Axillary Lymph Node Metastasis Research Articles

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

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

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 < 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 < 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.

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>.

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.

Establishment of a logistic regression model nomogram for clinicopathological characteristics and risk factors with axillary lymph node metastasis in T1 locally advanced breast cancer: a retrospective study.

Although the research reports on locally advanced breast cancer (LABC) are increasing year by year, there are few reports on T1 LABC axillary lymph node metastasis (ALNM). By establishing a prediction model for T1 LABC ALNM, this study provides a reference value for the probability of ALNM of related patients, which helps clinicians to develop a more effective and individualized treatment plan for LABC. Cases with pathologically confirmed T1 breast cancer (BC) between 2010 and 2015 in the Surveillance, Epidemiology, and End Results (SEER) database were identified. Logistic regression was used to analyze the correlation between LABC lymph node metastasis and every factor, and the odds ratio (OR) and 95% confidence interval (CI) were used to identify any influencing factors. A nomogram was drawn after incorporating meaningful factors identified in multivariate logistic regression into the model. The receiver operating characteristic (ROC) curve of the model was drawn, and the area under the curve (AUC) and its 95% CI were calculated. Hosmer-Lemeshow goodness-of-fit test and clinical decision curve analysis (DCA) were performed. The results were validated in the validation group. A total of 200,933 female T1 BC patients were included in this study. Univariate and multivariate logistic regression analysis of T1 BC showed that progesterone receptor (PR)-negative, race, age, lobular carcinoma, micropapillary ductal carcinoma, axillary tail tumor, poor differentiation, and larger tumor diameter increased the probability of ALNM in T1 LABC. A predictive nomogram was established using the above predictors, the AUC of the modeling group was 0.739 (95% CI: 0.732-0.747), and when the AUC cut-off value was 0.026, the specificity and sensitivity of the model were 65.78% and 69.99%, respectively. Validation of the model showed that the AUC of the validation group (n=60,280) was 0.741. When all the risk factors were met, the predicted probability of N2-N3 was 50.40%. In this study, it was found that PR-negative, Black race, age, lobular carcinoma, micropapillary ductal carcinoma, axillary tail tumor, poor differentiation, and tumor diameter increased the probability of large lymph node metastasis in T1 LABC small tumors.

Associations between body mass index and hormonal receptor, HER2/neu receptor, as well as other clinical characteristics of breast cancer

Background. Obesity is a growing problem in the world. Correlations have been found between increasing body mass index (BMI) and breast cancer (BC). BC has multiple predictive and prognostic factors like hormonal receptors, human epidermal growth factor 2 receptor, stage of the disease and grade. Obesity may be differentially related to the risk of different subtypes given the various potential mechanisms underlying its association with breast cancer. The study aims to analyze the association between BMI, BC histopathologic, ER, PR and HER2/neu receptors expression, tumor size axillary nodes involvement, metastasis and nuclear grade. Methods. A cross sectional study was conducted involving 256 cases with breast cancer. Oncology teaching hospital based BC cases had been interviewed and data collected regarding weight and height, estrogen and progesterone receptors status, human epidermal growth factor 2 receptor, stage of the disease, grade of tumor at time of diagnosis, and categorize the cases to pre and postmenopausal state and then subdivided according to body mass index (normal, overweight, obese) and compared between three groups. Results. Overweight/obese people showed a significant statistical effect with estrogen and progesterone receptors, positive p value = 0.026, 0.007 respectively, larger tumor size, p value =0.035, and more lymph node involvement, p-value=0.031 in post-menopausal women, while premenopausal cases did not show significant statistical effect regarding the above-mentioned variables. Body mass index difference did not show a significant statistical effect on human epidermal growth factor 2 receptor, metastasis at diagnosis, tumor grade in both pre and postmenopausal cases. Conclusion. Body mass index difference affects postmenopausal BC cases rather than premenopausal as overweight/obese cases presented with more estrogen and progesterone receptors positive, larger tumor size and more lymph node involvement.

High CD90 Expression is A Predictor of Axillary Nodal Metastasis in Breast Carcinoma

High CD90 Expression is A Predictor of Axillary Nodal Metastasis in Breast Carcinoma

Predictive efficacy of the preoperative neutrophil–lymphocyte ratio in lymph node metastasis of cN0 hormone receptor-positive breast cancer

Breast cancer, as the most common cancer, has surpassed lung cancer worldwide. The neutrophil-to-lymphocyte ratio (NLR) has been linked to the onset of cancer and its prognosis in recent studies. However, quite a few studies have shown that there is a link between NLR and lymph node metastases in cN0 hormone receptor-positive (HR(+)) breast cancer. The purpose of this study was to evaluate the correlation between NLR and lymph node metastases in cN0 HR(+) breast cancer patients. From January 2012 to January 2022, 220 patients with cN0 HR(+) invasive breast cancers were enrolled in this study. The relationship between NLR and pathological data was statistically examined. The receiver operating characteristic (ROC) curve was used to determine the optimal cutoff of NLR, a chi-squared test was used for the univariate analysis, and logistic analysis was used for the multivariate analysis. The NLR had an optimal cutoff of 2.4 when the Jorden index was at a maximum. Patients with axillary lymph node metastases had a higher NLR (P < 0.05). A Univariate analysis showed that there were significant differences in cN0 HR(+) breast cancer with axillary lymph node metastasis among different clinical stages, histological grades, Ki-67 levels, tumor sizes, and NLR levels (P < 0.05). Clinical stage, tumor size, and NLR were found to be independent risk factors for lymph node metastases in multifactorial analysis. In cN0 HR(+) breast cancer, NLR is an independent risk factor for lymph node metastases. An NLR ≥ 2.4 indicates an increased probability of lymph node metastases. An elevated preoperative NLR has a high predictive value for axillary lymph node metastases.

Mucinous Cystadenocarcinoma of the Breast with Axillary Lymph Node Metastasis: An Entity with an Unusual Clinical Course

Mucinous Cystadenocarcinoma of the Breast with Axillary Lymph Node Metastasis: An Entity with an Unusual Clinical Course

Biomarker conversion from primary breast cancer to synchronous axillary lymph node metastasis and neoadjuvant therapy response: a single-center analysis

PurposeThe biomarker characteristics of breast cancer plays an important role in predicting treatment sensitivity. The aim of the present study was to compare immunohistochemical profiles (ER, PR, HER2, and Ki67) between the primary tumor and synchronous axillary lymph node metastasis and investigate the subsequent effects on neoadjuvant therapy response.MethodsA total of 358 patients with pathologically confirmed synchronous axillary lymph node metastasis at first diagnosis and treated by neoadjuvant therapy at Peking University First Hospital from January 1, 2013 to December 31, 2022 were included in this retrospective study. Clinicopathologic data, especially receptor status in primary and metastatic foci, was collected for each case.ResultsChange of ER, PR, HER2, and Ki67 expression was observed in 5.9%, 8.7%, 12.6%, and 17.3% of patients, respectively. HR discordance was observed more frequently when the ER status (p = 0.023) or PR status (p = 0.010) of primary tumor was negative, while HER2 discordance seemed to be more frequent when the HER2 status of primary tumor was HER2-0 or HER2-low (p < 0.001). Patients with loss of HR-positivity (positive to negative) responded to neoadjuvant chemotherapy better compared to those with stable positive HR expression (50% vs. 11.1%, p = 0.0017). A significantly decrease in pCR rate was observed in patients with unstable HER2 status, but not in the HER2-0/HER2-low subgroup.ConclusionReceptor discordance between primary tumor and synchronous axillary LNM appears to already exist before any anti-tumor therapy. This instability has limited clinical impact on the choice of neoadjuvant therapy at current stage, but further investigation is warranted with the incremental application of endocrine drugs and ADCs in neoadjuvant therapy.

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

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

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

BackgroundTo 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 methodsThis 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. ResultsThe 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. ConclusionThe 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.

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

ObjectiveTo 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. MethodsFrom 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. ResultsLogistic 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 curve and DCA demonstrated its satisfactory performance in predicting ALNM. ConclusionThe nomogram combining ultrasound features and immune-inflammatory markers could serve as a valuable instrument for predicting ALNM in breast cancer patients. Data availability statementThe original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding authors.

Studying on correlation between Ki-67 index and histopathological and immunohistochemical characteristics in invasive breast carcinoma

Background: Breast cancer is the most common cancer and the leading cause of cancer death in women. Molecular subtyping of breast cancer is based on the expression of biomarkers such as ER, PR, HER2/neu and Ki-67. Among them, Ki-67 is a biomarker expressing the proliferation level of breast cancer cells, directly related to disease progression, treatment response and prognosis. Our study aims to evaluate the Ki-67 index and its relationship with histopathological and immunohistochemical characteristics in invasive breast carcinoma. Methods: A cross-sectional descriptive study in 101 paraffin-embedded specimens of patients with invasive breast carcinoma who underwent surgery at Hue University of Medicine and Pharmacy Hospital from October 2019 to June 2021. Results: Tumors with Ki-67 index ≥ 14% account for 65.3%. Breast cancer tumors with tumor size ≤ 2 cm occupy for 50.5%. Histological grade II accounts for 53.1%. The most common disease stage is stage II (40.6%). The rate of axillary lymph node metastasis is 48.5%; The ER(+) and PR(+) rates are 64.4% and 47.5%, respectively. Tumors with HER2(+) account for 32.7%. The Luminal B accounts for the highest proportion (39.4%). The Ki-67 index has a statistically significant relationship with disease stage, histological grade, axillary lymph node metastasis, ER, PR, HER2 and molecular subtypes. There is no relationship between Ki 67 and age, tumor size and histopathological type. Conclusion: Tumors with Ki-67 ≥ 14% account for a high proportion (65.3%). The Ki-67 index has a statistically significant relationship with histopathological and immunohistochemical features: stage of disease, histological grade, axillary lymph node metastasis, ER/PR hormone receptors, and other factors. HER2-positive endodermal growth and molecular subtypes. There is no significant relationship between Ki-67 and age, tumor size and histopathological type. Key words: breast carcinoma; Ki-67; histopathology; immunohistochemistry.

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

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

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

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

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

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