DCA) curves were plotted for evaluation.ResultsA total of 279,078 patients were enrolled and analysed, demonstrating that the isolated tumour cells (ITC) group had clinicopathological characteristics similar to those of micrometastases (Mic). Multivariate analysis was performed to identify each subgroup's independent risk factors and construct a nomogram. The AUC values were 74.7 (95% CI 73.6–75.8), 72.8 (95% CI 71.9–73.8), and 71.2 (95% CI 70.2–72.2) for 3-, 5-, and 10-year OS, respectively, and 82.2 (95% CI 80.9–83.6), 80.1 (95% CI 79.0–81.2), and 75.5 (95% CI 74.3–76.8) for BCSS in overall breast cancer cases, respectively. AUC values for 3-, 5-, and 10-year OS in the ITC group were 64.8 (95% CI 56.5–73.2), 67.7 (95% CI 62.0–73.4), and 65.4 (95% CI 60.0–70.7), respectively. For those in the Mic group, AUC values for 3-, 5-, and 10-year OS were 72.9 (95% CI 70.7–75.1), 72.4 (95% CI 70.6–74.1), and 71.3 (95% CI 69.6–73.1), respectively, and AUC values for BCSS were 77.8 (95% CI 74.9–80.7), 75.7 (95% CI 73.5–77.9), and 70.3 (95% CI 68.0–72.6), respectively. In the IMLN group, AUC values for 3-, 5-, and 10-year OS were 75.2 (95% CI 71.7–78.7), 73.4 (95% CI 70.0–76.8), and 74.0 (95% CI 69.6–78.5), respectively, and AUC values for BCSS were 76.6 (95% CI 73.0–80.3), 74.1 (95% CI 70.5–77.7), and 74.7 (95% CI 69.8–79.5), respectively. The ROC, calibration, and DCA curves verified that the nomogram had better predictability and benefits.ConclusionThis study is the first to investigate the predictive value of different axillary lymph node statuses and internal mammary lymph node metastases in breast cancer, providing clinicians with additional aid in treatment decisions.

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.

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.

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 PO3-22-09: Implementation of nanoparticle-assisted axillary staging: reduced axillary intervention for patients with node-positive breast cancer following neoadjuvant therapy

Abstract Background: Nanoparticle-assisted axillary staging (NAAS), which combines carbon nanoparticles with standard dual-tracer (radioisotope and blue dye) sentinel lymph node biopsy (SLNB), has shown excellent results in patients undergoing neoadjuvant therapy (NAC) for pre-NAC node-positive breast cancer. This pilot study aimed to explore the possible expansion of indications (pre-NAC cN2, and cN3 stages; ycN+ stage) for NAAS, which was the first attempt till now for patients undergoing NAC, and to select specific post-NAC individuals who might be exempt from axillary lymph node dissection (ALND), especially those with relatively low tumor burdens, such as ITCs and micrometastases. Method: The medical records of all consecutive patients with breast cancer were prospectively collected from the Breast Center, West China Hospital. Patients with invasive, pre-NAC node-positive breast cancer who underwent NAAS followed by ALND between April 2021 and February 2023 were included. The detection rate (DR), average number of sentinel or NAAS nodes, false negative rate (FNR), negative predictive value (NPV), and NAAS accuracy were assessed. Subgroup analyses according to different pre- and post-NAC cN stages, especially the pre-NAC cN2-3 stages and the post-NAC cN+ stages, which were not the focus of previous studies, were conducted using crosstabs. To screen possible patients who might exempt ALND, we focus on relationships between the status of axillary lymph nodes and the number or tumor burden of NAAS nodes. Results: A total of 241 eligible patients were included in the study. There were 153 (63.49%), 40 (16.60%), and 48 (19.92%) patients in pre-NAC cN1, cN2, and cN3 stages, respectively. The post-NAC stages of patients were not restricted, 38.17% (92/241) of them converted to ycN0 stages, while 61.83% (149/241) were still in ycN+ stages. A total of 232 (96.27%) eligible patients successfully underwent NAAS; the DR, NPV, accuracy, and FNR of NAAS were 96.27%, 95.97%, 97.84%, and 4.42%, respectively. Additionally, NAAS showed accuracies of 97.30% and 93.02% and FNRs of 7.14% and 9.09% in patients with pre-NAC cN2 and cN3 breast cancers, respectively. There was no statistical difference among the FNRs of NAAS in the pre-NAC cN1, cN2, and cN3 stages (p=0.134). Moreover, NAAS yielded FNRs of 0.00% in the ycN0 population and 6.02% in the ycN+ population, respectively (p=0.323). Accuracies reached 100.00% and 96.48% in ycN0 and ycN+ patients, respectively (p=0.159). Notably, patients with one NAAS node with ITC or micrometastasis in the entire population showed no further positive disease at ALND. Conclusion: NAAS showed excellent and stable performance with a low FNR and high accuracy, indicating valuable application prospects in patients with breast cancer who received NAC. Patients with pre-NAC cN2-3 stages also have a chance to undergo NAAS, and the application of NAAS appears to be feasible regardless of the ycN stage. Especially for ycN0 population, there is no chance for NAAS to miss positive axillary lymph nodes. In addition, patients who had one NAAS node with ITC or micrometastasis might avoid ALND. Diagnostic performance of nanoparticle-assisted axillary staging in pre-neoadjuvant chemotherapy cN1, cN2 and cN3 patients Diagnostic performance of nanoparticle-assisted axillary staging in ycN0 and ycN+ patients Additional axillary disease at axillary lymph node dissection in patients with positive NAAS nodes after neoadjuvant chemotherapy XX- XX XX- XX XX- XX Citation Format: Ruixian Chen, Tao He, Jiqiao Yang, Shan Lu, Yunhao Wu, Chihua Wu, Ruoning Yang, Weijing Liu, Ya Huang, Xiangyue Meng, Xin Zhao, Qintong Li, Xin Sun, Jing Jing, Jie Chen. Implementation of nanoparticle-assisted axillary staging: reduced axillary intervention for patients with node-positive breast cancer following neoadjuvant therapy [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 PO3-22-09.

Abstract PO5-07-05: Deep learning can diagnose axillary lymph node metastases on optical virtual histologic images in breast cancer patients during surgery

Abstract Background: Reliable identification of axillary lymph node (ALN) involvement in patients with breast cancer allows for definitive axillary dissection at the time of the initial surgery, thus avoiding the need for a separate axillary surgery. However, conventional intraoperative ALN diagnostic methods are time-consuming and labor-intensive and can result in tissue destruction. Dynamic full field optical coherence tomography, also called dynamic cell imaging (DCI), has been developed and validated to offer rapid and label-free histologic approximations of metastatic and non-metastatic ALNs. In this study, we aim to optimize the diagnostic pipeline with an automated approach and present the results of using a deep learning (DL) algorithm with DCI to predict ALN status intraoperatively in patients with breast cancer. Methods: Breast cancer patients who required ALN staging were enrolled prospectively in this study. DCI was applied to bisected fresh lymph nodes in a non-destructive manner, and the specimens were subsequently sent for histopathological examination, regarded as the gold standard for comparison. A DL model was trained and fine-tuned on over 80,000 DCI images, and the results were mapped to slide level to predict ALN diagnosis. Results: Total 607 DCI slides of ALNs with 112,852 cropped patches were included in the study. The DL model was trained and validated on a dataset containing 481 slides and tested on an independent testing dataset with 126 slides. In the test set, the DL algorithm yielded accuracy for prediction of ALN status, with sensitivity and specificity of 91.9% and 95.5% and an area under the receiver operating characteristic curve (AUC) of 0.937 (95% confidence interval [CI]: 0.912-0.957) at slide level. Conclusion: These results demonstrate that the integration of DCI with DL is rapid, reduces labor requirements and minimizes tissue destruction. Meanwhile, this algorithm had high classification accuracy to predict the metastatic burden of ALNs for patients with breast cancer. Citation Format: Shuwei Zhang, Houpu Yang, Jin Zhao, Shu Wang. Deep learning can diagnose axillary lymph node metastases on optical virtual histologic images in breast cancer patients during surgery [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 PO5-07-05.

Abstract PO2-07-03: Retrospective Validation Study of an Artificial Neural Network-Based Preoperative Decision-Support Tool for Noninvasive Lymph Node Staging (NILS) in Women with Primary Breast Cancer (ISRCTN14341750)

Abstract Background Surgical sentinel lymph node biopsy (SLNB) is routinely used to reliably stage axillary lymph nodes in early breast cancer (BC). However, SLNB may be associated with postoperative arm morbidities. For most patients with BC undergoing SLNB, the findings are benign, and the procedure is currently questioned. A decision-support tool for the prediction of benign sentinel lymph nodes based on preoperatively available data has been developed using artificial neural network modelling [1,2]. Methods This was a retrospective geographical and temporal validation study of the noninvasive lymph node staging (NILS) model, based on preoperatively available data from 586 women consecutively diagnosed with primary BC at two sites. Ten preoperative clinicopathological characteristics from each patient were entered into the web-based calculator (Table 1), and the probability of benign lymph nodes was predicted. Vascular invasion, the tenth feature of the NILS model, was difficult to determine preoperatively. Therefore, a separate ANN model was developed to impute this feature, using the other nine features of the NILS model as predictors. A user-friendly web implementation of the NILS model was tested in this study. The performance of the NILS model was assessed in terms of discrimination with the area under the receiver operating characteristic curve (AUC). The primary endpoint was axillary nodal status (discrimination, benign [N0] vs. metastatic axillary nodal status [N+]) determined by the NILS model compared to nodal status by definitive pathology. Results The mean age of the women in the cohort was 65 years, and most of them (93%) had luminal cancers (Table 2). Approximately three-fourths of the patients had no metastases in SLNB (N0 74%). The AUC for the predicted probabilities for the whole cohort was 0.6741 (95% confidence interval: 0.6255–0.7227). More than one in four patients (n=151, 26%) were identified as candidates for SLNB omission when applying the predefined cut-off for lymph node negativity from the development cohort (Table 3). Conclusion The performance of the NILS model was satisfactory. In approximately every fourth patient, SLNB could potentially be omitted. Considering the shift from postoperatively to preoperatively available predictors in this validation study, we have demonstrated the robustness of the NILS model. The clinical usability of the web interface will be evaluated before its clinical implementation. Trial registration Registered in the ISRCTN registry with study ID ISRCTN14341750.

Abstract PO4-20-05: An open label phase III randomized trial to evaluate the efficacy of Post-chemotherapy Axillary Conservation Surgery in breast cancer (PACS)

Abstract Background- Axillary lymph nodes (ALN) status is the most important prognostic factor in breast cancer. Traditionally, a complete axillary lymph node dissection (ALND) has been standard of care for those patients who have a positive node pre-chemotherapy. However, lately axillary conservation is paving its way in the post-chemotherapy setting as well to avoid significant morbidity of ALND. Most sentinel node biopsy studies have failed to achieve the desired false negative rate(FNR). (Boughey JC et al, JAMA. 2013 Oct; Kuehn T et al, Lancet Oncol. 2013; Boileau JF et al, J Clin Oncol. 2015 Jan).We previously reported a low FNR of low axillary sampling (LAS) in the post-chemotherapy setting. (Parmar et al, JCO-GO, 2021) We are conducting a phase III, randomised controlled trial of LAS versus complete ALND in post-chemotherapy negative (ypN0) axilla. Aims and objectives- The primary objective is to assess the non-inferiority of LAS in patients who are rendered ypN0 by pre-operative chemotherapy with disease-free survival as primary end point. The secondary objectives are comparing the morbidity of ALND (lymphedema, paraesthesia, and shoulder dysfunction) in the LAS and ALND arms, and to evaluate the impact on overall survival, local, regional, and distant recurrence free survival and quality of life. Trial design- This is a prospective, open label, randomised, phase III trial where histologically proven, non-metastatic, cT1-4,N0-2 breast cancer patients who are rendered ycN0 after pre-operative systemic chemotherapy are being accrued. Patients with prechemo N3 disease, inflammatory breast cancer, recurrent disease, pregnancy associated breast cancer are being excluded from the study. The chemotherapy regimens and surgical assessment is as per standard institutional protocols. After obtaining informed consent, patients undergo LAS during surgery and lymph nodes(LN) are sent for frozen section analysis. If any LN is found to be involved, a complete ALND is carried out. If the staging procedure is negative, then patients are randomised into 2 groups- observation v/s complete ALND. Stratification factors are T stage, N stage, Hormone receptor status, Her2 receptor status and number of chemotherapy cycles taken. Morbidity assessment is done with- Constans shoulder score, arm volumetry, paraesthesia assessment, and a QOL questionnaire- Self-reported Breast cancer Lymphedema Symptom Experience Index (BCLSEI). A baseline assessment pre-surgery is followed by 6 monthly morbidity assessments and regular oncologically relevant follow-up up to 5 years. A two-sided log-rank test with an overall sample size of 2316 with a 10% dropout rate (1158 in ALND and 1158 in LAS group) achieves 80% power at a 0.05 significance level to detect a hazard ratio of 1.16 (upper limit of non-inferiority) when the proportion surviving in the control group is 0.71. An interim analysis will be carried out at 25% events and conditional power will be calculated. If the conditional power is below 20%, then the study will be discontinued for futility. The study has been approved by Institutional Ethics Committee (CTRI/2022/07/044461) Expected Study and Results- We started accrual in February 2023 and have screened 63 and randomized 45 patients until now. Ours is a first large randomised prospective study in the post-chemotherapy axillary management setting which will help us understand the efficacy of axillary conservation surgery in patients who are node negative post neoadjuvant chemotherapy. In future, lesser morbid procedures than LAS like targeted axillary dissection can be tested in the same trial. However, LAS as a technique of axillary conservation suits resource constraints and high volume setting such as ours. Citation Format: Shalaka Joshi, Jessicka Shah, Pallavi Daphale, Rohini Hawaldar, Anuradha Daptardar, Ayushi Sahay, Tanuja Shet, Palak Popat, Tabassum Wadasadawala, Nita Nair, Vani Parmar, Bipin Bandre, Shabina Siddique, Vaibhav Vanmali, Sudeep Gupta, Rajendra Badwe. An open label phase III randomized trial to evaluate the efficacy of Post-chemotherapy Axillary Conservation Surgery in breast cancer (PACS) [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 PO4-20-05.

Abstract PO4-27-10: A Systematic Review and Meta-analysis of Diagnostic Performance of Fluorescein Guided Sentinel Lymph Node Biopsy in Early Breast Cancer

Abstract Background: Evaluation of axillary lymph nodes status in cN0 axilla is performed by sentinel lymph node biopsy (SLNB) utilizing a combination of radioactive isotope and blue dye. There is a paucity of radioactive tracer in many countries of transitional economies. Hence, alternatives to isotope have been explored, viz. indocyanine green (ICG), superparamagnetic iron oxide nanoparticles and Fluorescein Sodium (FS). This systematic review and meta-analysis, evaluates the diagnostic performance of Fluorescein Sodium guided SLNB. Objectives: The objective was to evaluate the diagnostic performance of Fluorescein Sodium for sentinel lymph node biopsy in early breast cancer. Methods: PRISMA 2020 guidelines were followed for this meta-analysis. Eligibility criteria: Studies where SLNB was performed using Fluorescein Sodium for breast carcinoma. Information sources: PubMed, EMBASE, Cochrane library and online clinical trial registers Risk of bias: Articles were assessed for risk of bias using the QUADAS-2 tool. Synthesis of results: The main summary measures were pooled detection rate and pooled false negative rate using random effects model. In order to describe heterogeneity, we constructed summary receiver operating characteristic (SROC) curves with 95% prediction regions, estimated using bivariate meta-analysis with a test level random effect only, and forest plots. Calculations were performed using the Meta-Disc v1.4 program (available from https://metadisc.software.informer.com/1.4/). Results: Out of 45 studies found on initial systematic search, 7 studies were found eligible and were included in the meta-analysis based on data on 332 patients. The pooled detection rate was 93.2% (95% confidence interval [CI] 87% to 97%).). The heterogeneity (I2 value) of the pooled detection rate was 71%. Five validation studies were included for pooling the false negative rate (FNR) and included a total of 211 patients. The pooled FNR was 5.6% (95% CI; 2.9 to 9.07). Five validation studies were included for sensitivity, specificity, diagnostic odds ratio, LR+ and LR- meta-analysis. With regards to the identification of SLN the pooled sensitivity was 80% [(95% CI, 68% to 89%); I2=79.8]; specificity was 98% [(95% CI, 95% to 100%); I2=0.0]. The likelihood ratio for a positive test LR+ was 23.2 [(95% CI, 5.91 to 95.18); I2=0.0] and likelihood ratio for a negative test a LR- of 0.41 [(95% CI, 0.16 to 1.05); I2=88.1]. The diagnostic Odds ratio OR was 76.94 (95% CI, 15.88 to 372.79). Diagnostic accuracy as measured by SROC was 99.08%. Q index was 0.96. Publication bias for pooled detection rate as per Egger’s test was -2.2 (p= 0.66) and for FNR was 1.85 (p=0.29). Visual inspection of summary ROC curve and of forest plots did not show significant heterogeneity. Conclusion: Fluorescein guided SLNB is an affordable option for detection of lymph node metastases in cN0 patients with early breast cancer. It achieves a high detection rate of 93% with a false negative rate of 5.6% for the detection of axillary lymph node metastasis. We recommend larger multinational trials to confirm its high detection rate with low false negativity. Citation Format: Sanjay Kumar Yadav, Bharath S, Sarada Khadka, Dhananjaya Sharma, Chandan Kumar Jha, Gaurav Agarwal, Manju Singh, Anurag Srivastava, Amit Goyal. A Systematic Review and Meta-analysis of Diagnostic Performance of Fluorescein Guided Sentinel Lymph Node Biopsy in Early 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 PO4-27-10.

Abstract PS01-03: What to expect from the No axillary surgical treatment for lymph node-negative patients after ultra-sonography [NAUTILUS] trial (KBCSG-21): Clinicopathologic characteristics and axillary lymph node status of enrolled patients

Abstract Purpose: The primary role of sentinel lymph node biopsy (SLNB) for early breast cancer (BC) is axillary staging. In terms of clearance of axillary disease or prevention of recurrence, its role may be limited considering the low axillary recurrence rate of less than 2% even though false-negative rates are 5-10% and the 25% additional axillary lymph node (ALN) detection in the ALND arms of the ACOSOG Z0011 and AMAROS trials. The NAUTILUS trial (NCT04303715) randomized cT1-2/N0 BC patients planned for breast-conserving surgery to evaluate the non-inferiority of omitting SLNB regarding 5-year invasive disease-free survival. The secondary endpoints are overall survival, distant metastasis-free survival, axillary recurrence rate, and quality of life of the patients. We aimed to investigate the clinicopathologic characteristics and ALN status of the subjects enrolled in the NAUTILUS trial. Methods: NAUTILUS trial randomized 1,734 subjects into SLNB or no-SLNB arms from September 2020 to October 2022. Axillary ultrasonography was mandatory to determine clinical N0, defined as no suspicious ALN or no tumor on ultrasound-guided biopsy of suspicious ALN. Clinicopathologic variables and the ALN status of the SLNB arm were analyzed. Results: Among 1,734 enrolled subjects, 828 (50.3%) and 818 (49.7%) subjects in the SLNB and no-SLNB arms, respectively, were included for analysis. Clinical and pathologic T stage, hormonal receptor/HER2 status, histologic grade, age, menopausal status, and Ki-67 were evenly distributed between the two groups (p = 0.554, 0.350, 0.056, 0.369, 0.623, 0.725 and 0.214, respectively). Median age was 55.3 (range, 48.0-62.0) years, and 661 (40.2%) were premenopausal. Overall, 30 (1.8%), 1,382 (84.0%), and 229 (13.9%) subjects were pTmic, pT1, and pT2, respectively, and median tumor size was 1.3 cm (range, 0.1-5.0). In the SLNB group, 94 (11.4%) had ALN metastasis, of which 9 (1.1%), 78 (9.4%), and 5 (0.6%) were pN1mic, pN1, and pN2-3, respectively (Table 1). According to pathologic tumor size, 5.8% (16/279), 11.4% (48/421), and 23.8% (30/126) were ALN positive for ≤ 1.0 cm, &amp;gt;1.0cm &amp; ≤ 2.0 cm, and &amp;gt; 2.0 &amp; ≤ 5.0 cm, respectively. The clinical and pathologic tumor size distribution among subjects with ALN metastasis were 23 (24.5%), 43 (45.7%), 9 (9.6%) and 16 (17.0%), 48 (51.1%), 30 (31.9%), respectively, for ≤ 1.0 cm, &amp;gt;1.0cm &amp; ≤ 2.0 cm, and &amp;gt; 2.0 &amp; ≤ 5.0 cm (Table 2). Among them, 12 (12.8%) received subsequent ALND. There was no difference in ALN metastasis rate according to molecular subtype, histologic grade, age, menopausal status, and Ki-67 (p= 0.812, 0.204, 0.671, and 0.101, respectively). Conclusions: The NAUTILUS trial completed enrollment of 1,734 subjects, among which 1,646 are available to analyze basic clinicopathologic characteristics. The trial included 229 (13.9%) pT2 and 661 (40.2%) premenopausal subjects and is expected to show the impact of SLNB omission in these subgroups. Data lock is expected in October 2027. Patients characteristics SLNB, sentinel lymph node biopsy; LVI, lymphovascular invasion; IDC, invasive ductal carcinoma; ILC, invasive lobular carcinoma; HER2, anti-human epidermal growth factor-2; BCS, breast conserving surgery; TM, total mastectomy; ALND, axillary lymph node dissection Basic characteristics for sentinel lymph node biopsy group a revealed no lymph node metastasis by fine needle aspiration or gun biopsy ALN, axillary lymph node; LN, lymph node Citation Format: Jai Min Ryu, Han-Byoel Lee, Sei Hyun Ahn, Il-Yong Chung, Seeyoun Lee, Seho Park, Woosung Lim, Joon Jeong, Jeong Eon Lee, Eunhye Kang, Ji Hyun Chang, Jung Min Chang, Woo Kyung Moon, Wonshik Han, Eun-Kyu Kim. What to expect from the No axillary surgical treatment for lymph node-negative patients after ultra-sonography [NAUTILUS] trial (KBCSG-21): Clinicopathologic characteristics and axillary lymph node status of enrolled patients [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 PS01-03.

Abstract PO4-22-08: Ten-Year Oncologic Outcomes in T1-3N1 Breast Cancer After Targeted Axillary Sampling: A Retrospective Study

Abstract Background Targeted axillary sampling (TAS) is a new surgical concept for the assessment of axillary lymph node status in breast cancer that is hypothesized to be more effective at minimizing postoperative morbidities than axillary lymph node dissection (ALND), provided the metastatic axillary lymph node can be accurately detected without missing data; however, the oncologic outcomes over long-term follow-up have not been sufficiently investigated. This was a retrospective analysis to evaluate the 10-year oncologic outcomes in T1-3N1 breast cancer after TAS. Methods Clinically, we defined TAS as removal of a bunch of ALNs located around SLNs and targeted LNs, but without full exposure of the axillary vein, long thoracic nerve and thoracodorsal nerve. Between 2008 and 2013, 230 female patients with cT1-3N1 breast cancer underwent breast and axillary surgery (ALND, n = 171; TAS, n = 59) at our institute. After TAS was applied, additional axillary radiotherapy was performed. Various postoperative complications, including postoperative seroma, lymphedema, and 10-year oncological outcomes, were evaluated and compared between the ALND and TAS groups. Results Although overall survival during the 10-year follow-up period was better in the TAS group, there was no statistically significant difference in oncologic outcomes, including locoregional recurrence, distant metastasis, and overall survival (p = 0.395, 0.818, and 0.555, respectively). Furthermore, the incidence of lymphedema on the ipsilateral arm was significantly higher in the ALND group (p &amp;lt; 0.001). Conclusions The 10-year oncological outcomes of TAS were not inferior to those of conventional ALND in T1-3N1 breast cancers; however, the incidence of lymphedema was significantly higher in the ALND group. Citation Format: Byeongju Kang, Jeeyeon Lee, Yee Soo Chae, Ho Yong PARK, Jin Hyang Jung. Ten-Year Oncologic Outcomes in T1-3N1 Breast Cancer After Targeted Axillary Sampling: A Retrospective Study [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 PO4-22-08.

Evaluation of Sensitivity and Specificity of Ultrasound-Guided FNA of Suspicious Axillary Lymph Nodes in Patients with Breast Cancer

Background: The axillary lymph node status in breast cancer is a major prognostic factor in survival and establishing a personalized treatment scheme. The ultrasound-guided fine needle aspiration (US-FNA) is a method for taking a lymph node sample. It allows physicians to decide how to manage the axilla. Objectives: This study was conducted to investigate the sensitivity of the US-FNA technique on suspicious axillary lymph nodes with a thickness of 3 to 6 mm in breast cancer patients. Methods: In a cross-sectional study, all the patients were subjected to preoperative ultrasound evaluation of the axilla to determine the presence of lymph nodes suspicious of malignancy. In cases where the suspicious lymph node cortex size was between 3 and 6 mm, US-FNA was performed. After surgery, the frozen section of the biopsy sample was examined histologically and compared with fine needle aspiration (FNA) cytology results. Results: A total of 102 patients were examined in the study. FNA test results indicated that 46 subjects had axillary malignant tissue, and benign cases summed 56. Also, the final results of frozen section surgical histopathology identified 46.1% of patients with involved lymph nodes. The sensitivity and specificity of FNA were 93.62% and 96.36%, respectively. Also, the overall diagnostic accuracy was 95.1%. Conclusions: This study showed that the sensitivity, specificity, and accuracy were more than 90% for the ultrasound-guided FNA test in identifying involved lymph nodes in patients with breast cancer. Therefore, the results of this test can be considered clinically reliable. However, there is still a need to examine the sensitivity and specificity of this method in identifying lymph node involvement.

Breast edema score at breast MRI: its value in prediction of molecular subtype of breast cancer and its impact on axillary LN metastasis

BackgroundSince many newly diagnosed breast cancer patients have breast MRI, the value of preoperative breast magnetic resonance imaging would improve if molecular subtypes could be consistently identified, and prognostic information provided in addition to diagnostic imaging. Breast edema may improve the ability to predict molecular subtypes and clinical and pathological outcomes in invasive breast cancer patients. The prognosis for breast cancer prognosis based on the findings of breast edema by magnetic resonance imaging will be useful in both pretreatment planning and prognosis. Breast edema on T2-weighted images and STIR was scored on a scale of 1 to 4, as follows: (a) breast edema score (BES) 1, no edema; (b) BES 2, peritumoral edema; (c) BES 3, pre pectoral edema; and (d) BES 4, subcutaneous edema (suspicious for occult inflammatory breast cancer “IBC”). Axillary lymph node status and number were also evaluated in T2 and STIR and after contrast administration. The aim of this work was to assess the role of tumour-related breast edema MRI features in distinguishing molecular subtypes of breast cancer and its effect on pathological axillary lymph nodes in patients with breast cancer.ResultsThere was a highly significant difference between BES with respect to the molecular subtypes of breast cancer, size of the mass, Ki-67 expression, LN status, and LN number (p < 0.0001, 0.045, < 0.0001, < 0.0001, and < 0.0001 respectively). However, there was no significant difference between BES and histopathological grade in studied masses, such as p-value = 0.49.ConclusionsTumour-related breast edema MRI characteristics may be useful in distinguishing molecular subtypes of breast cancer and could be used as a promising feature to improve the predictive performance of pathological axillary lymph nodes in patients with breast cancer, contributing to preoperative treatment planning and prognostic outcome.

A radiogenomic multimodal and whole-transcriptome sequencing for preoperative prediction of axillary lymph node metastasis and drug therapeutic response in breast cancer: a retrospective, machine learning and international multicohort study.

Axillary lymph nodes (ALN) status serves as a crucial prognostic indicator in breast cancer (BC). The aim of this study was to construct a radiogenomic multimodal model, based on machine learning and whole-transcriptome sequencing (WTS), to accurately evaluate the risk of ALN metastasis (ALNM), drug therapeutic response and avoid unnecessary axillary surgery in BC patients. In this study, conducted a retrospective analysis of 1078 BC patients from The Cancer Genome Atlas (TCGA), The Cancer Imaging Archive (TCIA), and Foshan cohort. These patients were divided into the TCIA cohort ( N =103), TCIA validation cohort ( N =51), Duke cohort ( N =138), Foshan cohort ( N =106), and TCGA cohort ( N =680). Radiological features were extracted from BC radiological images and differentially expressed gene expression was calibrated using technology. A support vector machine model was employed to screen radiological and genetic features, and a multimodal model was established based on radiogenomic and clinical pathological features to predict ALNM. The accuracy of the model predictions was assessed using the area under the curve (AUC) and the clinical benefit was measured using decision curve analysis. Risk stratification analysis of BC patients was performed by gene set enrichment analysis, differential comparison of immune checkpoint gene expression, and drug sensitivity testing. For the prediction of ALNM, rad-score was able to significantly differentiate between ALN- and ALN+ patients in both the Duke and Foshan cohorts ( P <0.05). Similarly, the gene-score was able to significantly differentiate between ALN- and ALN+ patients in the TCGA cohort ( P <0.05). The radiogenomic multimodal nomogram demonstrated satisfactory performance in the TCIA cohort (AUC 0.82, 95% CI: 0.74-0.91) and the TCIA validation cohort (AUC 0.77, 95% CI: 0.63-0.91). In the risk sub-stratification analysis, there were significant differences in gene pathway enrichment between high and low-risk groups ( P <0.05). Additionally, different risk groups may exhibit varying treatment responses ( P <0.05). Overall, the radiogenomic multimodal model employs multimodal data, including radiological images, genetic, and clinicopathological typing. The radiogenomic multimodal nomogram can precisely predict ALNM and drug therapeutic response in BC patients.

Determination of axillary lymph node status by fine-needle aspiration: preoperative axillary staging vs. postoperative surveillance for breast cancers.

Despite the common use of ultrasound (US)-guided fine-needle aspiration (FNA) for axillary node (AN) in breast cancer patients, only a limited number of studies are available regarding the diagnostic performance of AN-FNA according to the suspicion level based on US findings. This study compares the outcomes of US-guided AN-FNA in breast cancer patients, differentiating between those undergoing staging and surveillance. A cross-sectional retrospective study with retrospective analysis was conducted on 767 consecutive AN-FNA procedures performed in 2017 at Samsung Medical Center in Seoul, with 654 for staging and 113 for surveillance in breast cancer patients. The radiologists performed axillary US and the specific finding was prospectively classified into the AN-reporting and data system (AN-RADS) category 3-5 before FNA. The malignancy rate of each category was evaluated. The chi-square test, with or without Bonferroni correction, or Fisher's exact test was used to compare the malignancy rates between the staging and surveillance groups for each category. Among the 767 AN-FNAs, 424 (55.3%) were malignant. The malignancy rate was significantly higher in the staging group (59.5%) than in the surveillance group (31.0%, P<0.0001). The distribution of AN-RADS categories differed between the groups (P=0.015), with 4A being the most common. The malignancy rates in categories 3, 4A, 4B, 4C, and 5 were as follows: 5.6%, 36.0%, 77.4%, 87.7%, and 98.4% in the staging group, and 0.0%, 9.7%, 53.3%, 88.9%, and 100% in the surveillance group. The malignancy rate was significantly different between the two groups only in category 4A (P=0.0001). AN-FNA according to AN-RADS category appears to be an appropriate method for determination of axillary nodal status. Overall malignancy rate of AN-FNA in breast cancer patients was higher in the staging group than in the surveillance group. According to the suspicion level, the difference between two groups was significant only in category 4A.