Axillary Lymph Node Metastasis Research Articles

Prediction of High Nodal Burden in Patients With Sentinel Node–Positive Luminal ERBB2-Negative Breast Cancer

In patients with clinically node-negative (cN0) breast cancer and 1 or 2 sentinel lymph node (SLN) macrometastases, omitting completion axillary lymph node dissection (CALND) is standard. High nodal burden (≥4 axillary nodal metastases) is an indication for intensified treatment in luminal breast cancer; hence, abstaining from CALND may result in undertreatment. To develop a prediction model for high nodal burden in luminal ERBB2-negative breast cancer (all histologic types and lobular breast cancer separately) without CALND. The prospective Sentinel Node Biopsy in Breast Cancer: Omission of Axillary Clearance After Macrometastases (SENOMAC) trial randomized patients 1:1 to CALND or its omission from January 2015 to December 2021 among adult patients with cN0 T1-T3 breast cancer and 1 or 2 SLN macrometastases across 5 European countries. The cohort was randomly split into training (80%) and test (20%) sets, with equal proportions of high nodal burden. Prediction models were developed by multivariable logistic regression in the complete luminal ERBB2-negative cohort and a lobular breast cancer subgroup. Nomograms were constructed. The present diagnostic/prognostic study presents the results of a prespecified secondary analysis of the SENOMAC trial. Herein, only patients with luminal ERBB2-negative tumors assigned to CALND were selected. Data analysis for this article took place from June 2023 to April 2024. Predictors of high nodal burden. High nodal burden was defined as ≥4 axillary nodal metastases. The luminal prediction model was evaluated regarding discrimination and calibration. Of 1010 patients (median [range] age, 61 [34-90] years; 1006 [99.6%] female and 4 [0.4%] male), 138 (13.7%) had a high nodal burden and 212 (21.0%) had lobular breast cancer. The model in the training set (n = 804) included number of SLN macrometastases, presence of SLN micrometastases, SLN ratio, presence of SLN extracapsular extension, and tumor size (not included in lobular subgroup). Upon validation in the test set (n = 201), the area under the receiver operating characteristic curve (AUC) was 0.74 (95% CI, 0.62-0.85) and the calibration was satisfactory. At a sensitivity threshold of ≥80%, all but 5 low-risk patients were correctly classified corresponding to a negative predictive value of 94%. The prediction model for the lobular subgroup reached an AUC of 0.74 (95% CI, 0.66-0.83). The predictive models and nomograms may facilitate systemic treatment decisions without exposing patients to the risk of arm morbidity due to CALND. External validation is needed. ClinicalTrials.gov Identifier: NCT02240472.

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

BackgroundThis 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.MethodsThis 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.ResultsThe 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.ConclusionsThe 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.

Secretory Carcinoma of the Breast: Radiologic-Pathologic Correlation.

Secretory carcinoma is a rare, low-grade, special histological type of invasive breast carcinoma. Although it is the most common primary breast cancer in the pediatric population, most cases are diagnosed in adults, with a median age of 48 years (range 3 to 91 years). It most often presents as a painless and slowly growing palpable lump. Imaging findings are nonspecific. Secretory carcinomas have abundant periodic acid-Schiff positive intracytoplasmic and extracellular secretions on histopathology. Nearly all secretory carcinomas have mild to moderate nuclear pleomorphism with low mitotic activity. Over 80% (86/102) of secretory carcinomas display the translocation of t(12;15)(p13;q25), resulting in ETV6::NTRK3 gene fusion. Secretory carcinoma generally has an indolent course and has a better prognosis and overall survival than invasive breast carcinoma of no special type. A good prognosis is associated with age <20 years, tumor size <2 cm, and ≤3 axillary lymph node metastases. Metastases beyond the ipsilateral axillary lymph nodes are rare, with the most common sites involving the lung and liver. Except for the potential addition of targeted drug therapy for NTRK fusion-positive tumors, the treatment approach is otherwise similar to invasive breast carcinomas of similar receptor status.

Spectral Ultrasound Combined With Clinical Pathological Parameters in Prediction of Axillary Lymph Node Metastasis in Breast Cancer.

To explore the clinical value of the nomogram based on spectral Doppler ultrasound combined with clinical pathological parameter in predicting axillary lymph node metastasis in breast cancer. We prospectively gathered clinicopathologic and ultrasonic data from 240 patients confirmed breast cancer. The risk factors of axillary lymph node metastasis were analyzed by univariate and multivariate logistic regression, and the prediction model was established. The model calibration, predictive ability, and diagnostic efficiency in the training set and the testing set were analyzed by receiver operating characteristic curve and calibration curve analysis, respectively. Univariate analysis showed that lymph node metastasis was related with tumor size, Ki-67, axillary ultrasound, ultrasound spectral quantitative parameter, internal echo, and calcification (P < .05). Multivariate logistic regression analysis showed that the Ki-67, axillary ultrasound, quantitative parameter (the mean of the mid-band fit in tumor and posterior tumor) were independent risk factors of axillary lymph node metastasis (P < .05). The models developed using Ki-67, axillary ultrasound, and quantitative parameters for predicting axillary lymph node metastasis demonstrated an area under the receiver operating characteristic curve of 0.83. Additionally, the prediction model exhibited outstanding predictability for axillary lymph node metastasis, as evidenced by a Harrell C-index of 0.83 (95% confidence interval 0.73-0.93). Axillary ultrasound combined with Ki-67 and spectral ultrasound parameters has the potential to predict axillary lymph node metastasis in breast cancer, which is superior to axillary ultrasound alone.

Development and Assessment of a Predictive Model for Ki-67 Expression Using Ultrasound Indicators and Non-Morphological Magnetic Resonance Imaging Parameters Before Breast Cancer Therapy.

To formulate a predictive model for assessing Ki-67 expression in breast cancer by integrating pre-treatment ultrasound features with non-morphological magnetic resonance imaging (MRI) parameters, encompassing functional and hemodynamic indicators. A retrospective study was conducted on 167 patients. All patients underwent a breast mass biopsy for histopathological and Ki-67 analysis prior to neoadjuvant chemotherapy (NAC) treatment. Additionally, all patients underwent ultrasonography and MRI examinations prior to the biopsy. The recorded variables were Ki-67, apparent diffusion coefficient (ADC) values, Max Slope, time to peak (TTP), signal enhancement ratio (SER), early enhancement rate (EER), time-signal intensity curve (TIC), tumor maximum diameter, tumor margins and boundaries, aspect ratio, microcalcification, color Doppler flow imaging grading, resistance index (RI), and axillary lymph node metastasis. Statistical analysis was performed using the R software package. Normally distributed continuous data are presented as mean ± standard deviation (SD), skewed continuous data as median, and categorical variables as frequency or percentage. The dataset was randomly divided into a modeling group and a validation group following a 7:3 ratio, employing a predetermined random seed. The selection of variables was conducted using the random forest algorithm. Specifically, in the initial analysis, we trained a random forest model using all available variables. By evaluating the Gini importance scores of each variable, we identified those that contributed the most to predicting Ki-67 expression. The predictive model for Ki-67 expression was constructed using selected variables: Maximum Diameter, ADC value, SER value, Max Slope value, TTP value, and EER value. Within the validation group, the evaluation metrics demonstrated an Area under the curve of 0.961 with a 95% confidence interval ranging from 0.865 to 0.995. The model achieved a kappa score of 1.00, precision of 0.949, recall of 1, an F1 score of 0.974, sensitivity of 100%, specificity of 85.71%, a positive predictive value of 94.87%, and a negative predictive value of 100%. The combination of non-morphological MRI parameters and pre-treatment ultrasound features in a breast cancer prediction model powered by RF machine learning demonstrated favorable clinical outcomes and improved diagnostic performance.

Predicting axillary lymph node metastasis in breast cancer based on ultrasound radiofrequency time-series analysis.

The status of axillary lymph nodes (ALN) plays a critical role in the management of patients with breast cancer. It is an urgent demand to develop highly accurate, non-invasive methods for predicting ALN status. To evaluate the efficacy of ultrasound radiofrequency (URF) time-series parameters, in combination with clinical data, in predicting ALN metastasis in patients with breast cancer. We prospectively gathered clinicopathologic and ultrasonic data from patients diagnosed with breast cancer. Various machine-learning (ML) models were developed using all available features to determine the most efficient diagnostic model. Subsequently, distinct prediction models were created using the optimal ML model, and their diagnostic performances were evaluated and compared. The study encompassed 240 patients, of whom 88 had lymph node metastases. A leave-one-out cross-validation (LOOCV) method was used to split the entire dataset into training and testing subsets. The random forest ML model outperformed the other algorithms, with an area under the curve (AUC) of 0.92. Prediction models based on clinical, ultrasonic, URF parameters, clinical + ultrasonic, clinical + URF, and ultrasonic + URF parameters had AUCs of 0.56, 0.79, 0.78, 0.90, 0.80, and 0.84, respectively, in the testing set. The comprehensive diagnostic model (clinical + ultrasonic + URF parameters) demonstrated strong diagnostic capability, with an AUC of 0.94 in the testing set, exceeding any single prediction model. The combined model (clinical + ultrasonic + URF parameters) could be used preoperatively to predict lymph node status, offering valuable input for the design of individualized surgical approaches.

An unsupervised learning model based on CT radiomics features accurately predicts axillary lymph node metastasis in breast cancer patients: diagnostic study.

The accuracy of traditional clinical methods for assessing the metastatic status of axillary lymph nodes (ALNs) is unsatisfactory. In this study, the authors propose the use of radiomic technology and three-dimensional (3D) visualization technology to develop an unsupervised learning model for predicting axillary lymph node metastasis in patients with breast cancer (BC), aiming to provide a new method for clinical axillary lymph node assessment in patients with this disease. In this study, we retrospectively analyzed the data of 350 patients with invasive BC who underwent lung-enhanced computed tomography (CT) and axillary lymph node dissection surgery at the Department of Breast Surgery of the Second Xiangya Hospital of Central South University. The authors used 3D visualization technology to create a 3D atlas of ALNs and identified the region of interest for the lymph nodes. Radiomic features were subsequently extracted and selected, and a prediction model for ALNs was constructed using the K-means unsupervised algorithm. To validate the model, the authors prospectively collected data from 128 BC patients who were clinically evaluated as negative at our center. Using 3D visualization technology, we extracted and selected a total of 36 CT radiomics features. The unsupervised learning model categorized 1737 unlabeled lymph nodes into two groups, and the analysis of the radiomic features between these groups indicated potential differences in lymph node status. Further validation with 1397 labeled lymph nodes demonstrated that the model had good predictive ability for axillary lymph node status, with an area under the curve of 0.847 (0.825-0.869). Additionally, the model's excellent predictive performance was confirmed in the 128 axillary clinical assessment negative cohort (cN0) and the 350 clinical assessment positive (cN+) cohort, for which the correct classification rates were 86.72 and 87.43%, respectively, which were significantly greater than those of clinical assessment methods. The authors created an unsupervised learning model that accurately predicts the status of ALNs. This approach offers a novel solution for the precise assessment of ALNs in patients with BC.

Photoacoustic/ultrasound dual-modality imaging of Sentinel lymph node with carbon nanoparticles

Abstract Sentinel lymph node (SLN) biopsy is a widely used method for identifying axillary lymph node metastasis in breast cancer patients, offering an avoidance of unnecessary axillarydissections and a lower incidence of lymphedema and other complications. However, the nuclide localization has added complexity to the procedure due to its accessibility. Methylene blue and other small molecule dyes have been widely used due to their convenience, but they have been found to have lower specificity and sensitivity. Carbon nanoparticles (CNPs), as a new type of tracer, are safe and show improved specificity. However, the significant optical absorption in human tissues still renders the detection of deep lymph nodes challenging. To overcome these challenges, we developed a high-sensitivity ultrasound probe optimized for photoacoustic imaging. Based on the new probe, we constructed a high-sensitivity photoacoustic/ultrasound dual-modality imaging system. A pilot clinical study has been conducted on two patients, preliminarily verifying the system’s ability to locate SLN when combined with the CNPs.

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.

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. 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. 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. 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.

Postoperative Radiotherapy Using VMAT for RNI Including IMN in Breast Cancer: Report of Short-term Follow-up.

The application of volumetric-modulated arc therapy (VMAT) in postoperative radiotherapy for breast cancer has recently garnered considerable interest, especially when regional lymph node irradiation (RNI), is extended to include the internal mammary node (IMN) region, along with whole-breast or chest wall irradiation. This study aimed to assess both acute and late toxicities, as well as breast cancer outcomes, during the observation period. This retrospective study aimed to assess the safety and feasibility of VMAT in postoperative radiotherapy, including the IMN region, for patients with breast cancer at our hospital. We included 33 eligible patients who received postoperative VMAT radiotherapy, including the IMN region, between November 2018 and July 2021. The primary indications for implementing VMAT were the presence of IMN metastases before primary systemic therapy or more than four axillary lymph node metastases. The median prescribed dose for the chest wall or conserved breast and regional lymph nodes was 50 Gy in 25 fractions. In 16 cases, a simultaneous boost (median, 10 Gy) was administered to residual lymph node metastases. VMAT facilitated a reduction in the mid- or high-dose range to organs at risk, exemplified by V20Gy and V40Gy of the ipsilateral lung, which were 24.10% and 6.84%, respectively, while ensuring adequate target dose irradiation, V45Gy=95.23%. No serious adverse events, including symptomatic radiation pneumonitis, occurred during the 15-month median observation period. Our findings demonstrate the safety and effectiveness of postoperative radiotherapy for breast cancer, including the IMN region, when using VMAT.

Comparison of the Performance of [18F]-FDG PET/CT and [18F]-FDG PET/MRI for Lymph Node Metastasis in Breast Cancer: A Systematic Review and Meta- Analysis.

The primary objective of this study was to conduct a comparative analysis of the diagnostic efficacy of [18F]-FDG PET/CT and [18F]-FDG PET/MRI in the detection of breast cancer lymph node metastasis. We conducted a comprehensive search on PubMed, Embase, and Web of Science databases, encompassing eligible articles until March 2023. The pooled sensitivity and specificity for [18F]-FDG PET/CT and [18F]-FDG PET/MRI have been reported as estimates with 95% Confidence Intervals (CIs) using a bivariate random-effect model. Utilizing the I square (I2) statistic, heterogeneity among pooled studies was evaluated. The quality assessment of the included studies was conducted using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) methodology. We included 18 studies (2057 patients). The sensitivity, specificity, and AUC (Area Under the Curve) values of [18F]-FDG PET/CT for overall lymph node metastasis in breast cancer have been found to be 0.58 (0.39 - 0.75), 0.83 (0.69-0.92), and 0.79 (0.75-0.82), respectively. Correspondingly, the values for [18F]-FDG PET/MRI were found to be 0.76 (0.60-0.88), 0.85 (0.77-0.91), and 0.89 (0.86-0.91), respectively. The sensitivity, specificity, and AUC values of [18F]-FDG PET/CT for axillary lymph node metastasis in breast cancer were 0.52 (0.37-0.67), 0.84 (0.68-0.92), and 0.73 (0.69-0.76), respectively. Correspondingly, the values for [18F]-FDG PET/MRI were 0.84 (0.76-0.89), 0.87 (0.75-0.94), and 0.86 (0.83-0.89), respectively. This study has suggested [18F]-FDG PET/MRI to have greater diagnostic power than [18F]-FDG PET/CT in detecting lymph node metastasis in breast cancer. However, the [18F]-FDG PET/MRI results have been obtained from a small sample size study, and more and larger prospective studies are needed for further confirmation on this issue.</p>.

Peritumoral edema enhances MRI-based deep learning radiomic model for axillary lymph node metastasis burden prediction in breast cancer

To investigate whether peritumoral edema (PE) could enhance deep learning radiomic (DLR) model in predicting axillary lymph node metastasis (ALNM) burden in breast cancer. Invasive breast cancer patients with preoperative MRI were retrospectively enrolled and categorized into low (< 2 lymph nodes involved (LNs+)) and high (≥ 2 LNs+) burden groups based on surgical pathology. PE was evaluated on T2WI, and intra- and peri-tumoral radiomic features were extracted from MRI-visible tumors in DCE-MRI. Deep learning models were developed for LN burden prediction in the training cohort and validated in an independent cohort. The incremental value of PE was evaluated through receiver operating characteristic (ROC) analysis, confirming the improvement in the area under the curve (AUC) using the Delong test. This was complemented by net reclassification improvement (NRI) and integrated discrimination improvement (IDI) metrics. The deep learning combined model, incorporating PE with selected radiomic features, demonstrated significantly higher AUC values compared to the MRI model and the DLR model in the training cohort (n = 177) (AUC: 0.953 vs. 0.849 and 0.867, p < 0.05) and the validation cohort (n = 111) (AUC: 0.963 vs. 0.883 and 0.882, p < 0.05). The complementary analysis demonstrated that PE significantly enhances the prediction performance of the DLR model (Categorical NRI: 0.551, p < 0.001; IDI = 0.343, p < 0.001). These findings were confirmed in the validation cohort (Categorical NRI: 0.539, p < 0.001; IDI = 0.387, p < 0.001). PE improved preoperative ALNM burden prediction of DLR model, facilitating personalized axillary management in breast cancer patients.

Non-invasive prediction of axillary lymph node dissection exemption in breast cancer patients post-neoadjuvant therapy: A radiomics and deep learning analysis on longitudinal DCE-MRI data

PurposeIn breast cancer (BC) patients with clinical axillary lymph node metastasis (cN+) undergoing neoadjuvant therapy (NAT), precise axillary lymph node (ALN) assessment dictates therapeutic strategy. There is a critical demand for a precise method to assess the axillary lymph node (ALN) status in these patients. Materials and methodsA retrospective analysis was conducted on 160 BC patients undergoing NAT at Fujian Medical University Union Hospital. We analyzed baseline and two-cycle reassessment dynamic contrast-enhanced MRI (DCE-MRI) images, extracting 3668 radiomic and 4096 deep learning features, and computing 1834 delta-radiomic and 2048 delta-deep learning features. Light Gradient Boosting Machine (LightGBM), Support Vector Machine (SVM), RandomForest, and Multilayer Perceptron (MLP) algorithms were employed to develop risk models and were evaluated using 10-fold cross-validation. ResultsOf the patients, 61 (38.13 %) achieved ypN0 status post-NAT. Univariate and multivariable logistic regression analyses revealed molecular subtypes and Ki67 as pivotal predictors of achieving ypN0 post-NAT. The SVM-based “Data Amalgamation” model that integrates radiomic, deep learning features, and clinical data, exhibited an outstanding AUC of 0.986 (95 % CI: 0.954–1.000), surpassing other models. ConclusionOur study illuminates the challenges and opportunities inherent in breast cancer management post-NAT. By introducing a sophisticated, SVM-based “Data Amalgamation” model, we propose a way towards accurate, dynamic ALN assessments, offering potential for personalized therapeutic strategies in BC.

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

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. 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. 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. 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.

The Value of Radiomic Features of Primary Breast Tumor in STIR Sequences in Predicting Axilla Metastasis

Background: Detection of axillary metastases in breast cancer is critical for treatment options and prognosis. The aim of this study is to investigate the value of radiomic features obtained from short tau inversion recovery (STIR) sequences in magnetic resonance imaging (MRI) of primary tumor in breast cancer in predicting axillary lymph node metastasis (ALNM). Methods: Lesions of 165 patients with a mean age of 51.12 ±11 (range 28-82) with newly diagnosed invasive breast cancer who underwent breast MRI before treatment were manually segmented from STIR sequences in the 3D Slicer program in all sections. Machine learning (ML) analysis was performed using the extracted 851 features Python 3.11, Pycaret library program. Datasets were randomly divided into train (123, 80%) and independent test (63, 20%) datasets. The performances of ML algorithms were compared with area under curve (AUC), accuracy, recall, presicion and F1 scores. Results: Accuracy and AUC in the training set were in the range of 57 %-86 % and 0.50-0.95, respectively. The best model in the training set was the catBoost classifier with an AUC of 0.95 and 84% accuracy. The AUC, accuracy, recall, precision values and F1 score of the CatBoost classifier on the test set were 0.92, 84 %, 89 %, 85 %, 86 %, respectively. Conclusion: Radiomic features obtained from primary tumors on STIR sequences have the potential to predict ALNM in invasive breast cancer.

Artificial intelligence assisted ultrasound for the non-invasive prediction of axillary lymph node metastasis in breast cancer

PurposeA practical noninvasive method is needed to identify lymph node (LN) status in breast cancer patients diagnosed with a suspicious axillary lymph node (ALN) at ultrasound but a negative clinical physical examination. To predict ALN metastasis effectively and noninvasively, we developed an artificial intelligence-assisted ultrasound system and validated it in a retrospective study.MethodsA total of 266 patients treated with sentinel LN biopsy and ALN dissection at Peking Union Medical College & Hospital(PUMCH) between the year 2017 and 2019 were assigned to training, validation and test sets (8:1:1). A deep learning model architecture named DeepLabV3 + was used together with ResNet-101 as the backbone network to create an ultrasound image segmentation diagnosis model. Subsequently, the segmented images are classified by a Convolutional Neural Network to predict ALN metastasis.ResultsThe area under the receiver operating characteristic curve of the model for identifying metastasis was 0.799 (95% CI: 0.514–1.000), with good end-to-end classification accuracy of 0.889 (95% CI: 0.741–1.000). Moreover, the specificity and positive predictive value of this model was 100%, providing high accuracy for clinical diagnosis.ConclusionThis model can be a direct and reliable tool for the evaluation of individual LN status. Our study focuses on predicting ALN metastasis by radiomic analysis, which can be used to guide further treatment planning in breast cancer.

Predictive value of MRI-based deep learning model for lymphovascular invasion status in node-negative invasive breast cancer

To retrospectively assess the effectiveness of deep learning (DL) model, based on breast magnetic resonance imaging (MRI), in predicting preoperative lymphovascular invasion (LVI) status in patients diagnosed with invasive breast cancer who have negative axillary lymph nodes (LNs). Data was gathered from 280 patients, including 148 with LVI-positive and 141 with LVI-negative lesions. These patients had undergone preoperative breast MRI and were histopathologically confirmed to have invasive breast cancer without axillary LN metastasis. The cohort was randomly split into training and validation groups in a 7:3 ratio. Radiomics features for each lesion were extracted from the first post-contrast dynamic contrast-enhanced (DCE)-MRI. The Least Absolute Shrinkage and Selection Operator (LASSO) regression method and logistic regression analyses were employed to identify significant radiomic features and clinicoradiological variables. These models were established using four machine learning (ML) algorithms and one DL algorithm. The predictive performance of the models (radiomics, clinicoradiological, and combination) was assessed through discrimination and compared using the DeLong test. Four clinicoradiological parameters and 10 radiomic features were selected by LASSO for model development. The Multilayer Perceptron (MLP) model, constructed using both radiomic and clinicoradiological features, demonstrated excellent performance in predicting LVI, achieving a high area under the curve (AUC) of 0.835 for validation. The DL model (MLP-radiomic) achieved the highest accuracy (AUC = 0.896), followed by DL model (MLP-combination) with an AUC of 0.835. Both DL models were significantly superior to the ML model (RF-clinical) with an AUC of 0.720. The DL model (MLP), which integrates radiomic features with clinicoradiological information, effectively aids in the preoperative determination of LVI status in patients with invasive breast cancer and negative axillary LNs. This is beneficial for making informed clinical decisions.

Analysis of the Clinical Value of Immunohistochemical Testing in the Pathological Diagnosis of Breast Cancer

Objective: To investigate the clinical value of immunohistochemistry (IHC) detection in the pathological diagnosis of breast cancer. Methods: Eighty breast cancer patients admitted to Baoding No. 1 Central Hospital from June 2022 to June 2023 were selected as study subjects. The samples were divided into a positive group (40 cases) and a negative group (40 cases) according to ER and PR test results. Immunohistochemistry was performed on all patients to compare the differences between the two groups in C-erbB-2 positive expression and axillary lymph node metastasis. Results: The positive expression rate of C-erbB-2 in the positive group (35.00%) was significantly lower than that in the negative group (80.00%), with a highly significant difference (P &lt; 0.001). The axillary lymph node metastasis rate in the positive group (40.00%) was significantly lower than that in the negative group (75.00%), with a significant difference (P &lt; 0.05). Conclusion: Immunohistochemical detection in breast cancer pathology enhances diagnostic accuracy, predicts prognosis, and supports personalized treatment by identifying ER, PR, and C-erbB-2. It is worth being widely adopted in clinical practice.

Can Highly Specific Ultrasound Criteria Obviate the Need for Sentinel Lymph Node Biopsy in Breast Cancer Patients with Axillary Lymph Node Metastasis?

Background: Sentinel lymph node biopsy (SLNB) is primarily performed to detect axillary lymph node involvement and determine the need for surgical axillary lymph node dissection (ALND). However, the procedure is expensive and necessitates the use of radio-labelled colloids that are not readily available to a large number of patients. It also carries the risk of producing occasional false negative results. Objectives: This study aimed to define highly specific ultrasound parameters to ascertain axillary lymph node involvement as an alternative or complementary method to SLNB in selected patients who require ALND. Patients and Methods: In this prospective cross-sectional study, 256 patients with confirmed breast cancer were selected through non-probability purposive sampling. The selected patients were referred to Omid Hospital, a tertiary educational oncology center in Mashhad, Iran, from 2018 to 2022. This study identified highly specific cut-off points for ultrasound parameters to determine lymph node involvement in comparison with the histological diagnosis post-SLNB or ALND. Measurements were taken of the tumor size, the cortical thickness of the axillary lymph node, the short axis diameter of the node, and the count of lymph nodes with a cortical thickness of ≥ 3 mm. Results: Among 256 patients with a mean age of 46.41 ± 10.77 years, 202 (87.9%) had histologically confirmed metastatic adenopathy. The ultrasound cut-off values identified to define metastasis with high certainty were as follows: A tumor size &gt; 50.5 mm (with 97% specificity), a difference in cortical thickness of ≥ 4.5 mm (with 100% specificity), a short axis diameter of the node &gt; 12 mm (with 95% specificity), more than three lymph nodes with a cortical thickness of ≥ 3 mm (with 94.3% specificity), and a cortical thickness of ≥ 6 mm (with 95% specificity). Conclusion: Highly specific ultrasound findings can diagnose lymph node metastasis with a high degree of certainty and can used as an alternative method to SLNB. A difference of ≥ 4.5 mm between the cortical thickness of the suspected lymph node and the contralateral lymph node and a cortical thickness of ≥ 6 mm are ultrasound findings that can detect lymph nodes involvement, with a specificity ranging from 95% to 100%.

Treatment of HER2-positive cutaneous apocrine carcinoma of the axilla.

Cutaneous apocrine carcinoma (CAC) is an extremely rare skin appendage malignant tumor that develops in the apocrine sweat glands, and no evidence-based drug therapy has been established. A 61-year-old female patient was diagnosed with axillary CAC with axillary lymph node metastasis and underwent surgery. Pathological examination revealed seven lymph node metastases. Immunostaining revealed that the tumor cells were estrogen receptor (ER)-positive, progesterone receptor (PgR)-positive, and human epidermal growth factor receptor 2 (HER2)-positive. The patient received chemotherapy, including anti-HER2 drugs, and hormone therapy to prevent recurrence. No recurrence was observed for > 3years after surgery. The apocrine glands in the skin and mammary glands have similar characteristics and mammary glands are thought to be modified or derived from the apocrine glands present in the subcutaneous adipose tissue. Therefore, ER, PgR, and HER2 levels may be positive in CAC. Drug treatments for breast cancer may also be effective for CAC.