Prediction of axillary lymph node metastasis using a magnetic resonance imaging radiomics model of invasive breast cancer primary tumor.

BACKGROUND: This study investigated the clinical value of breast magnetic resonance imaging (MRI) radiomics for predicting axillary lymph node metastasis (ALNM) and to compare the discriminative abilities of different combinations of MRI sequences. METHODS: This study included 141 patients diagnosed with invasive breast cancer from two centers (center 1: n = 101, center 2: n = 40). Patients from center 1 were randomly divided into training set and test set 1. Patients from center 2 were assigned to the test set 2. All participants underwent preoperative MRI, and four distinct MRI sequences were obtained. The volume of interest (VOI) of the breast tumor was delineated on the dynamic contrast-enhanced (DCE) postcontrast phase 2 sequence, and the VOIs of other sequences were adjusted when required. Subsequently, radiomics features were extracted from the VOIs using an open-source package. Both single- and multisequence radiomics models were constructed using the logistic regression method in the training set. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and precision of the radiomics model for the test set 1 and test set 2 were calculated. Finally, the diagnostic performance of each model was compared with the diagnostic level of junior and senior radiologists. RESULTS: The single-sequence ALNM classifier derived from DCE postcontrast phase 1 had the best performance for both test set 1 (AUC = 0.891) and test set 2 (AUC = 0.619). The best-performing multisequence ALNM classifiers for both test set 1 (AUC = 0.910) and test set 2 (AUC = 0.717) were generated from DCE postcontrast phase 1, T2-weighted imaging, and diffusion-weighted imaging single-sequence ALNM classifiers. Both had a higher diagnostic level than the junior and senior radiologists. CONCLUSIONS: The combination of DCE postcontrast phase 1, T2-weighted imaging, and diffusion-weighted imaging radiomics features had the best performance in predicting ALNM from breast cancer. Our study presents a well-performing and noninvasive tool for ALNM prediction in patients with breast cancer.

Utility of a breast biopsy clip and a point marker system in tailored axillary surgery for patients with breast cancer after neoadjuvant chemotherapy.

BACKGROUND: Tailored axillary surgery (TAS) is a new approach for selective removal of metastatic lymph nodes. This study evaluated the safety and utility of TAS using a breast biopsy clip inserted into a metastatic lymph node and a point marker consisting of a short hook wire and nylon thread to remove the clipped lymph node. METHODS: Patients with breast cancer and clinically confirmed metastases to one-to-three axillary lymph nodes were included in this study. A breast biopsy clip was inserted into the metastatic lymph nodes before neoadjuvant chemotherapy. TAS was performed in patients with ycN0 disease after neoadjuvant chemotherapy. The lymph nodes containing the clips were removed using a point marker. The success criteria for TAS were the removal of the lymph node into which the clip was inserted using a point marker and the identification of the sentinel lymph node. The false-negative rate was calculated for cases in which TAS and axillary lymph node dissection were performed. RESULTS: Thirty individuals from two institutions were enrolled between May 2021 and November 2022, of whom 20 underwent TAS. Ten patients had clinically positive axillary lymph nodes and underwent axillary lymph node dissection. No adverse events were observed in any patient using the clips or point markers. TAS was successful in 18 of the 20 patients (90%). Seven patients underwent TAS and axillary lymph node dissection with a false-negative rate of 0%. CONCLUSION: The use of clips and point markers to perform TAS is clinically feasible.

Can negative axillary ultrasound reliably predict pathologically negative axillary lymph node status in breast cancer patients with cT ≤3 cm, cN0, and HER2-positive?-a retrospective, single-institution study.

Background: Breast cancer (BC) is the leading cancer in women globally, with human epidermal growth factor receptor 2 (HER2)-positive subtype accounting for 15-20% of cases and exhibiting aggressive behavior. The standard of care for operable BC has evolved to include neoadjuvant systemic therapy, which can guide treatment decisions and improve outcomes, particularly in HER2+ BC. This study aims to investigate whether axillary ultrasound has a good negative predictive value (NPV) for early HER2 BC patients and to identify clinicopathological factors that can impact the axillary lymph node metastasis. Methods: This retrospective, single-center study evaluated the medical records of 135 patients with HER2+ BC, cT ≤3 cm, and clinically negative axillary lymph nodes from 2018 to 2020. The study aimed to determine the NPV of axillary ultrasound for pathologically negative axillary lymph node status and to identify factors associated with axillary lymph node metastasis. Results: The NPV of axillary ultrasound was 78.5%, increasing to 89.6% and 93.3% when considering 0-1 and 0-2 metastatic lymph nodes, respectively. Lymphovascular invasion (LVI) was significantly associated with axillary lymph node metastasis, with a 2.2-fold increased risk. Conclusions: Axillary ultrasound shows good predictive value for axillary lymph node negativity in HER2+ BC patients with small tumors. However, the presence of LVI increases the risk of metastasis, suggesting a need for neoadjuvant chemotherapy. These findings contribute to personalized treatment strategies for early HER2+ BC, emphasizing the role of axillary ultrasound in clinical decision-making.

DLCNBC-SA: a model for assessing axillary lymph node metastasis status in early breast cancer patients.

Background: Axillary lymph node (ALN) status is a crucial prognostic indicator for breast cancer metastasis, with manual interpretation of whole slide images (WSIs) being the current standard practice. However, this method is subjective and time-consuming. Recent advancements in deep learning-based methods for medical image analysis have shown promise in improving clinical diagnosis. This study aims to leverage these technological advancements to develop a deep learning model based on features extracted from primary tumor biopsies for preoperatively identifying ALN metastasis in early-stage breast cancer patients with negative nodes. Methods: We present DLCNBC-SA, a deep learning-based network specifically tailored for core needle biopsy and clinical data feature extraction, which integrates a self-attention mechanism (CNBC-SA). The proposed model consists of a feature extractor based on convolutional neural network (CNN) and an improved self-attention mechanism module, which can preserve the independence of features in WSIs for analysis and enhancement to provide rich feature representation. To validate the performance of the proposed model, we conducted comparative experiments and ablation studies using publicly available datasets, and verification was performed through quantitative analysis. Results: The comparative experiment illustrates the superior performance of the proposed model in the task of binary classification of ALNs, as compared to alternative methods. Our method achieved outstanding performance [area under the curve (AUC): 0.882] in this task, significantly surpassing the state-of-the-art (SOTA) method on the same dataset (AUC: 0.862). The ablation experiment reveals that incorporating RandomRotation data augmentation technology and utilizing Adadelta optimizer can effectively enhance the performance of the proposed model. Conclusions: The experimental results demonstrate that the model proposed in this paper outperforms the SOTA model on the same dataset, thereby establishing its reliability as an assistant for pathologists in analyzing WSIs of breast cancer. Consequently, it significantly enhances both the efficiency and accuracy of doctors during the diagnostic process.

A cutting-edge deep learning-and-radiomics-based ultrasound nomogram for precise prediction of axillary lymph node metastasis in breast cancer patients ≥ 75 years.

Objective: The objective of this study was to develop a deep learning-and-radiomics-based ultrasound nomogram for the evaluation of axillary lymph node (ALN) metastasis risk in breast cancer patients ≥ 75 years. Methods: The study enrolled breast cancer patients ≥ 75 years who underwent either sentinel lymph node biopsy or ALN dissection at Fudan University Shanghai Cancer Center. DenseNet-201 was employed as the base model, and it was trained using the Adam optimizer and cross-entropy loss function to extract deep learning (DL) features from ultrasound images. Additionally, radiomics features were extracted from ultrasound images utilizing the Pyradiomics tool, and a Rad-Score (RS) was calculated employing the Lasso regression algorithm. A stepwise multivariable logistic regression analysis was conducted in the training set to establish a prediction model for lymph node metastasis, which was subsequently validated in the validation set. Evaluation metrics included area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1-score. The calibration of the model's performance and its clinical prediction accuracy were assessed using calibration curves and decision curves respectively. Furthermore, integrated discrimination improvement and net reclassification improvement were utilized to quantify enhancements in RS. Results: Histological grade, axillary ultrasound, and RS were identified as independent risk factors for predicting lymph node metastasis. The integration of the RS into the clinical prediction model significantly improved its predictive performance, with an AUC of 0.937 in the training set, surpassing both the clinical model and the RS model alone. In the validation set, the integrated model also outperformed other models with AUCs of 0.906, 0.744, and 0.890 for the integrated model, clinical model, and RS model respectively. Experimental results demonstrated that this study's integrated prediction model could enhance both accuracy and generalizability. Conclusion: The DL and radiomics-based model exhibited remarkable accuracy and reliability in predicting ALN status among breast cancer patients ≥ 75 years, thereby contributing to the enhancement of personalized treatment strategies' efficacy and improvement of patients' quality of life.

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

OBJECTIVE: To develop a nomogram for predicting axillary lymph node metastasis (ALNM) in patients with invasive breast cancer. METHODS: We included 307 patients with clinicopathologically confirmed invasive breast cancer. The cohort was divided into a training group (n=215) and a validation group (n=92). Ultrasound images were used to extract radiomics features. The least absolute shrinkage and selection operator (LASSO) algorithm helped select pertinent features, from which Radiomics Scores (Radscores) were calculated using the LASSO regression equation. We developed three logistic regression models based on Radscores and 2D image features, and assessed the models' performance in the validation group. A nomogram was created from the best-performing model. RESULTS: In the training set, the area under the curve (AUC) for the Radscore model, 2D feature model, and combined model were 0.76, 0.85, and 0.88, respectively. In the validation set, the AUCs were 0.71, 0.78, and 0.83, respectively. The combined model demonstrated good calibration and promising clinical utility. CONCLUSION: Our ultrasound-based radiomics nomogram can accurately and non-invasively predict ALNM in breast cancer, suggesting potential clinical applications to optimize surgical and medical strategies.

Integration of Radiomics and Immune-Related Genes Signatures for Predicting Axillary Lymph Node Metastasis in Breast Cancer.

BACKGROUND: To develop a radiogenomics nomogram for predicting axillary lymph node (ALN) metastasis in breast cancer and reveal underlying associations between radiomics features and biological pathways. MATERIALS AND METHODS: This study included 1062 breast cancer patients, 90 patients with both DCE-MRI and gene expression data. The optimal immune-related genes and radiomics features associated with ALN metastasis were firstly calculated, and corresponding feature signatures were constructed to further validate their performances in predicting ALN metastasis. The radiogenomics nomogram for predicting the risk of ALN metastasis was established by integrating radiomics signature, immune-related genes (IRG) signature, and critical clinicopathological factors. Gene modules associated with key radiomics features were identified by weighted gene co-expression network analysis (WGCNA) and submitted to functional enrichment analysis. Gene set variation analysis (GSVA) and correlation analysis were performed to investigate the associations between radiomics features and biological pathways. RESULTS: The radiogenomics nomogram showed promising predictive power for predicting ALN metastasis, with AUCs of 0.973 and 0.928 in the training and testing groups, respectively. WGCNA and functional enrichment analysis revealed that gene modules associated with key radiomics features were mainly enriched in breast cancer metastasis-related pathways, such as focal adhesion, ECM-receptor interaction, and cell adhesion molecules. GSVA also identified pathway activities associated with radiomics features such as glycogen synthesis, integration of energy metabolism. CONCLUSION: The radiogenomics nomogram can serve as an effective tool to predict the risk of ALN metastasis. This study provides further evidence that radiomics phenotypes may be driven by biological pathways related to breast cancer metastasis.

Identifying Key Genes Involved in Axillary Lymph Node Metastasis in Breast Cancer Using Advanced RNA-Seq Analysis: A Methodological Approach with GLMQL and MAS.

Our study aims to address the methodological challenges frequently encountered in RNA-Seq data analysis within cancer studies. Specifically, it enhances the identification of key genes involved in axillary lymph node metastasis (ALNM) in breast cancer. We employ Generalized Linear Models with Quasi-Likelihood (GLMQLs) to manage the inherently discrete and overdispersed nature of RNA-Seq data, marking a significant improvement over conventional methods such as the t-test, which assumes a normal distribution and equal variances across samples. We utilize the Trimmed Mean of M-values (TMMs) method for normalization to address library-specific compositional differences effectively. Our study focuses on a distinct cohort of 104 untreated patients from the TCGA Breast Invasive Carcinoma (BRCA) dataset to maintain an untainted genetic profile, thereby providing more accurate insights into the genetic underpinnings of lymph node metastasis. This strategic selection paves the way for developing early intervention strategies and targeted therapies. Our analysis is exclusively dedicated to protein-coding genes, enriched by the Magnitude Altitude Scoring (MAS) system, which rigorously identifies key genes that could serve as predictors in developing an ALNM predictive model. Our novel approach has pinpointed several genes significantly linked to ALNM in breast cancer, offering vital insights into the molecular dynamics of cancer development and metastasis. These genes, including ERBB2, CCNA1, FOXC2, LEFTY2, VTN, ACKR3, and PTGS2, are involved in key processes like apoptosis, epithelial-mesenchymal transition, angiogenesis, response to hypoxia, and KRAS signaling pathways, which are crucial for tumor virulence and the spread of metastases. Moreover, the approach has also emphasized the importance of the small proline-rich protein family (SPRR), including SPRR2B, SPRR2E, and SPRR2D, recognized for their significant involvement in cancer-related pathways and their potential as therapeutic targets. Important transcripts such as H3C10, H1-2, PADI4, and others have been highlighted as critical in modulating the chromatin structure and gene expression, fundamental for the progression and spread of cancer.

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.

Value of Contrast-Enhanced Ultrasound Combined with Immune-Inflammatory Markers in Predicting Axillary Lymph Node Metastasis of Breast Cancer.

RATIONALE AND OBJECTIVES: To evaluate the diagnostic performance of contrast-enhanced ultrasound (CEUS) combined with immune-inflammatory markers in predicting axillary lymph node metastasis (ALNM) in breast cancer patients. METHODS: From January 2020 to June 2023, the clinicopathological data and ultrasound features of 401 breast cancer patients who underwent biopsy or surgery were recorded. Patients were randomly divided into a training set (321 patients) and a validation set (80 patients). The risk factors for ALNM were determined using univariate, least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression analysis, and prediction models were constructed. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were used to assess their diagnostic performance. RESULTS: Logistic regression analysis demonstrated that systemic immunoinflammatory index (SII), CA125, Ki67, pathological type, lesion size, enhancement pattern and Breast Imaging Reporting and Data System (BI-RADS) category were significant risk factors for ALNM. Three different models were constructed, and the combined model yielded an AUC of 0.903, which was superior to the clinical model (AUC=0.790) and ultrasound model (AUC=0.781). A nomogram was constructed based on the combined model, calibration curves and DCA demonstrated its satisfactory performance in predicting ALNM. CONCLUSION: The nomogram combining ultrasound features and immune-inflammatory markers could serve as a valuable instrument for predicting ALNM in breast cancer patients. DATA AVAILABILITY STATEMENT: The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding authors.

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

INTRODUCTION: The 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. METHODS: We 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. RESULTS: A 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. CONCLUSION: This 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.

Predicting axillary lymph node metastasis in breast cancer patients: A radiomics-based multicenter approach with interpretability analysis.

PURPOSE: To develop a MRI-based radiomics model, integrating the intratumoral and peritumoral imaging information to predict axillary lymph node metastasis (ALNM) in patients with breast cancer and to elucidate the model's decision-making process via interpretable algorithms. METHODS: This study included 376 patients from three institutions who underwent contrast-enhanced breast MRI between 2021 and 2023. We used multiple machine learning algorithms to combine peritumoral, intratumoral, and radiological characteristics with the building of radiological, radiomics, and combined models. The model's performance was compared based on the area under the curve (AUC) obtained from the receiver operating characteristic analysis and interpretable machine learning techniques to analyze the operating mechanism of the model. RESULTS: The radiomics model, incorporating features from both intratumoral tissue and the 3 mm peritumoral region and utilizing the backpropagation neural network (BPNN) algorithm, demonstrated superior diagnostic efficacy, achieving an AUC of 0.820. The AUC of the combination of the RAD score, clinical T stage, and spiculated margin was as high as 0.855. Furthermore, we conducted SHapley Additive exPlanations (SHAP) analysis to evaluate the contributions of RAD score, clinical T stage, and spiculated margin in ALNM status prediction. CONCLUSIONS: The interpretable radiomics model we propose can better predict the ALNM status of breast cancer and help inform clinical treatment decisions.

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

BACKGROUND: The 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). METHODS: A 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. RESULTS: The 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). CONCLUSIONS: Tumor 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.

OBJECTIVES: Developing 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. METHODS: Breast 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. RESULTS: In 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%. CONCLUSION: The 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 STATEMENT: Our AI model based on pre- and post-neoadjuvant chemotherapy ultrasound can accurately predict axillary lymph node metastasis and assist sonographer's axillary diagnosis. KEY POINTS: Axillary 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.

Characteristics and risk factors of axillary lymph node metastasis of microinvasive breast cancer.

PURPOSE: To select patients who would benefit most from sentinel lymph node biopsy (SLNB) by investigating the characteristics and risk factors of axillary lymph node metastasis (ALNM) in microinvasive breast cancer (MIBC). METHODS: This retrospective study included 1688 patients with MIBC who underwent breast surgery with axillary staging at the Asan Medical Center from 1995 to 2020. RESULTS: Most patients underwent SLNB alone (83.5%). Seventy (4.1%) patients were node-positive, and the majority had positive lymph nodes < 10 mm, with micro-metastases occurring frequently (n = 37; 55%). Node-positive patients underwent total mastectomy and axillary lymph node dissection (ALND) more than breast-conserving surgery (BCS) and SLNB compared with node-negative patients (p < 0.001). In the multivariate analysis, independent predictors of ALNM included young age [odds ratio (OR) 0.959; 95% confidence interval (CI) 0.927-0.993; p = 0.019], ALND (OR 11.486; 95% CI 5.767-22.877; p < 0.001), number of lymph nodes harvested (≥ 5) (OR 3.184; 95% CI 1.555-6.522; p < 0.001), lymphovascular invasion (OR 6.831; 95% CI 2.386-19.557; p < 0.001), presence of multiple microinvasion foci (OR 2.771; 95% CI 1.329-5.779; p = 0.007), prominent lymph nodes in preoperative imaging (OR 2.675; 95% CI 1.362-5.253; p = 0.004), and hormone receptor positivity (OR 2.491; 95% CI 1.230-5.046; p = 0.011). CONCLUSION: Low ALNM rate (4.1%) suggests that routine SLNB for patients with MIBC is unnecessary but can be valuable for patients with specific risk factors. Ongoing trials for omitting SLNB in early breast cancer, and further subanalyses focusing on rare populations with MIBC are necessary.

Deep Learning Prediction of Axillary Lymph Node Metastasis in Breast Cancer Patients Using Clinical Implication-Applied Preprocessed CT Images.

Background: Accurate detection of axillary lymph node (ALN) metastases in breast cancer is crucial for clinical staging and treatment planning. This study aims to develop a deep learning model using clinical implication-applied preprocessed computed tomography (CT) images to enhance the prediction of ALN metastasis in breast cancer patients. Methods: A total of 1128 axial CT images of ALN (538 malignant and 590 benign lymph nodes) were collected from 523 breast cancer patients who underwent preoperative CT scans between January 2012 and July 2022 at Hallym University Medical Center. To develop an optimal deep learning model for distinguishing metastatic ALN from benign ALN, a CT image preprocessing protocol with clinical implications and two different cropping methods (fixed size crop [FSC] method and adjustable square crop [ASC] method) were employed. The images were analyzed using three different convolutional neural network (CNN) architectures (ResNet, DenseNet, and EfficientNet). Ensemble methods involving and combining the selection of the two best-performing CNN architectures from each cropping method were applied to generate the final result. Results: For the two different cropping methods, DenseNet consistently outperformed ResNet and EfficientNet. The area under the receiver operating characteristic curve (AUROC) for DenseNet, using the FSC and ASC methods, was 0.934 and 0.939, respectively. The ensemble model, which combines the performance of the DenseNet121 architecture for both cropping methods, delivered outstanding results with an AUROC of 0.968, an accuracy of 0.938, a sensitivity of 0.980, and a specificity of 0.903. Furthermore, distinct trends observed in gradient-weighted class activation mapping images with the two cropping methods suggest that our deep learning model not only evaluates the lymph node itself, but also distinguishes subtler changes in lymph node margin and adjacent soft tissue, which often elude human interpretation. Conclusions: This research demonstrates the promising performance of a deep learning model in accurately detecting malignant ALNs in breast cancer patients using CT images. The integration of clinical considerations into image processing and the utilization of ensemble methods further improved diagnostic precision.

Multi-modality radiomics model predicts axillary lymph node metastasis of breast cancer using MRI and mammography.

OBJECTIVES: We aimed to develop a multi-modality model to predict axillary lymph node (ALN) metastasis by combining clinical predictors with radiomic features from magnetic resonance imaging (MRI) and mammography (MMG) in breast cancer. This model might potentially eliminate unnecessary axillary surgery in cases without ALN metastasis, thereby minimizing surgery-related complications. METHODS: We retrospectively enrolled 485 breast cancer patients from two hospitals and extracted radiomics features from tumor and lymph node regions on MRI and MMG images. After feature selection, three random forest models were built using the retained features, respectively. Significant clinical factors were integrated with these radiomics models to construct a multi-modality model. The multi-modality model was compared to radiologists' diagnoses on axillary ultrasound and MRI. It was also used to assist radiologists in making a secondary diagnosis on MRI. RESULTS: The multi-modality model showed superior performance with AUCs of 0.964 in the training cohort, 0.916 in the internal validation cohort, and 0.892 in the external validation cohort. It surpassed single-modality models and radiologists' ALN diagnosis on MRI and axillary ultrasound in all validation cohorts. Additionally, the multi-modality model improved radiologists' MRI-based ALN diagnostic ability, increasing the average accuracy from 70.70 to 78.16% for radiologist A and from 75.42 to 81.38% for radiologist B. CONCLUSION: The multi-modality model can predict ALN metastasis of breast cancer accurately. Moreover, the artificial intelligence (AI) model also assisted the radiologists to improve their diagnostic ability on MRI. CLINICAL RELEVANCE STATEMENT: The multi-modality model based on both MRI and mammography images allows preoperative prediction of axillary lymph node metastasis in breast cancer patients. With the assistance of the model, the diagnostic efficacy of radiologists can be further improved. KEY POINTS: • We developed a novel multi-modality model that combines MRI and mammography radiomics with clinical factors to accurately predict axillary lymph node (ALN) metastasis, which has not been previously reported. • Our multi-modality model outperformed both the radiologists' ALN diagnosis based on MRI and axillary ultrasound, as well as single-modality radiomics models based on MRI or mammography. • The multi-modality model can serve as a potential decision support tool to improve the radiologists' ALN diagnosis on MRI.

Association and predictive value of contrast­enhanced ultrasound features with axillary lymph node metastasis in primary breast cancer.

Primary breast cancer is the most common malignant tumor in women worldwide, and axillary lymph node metastasis (ALNM) is an important marker of disease progression in patients with breast cancer. The objective of the present study was to analyze the association between contrast-enhanced ultrasound (CEUS) features and ALNM in primary breast cancer and its predictive value. A total of 120 patients with breast cancer were assigned to the non-metastatic group (n=70) and metastatic group (n=50). The factors influencing ALNM were explored by multivariate logistic regression analysis. The consistency of CEUS, ordinary ultrasonography and pathological examination in the diagnosis of the ALNM of breast cancer was evaluated by consistency testing. The sensitivity, specificity and consistency rate of CEUS features and ordinary ultrasonography were analyzed by receiver operating characteristic curve and four-fold table analyses. High enhancement amplitude, centripetal enhancement sequence, increased maximum cortical thickness, high peak intensity and a larger area under the curve of lymph nodes were more commonly found in the metastatic group than in the non-metastatic group. The lymph node aspect ratio and time to peak were lower in the metastatic group than the non-metastatic group. The time to peak was a protective factor for ALNM in patients with breast cancer. The sensitivity, specificity and coincidence rate with pathological examination of CEUS in the diagnosis of ALNM were 92.00, 90.00 and 90.83%, while these of ordinary ultrasonography were 76.00, 80.00 and 78.33%, respectively. The consistency test indicated that CEUS and pathological examination were consistent in the diagnosis of ALNM in patients with breast cancer, with a κ value of 0.816, indicating a good consistency. The κ value of ordinary ultrasonography and pathological examination was 0.763, also indicating a good consistency. However, these results indicate that CEUS is more valuable than ordinary ultrasonography in the diagnosis of ALNM in cases of breast cancer. In conclusion, the present study indicates that CEUS features were influencing factors associated with ALNM in patients with breast cancer and may serve as an important reference for the preoperative prediction of ALNM in breast cancer.

Ultrasonic features of automated breast volume scanner (ABVS) and handheld ultrasound (HHUS) combined with molecular biomarkers in predicting axillary lymph node metastasis of clinical T1-T2 breast cancer.

Background: In the post-American College of Surgeons Oncology Group Z0011 trial era, clinicians are attempting to preoperatively evaluate axillary lymph node (ALN) status using ultrasound. However, the value of preoperative ultrasound examination remains uncertain. The study aimed to investigate the ultrasonic features of automated breast volume scanner (ABVS) and handheld ultrasound (HHUS), in combination with molecular biomarkers, to predict the risk of ALN metastasis (ALNM) in clinical T1-T2 breast cancer. Methods: A retrospective case-control analysis was conducted on 168 patients with clinical T1-T2 breast cancer at Peking University First Hospital between January 2013 and August 2021. Preoperative ABVS and HHUS examinations were performed. According to the pathology results of the ALN, patients were divided into metastatic and nonmetastatic groups. Logistic regression analyses were used to analyze the ultrasonic characteristics of ABVS and HHUS on clinical T1-T2 breast cancer, and molecular biomarkers were incorporated to predict the risk of ALNM. Results: Of the 168 patients, 88 (52.4%) had ipsilateral ALNM while 80 (47.6%) had no ipsilateral ALNM. The univariate analysis showed that shorter tumor-skin distance (P=0.011), the Adler blood flow grade of II-III (P=0.014), and larger tumor size on ABVS (P<0.001) were associated with ALNM. The multivariate logistic analysis showed that these three risk factors, including the tumor-skin distance [odds ratio (OR) =0.279; P=0.024], the Adler blood flow grade (OR =2.164; P=0.046), and the tumor size on ABVS (OR =1.033; P=0.002), were independent predictive parameters. Conclusions: The tumor-skin distance, tumor size on ABVS, and Adler blood flow grade have diagnostic value for ALNM in clinical T1-T2 breast cancer.