Lymph Node Metastasis In Breast Cancer Research Articles

Study on the Relationship Between Differentially Expressed Proteins in Breast Cancer and Lymph Node Metastasis.

Lymph node metastasis is a cause of poor prognosis in breast cancer. Mass spectrometry-based proteomics aims to map the protein landscapes of biological samples and profile tumors more comprehensively. Here, proteomics was employed to identify differentially expressed proteins (DEPs) that were associated with lymph node metastasis. Tandem mass tag (TMT) quantitative proteomic approaches were applied for extensive profiling of conditioned medium of MDA-MB-231 and MCF7 cell lines and serums of patients who did or did not have lymph node metastasis, and DEPs were analyzed by bioinformatics. Furthermore, potential secreted or membrane proteins MUC5AC, ITGB4, CTGF, EphA2, S100A4, PRDX2, and PRDX6 were selected for verification in 114 tissue microarray samples of breast cancer using the immunohistochemical method. The relevant data was analyzed and processed by independent sample ttest, chi-square test, or Fisher's exact test using SPSS22.0 software. In the conditioned medium of MDA-MB-231 cell lines, 154 proteins were upregulated, while 136 were downregulated compared to those of MCF7. In the serum of patients with breast cancer and lymph node metastasis, 17 proteins were upregulated, and 5 proteins were downregulated compared to those without lymph node metastasis. Furthermore, according to tissue verification, CTGF, EphA2, S100A4, and PRDX2 were associated with breast cancer lymph node metastasis. Our study provides a new perspective for the understanding of the role of DEPs (especially CTGF, EphA2, S100A4, and PRDX2) in the development and metastasis of breast cancer. They could become potential diagnostic and prognostic biomarkers and therapeutic targets.

Cancer cell plasticity and MHC-II-mediated immune tolerance promote breast cancer metastasis to lymph nodes.

Tumor-draining lymph nodes (TDLNs) are important for tumor antigen-specific T cell generation and effective anticancer immune responses. However, TDLNs are often the primary site of metastasis, causing immune suppression and worse outcomes. Through cross-species single-cell RNA-Seq analysis, we identified features defining cancer cell heterogeneity, plasticity, and immune evasion during breast cancer progression and lymph node metastasis (LNM). A subset of cancer cells in the lymph nodes exhibited elevated MHC class II (MHC-II) gene expression in both mice and humans. MHC-II+ cancer cells lacked costimulatory molecule expression, leading to regulatory T cell (Treg) expansion and fewer CD4+ effector T cells in TDLNs. Genetic knockout of MHC-II reduced LNM and Treg expansion, while overexpression of the MHC-II transactivator, Ciita, worsened LNM and caused excessive Treg expansion. These findings demonstrate that cancer cell MHC-II expression promotes metastasis and immune evasion in TDLNs.

High Levels of SII and PIV are the Risk Factors of Axillary Lymph Node Metastases in Breast Cancer: A Retrospective Study.

To investigate the predictive value of systemic immune-inflammation-index (SII) and Pan-Immune-Inflammation-value (PIV) for axillary lymph node (ALN) metastasis in patients with breast cancer. We retrospectively collected data of 247 patients with invasive breast from the Affiliated Hospital of Jiangnan University. The state of axillary lymph node (ALN) metastasis was confirmed by pathological diagnosis. Clinicopathological data (age, ER, PR, HER2, Ki67 expression levels, diapause status, weight, histological grade, vascular invasion, and state of axillary lymph node) were compared between differences of SII and PIV groups and an association between clinical indexes and ALN metastasis was evaluated. The cut-off values of SII and PIV were 320.04 and 92.01, respectively. The significant difference between vascular invasion (P=0.023) and axillary lymph node metastases (P<0.001) in the high and low SII levels. Significant differences were observed in tumor size (p=0.024), PR expression level (P=0.033) and the status of axillary lymph node metastases (p<0.001) between the high PIV group and the low PIV group. Univariate analysis showed that vascular invasion, tumor size, Ki67 expression level, SII, and PIV were significantly correlated with axillary lymph node metastases (p<0.05). Then, multivariate analysis revealed that the vascular invasion (p<0.001), HER2 expression levels (p<0.047), SII (p<0.001) and PIV (p<0.030) were risk factors for axillary lymph node metastases. High levels of SII, PIV, LVI, and HER2 are the risk factors for axillary lymph node metastases in breast cancer patients.

Artificial Intelligence–Aided Diagnosis of Breast Cancer Lymph Node Metastasis on Histologic Slides in a Digital Workflow

Identifying lymph node (LN) metastasis in invasive breast carcinoma can be tedious and time-consuming. We investigated an artificial intelligence (AI) algorithm to detect LN metastasis by screening hematoxylin and eosin (H&E) slides in a clinical digital workflow. The study included 2 sentinel LN (SLN) cohorts (a validation cohort with 234 SLNs and a consensus cohort with 102 SLNs) and 1 nonsentinel LN cohort (258 LNs enriched with lobular carcinoma and postneoadjuvant therapy cases). All H&E slides were scanned into whole slide images in a clinical digital workflow, and whole slide images were automatically batch-analyzed using the Visiopharm Integrator System (VIS) metastasis AI algorithm. For the SLN validation cohort, the VIS metastasis AI algorithm detected all 46 metastases, including 19 macrometastases, 26 micrometastases, and 1 with isolated tumor cells with a sensitivity of 100%, specificity of 41.5%, positive predictive value of 29.5%, and negative predictive value (NPV) of 100%. The false positivity was caused by histiocytes (52.7%), crushed lymphocytes (18.2%), and others (29.1%), which were readily recognized during pathologists’ reviews. For the SLN consensus cohort, 3 pathologists examined all VIS AI annotated H&E slides and cytokeratin immunohistochemistry slides with similar average concordance rates (99% for both modalities). However, the average time consumed by pathologists using VIS AI annotated slides was significantly less than using immunohistochemistry slides (0.6 vs 1.0 minutes, P = .0377). For the nonsentinel LN cohort, the AI algorithm detected all 81 metastases, including 23 from lobular carcinoma and 31 from postneoadjuvant chemotherapy cases, with a sensitivity of 100%, specificity of 78.5%, positive predictive value of 68.1%, and NPV of 100%. The VIS AI algorithm showed perfect sensitivity and NPV in detecting LN metastasis and less time consumed, suggesting its potential utility as a screening modality in routine clinical digital pathology workflow to improve efficiency.

O091 Predict factors of hepatocyte growth factor family and bone morphogenetic proteins for breast cancer nodal metastasis

Abstract Introduction Lymph node metastasis is essential for the outcome of patients with breast cancer. Bone morphogenetic proteins (BMP) and hepatocyte growth factor (HGF), key to angiogenesis and metastasis of cancers, were investigated for their roles in the predication of nodal metastasis from breast cancer. Methods Expression of BMP family ligands, BMP receptors, HGF, its receptor MET and regulatory factors of HGF activation including HGF activator (HGFA), HGFA inhibitor 1 (HAI1), HAI2, Matriptase-1 and Matriptase-2 were determined in breast cancers (n=127) using real time quantitative PCR. Transcript levels of these genes were analysed in the breast cancers for their implication in lymphatic involvement using Mann-Whitney test. Predictive potential of those genes for nodal metastasis of these molecules was evaluated using both binary logistic regression and receiver operating characteristic (ROC) with SPSS (Version 27). Results With median as a cut-off value, logistic regression analyses showed that MET, Matriptase-1 and BMP15 were positively associated with nodal metastasis whereas Matriptase-2, BMP3 and HAI1 were inversely correlated with nodal involvement. After integrating these six prospective factors, ROC model returned with a significant value against nodal status (RUC=0.657, p=0.001). With the optimal ROC cut-off value to dichotomise patients, the integrated expression produced a significant prediction of nodal metastasis (p=0.006, HR=2.929). MET and HAI1 was correlated with the lymphangiogenesis marks of LYVE1, podo, prox1. Matriptase-1 was inversely correlated with podo, whilst Matriptase-2 and BMP15 showed a positive correlation with prox1. Conclusion The MET/Matriptase-1/BMP15 and the inversed Matriptase-2/BMP3/HAI1 are connected significantly with lymph node metastasis in breast cancer.

Identifying Lymph Node Metastasis-Related Factors in Breast Cancer Using Differential Modular and Mutational Structural Analysis.

Complex diseases are generally caused by disorders of biological networks and/or mutations in multiple genes. Comparisons of network topologies between different disease states can highlight key factors in their dynamic processes. Here, we propose a differential modular analysis approach that integrates protein-protein interactions with gene expression profiles for modular analysis, and introduces inter-modular edges and date hubs to identify the "core network module" that quantifies the significant phenotypic variation. Then, based on this core network module, key factors, including functional protein-protein interactions, pathways, and driver mutations, are predicted by the topological-functional connection score and structural modeling. We applied this approach to analyze the lymph node metastasis (LNM) process in breast cancer. The functional enrichment analysis showed that both inter-modular edges and date hubs play important roles in cancer metastasis and invasion, and in metastasis hallmarks. The structural mutation analysis suggested that the LNM of breast cancer may be the outcome of the dysfunction of rearranged during transfection (RET) proto-oncogene-related interactions and the non-canonical calcium signaling pathway via an allosteric mutation of RET. We believe that the proposed method can provide new insights into disease progression such as cancer metastasis.

The Role of AI in Breast Cancer Lymph Node Classification: A Comprehensive Review.

Breast cancer is a significant health issue affecting women worldwide, and accurately detecting lymph node metastasis is critical in determining treatment and prognosis. While traditional diagnostic methods have limitations and complications, artificial intelligence (AI) techniques such as machine learning (ML) and deep learning (DL) offer promising solutions for improving and supplementing diagnostic procedures. Current research has explored state-of-the-art DL models for breast cancer lymph node classification from radiological images, achieving high performances (AUC: 0.71-0.99). AI models trained on clinicopathological features also show promise in predicting metastasis status (AUC: 0.74-0.77), whereas multimodal (radiomics + clinicopathological features) models combine the best from both approaches and also achieve good results (AUC: 0.82-0.94). Once properly validated, such models could greatly improve cancer care, especially in areas with limited medical resources. This comprehensive review aims to compile knowledge about state-of-the-art AI models used for breast cancer lymph node metastasis detection, discusses proper validation techniques and potential pitfalls and limitations, and presents future directions and best practices to achieve high usability in real-world clinical settings.

Multiplex imaging of breast cancer lymph node metastases identifies prognostic single-cell populations independent of clinical classifiers

Although breast cancer mortality is largely caused by metastasis, clinical decisions are based on analysis of the primary tumor and on lymph node involvement but not on the phenotype of disseminated cells. Here, we use multiplex imaging mass cytometry to compare single-cell phenotypes of primary breast tumors and matched lymph node metastases in 205 patients. We observe extensive phenotypic variability between primary and metastatic sites and that disseminated cell phenotypes frequently deviate from the clinical disease subtype. We identify single-cell phenotypes and spatial organizations of disseminated tumor cells that are associated with patient survival and a weaker survival association for high-risk phenotypes in the primary tumor. We show that p53 and GATA3 in lymph node metastases provide prognostic information beyond clinical classifiers and can be measured with standard methods. Molecular characterization of disseminated tumor cells is an untapped source of clinically applicable prognostic information for breast cancer.

Abstract P5-01-06: Single cell profile of tumor and immune cells in primary triple-negative breast cancer and different sites in the axillary lymph nodes

Abstract Purpose: Little is known about the host-tumor interaction in the lymph node basin at a single cell level. This study examines single cell sequences in breast cancer nodal metastasis of a patient with triple negative breast cancer. Methods: The primary breast tumor, sentinel lymph node, an adjacent lymph node with metastatic involvement and a clinically normal-appearing lymph node were collected during operation. Single-cell sequencing was performed on all specimens. Results: 14,016 cells were clustered as 6 cell populations. Cancer cells demonstrated the molecular characteristics of TNBC basal B subtype and highly expressed genes in the MAPK signaling cascade. Tumor associated macrophages regulated antigen processing and presentation and other immune-related pathways to promote tumor invasion. CD8+ and CD4+ T lymphocytes concentrated more in sentinel lymph node and mainly stratified as two transcriptional states. The immune cell amount variation among primary tumor, sentinel and normal lymph nodes showed the similar tendency between the scRNA-seq profile of TNBC samples and a previous reported bulk RNA-seq profile of a breast cancer cohort including all four breast cancer subtype samples. Discussion: Single-cell sequencing analysis suggested that the sentinel lymph node was the initial meeting site of tumor infiltration and immune response, where partial T lymphocytes perform anti-tumor activity while other T cells exhibit an exhaustion state. We proposed a molecular explanation to the well-established clinical principle that the 5-year and 10-year survival outcomes were noninferior between SLND and ALND. Citation Format: Ning Liao, Cheukfai Li, Li Cao, Yanhua Chen, Chongyang Ren, Xiaoqing Chen, Hsiaopei Mok, Lingzhu Wen, Kai Li, Yulei Wang, Yuchen Zhang, Yingzi Li, Jiaoyi Lv, Fangrong Cao, Yuting Luo, Hongrui Li, Wendy Wu, Charles M. Balch, Armando E. Giuliano. Single cell profile of tumor and immune cells in primary triple-negative breast cancer and different sites in the axillary lymph nodes [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P5-01-06.

LIMCH1 protein expression associated with lymph node metastasis in breast cancer

Background. Lymph node metastasis in breast cancer is not only a variant of cancer progression, but is also associated with the development of hematogenous metastases. The LIMCH1 protein is involved in the mesenchymal type of migration due to the activation of non-muscle myosin IIA.The objective of the study was to investigate the association of different LIMCH1 protein expression variants with lymph node metastasis.Material and Methods. The retrospective study included 53 patients with invasive breast carcinoma of no special type. The study group included all molecular genetic types (luminal A, luminal B, HER2-positive and triple negative). The median age of the patients was 55.4 ± 14 years. According to the TNM classification, patients had stage T1–3N0–3M0 breast cancer. The patients did not receive preoperative chemotherapy. The expression of the LIMCH1 protein in tumor cells was assessed using immunohistochemistry. Anti-LIMCH1Prestige (HPA004184, Sigma Aldrich, Germany) and anti-LIMCH1-C-term (SAB2700402, Sigma Aldrich, Germany) antibodies were used.Results. Lymph node metastases were more frequent in patients with LIMCH1 protein expression in tumor cells. This is true for cytoplasmic expression of the LIMCH1 protein detected by the CH domain, but not for submembrane expression or expression of the LIMCH1 protein detected by the LIM domain, regardless of localization in the cell. This phenomenon was not associated with the morphological heterogeneity of breast cancer. The most unfavorable factor for worse metastasis-free survival is the combination of the presence of lymph node metastases and cytoplasmic expression of the LIMCH1 protein detected by the CH domain.Conclusion. The development of synchronous lymph node metastases in breast cancer is associated with the presence of cytoplasmic expression of the LIMCH1 protein detected by the CH domain in the primary tumor tissue.

Construction of a predictive model for breast cancer metastasis based on lncRNAs.

There is currently a lack of biological markers to determine the risk of lymph node metastasis in breast cancer. A single long non-coding RNA (lncRNA) cannot accurately describe the heterogeneity of tumors. Thus, more accurate algorithms are needed to screen key pathogenic lncRNAs, and quantitative models are needed to describe the heterogeneity of breast cancer. A whole transcriptome sequencing data set of breast cancer tissue samples was downloaded from The Cancer Genome Atlas database (n=1,091). A weighted correlation network analysis was conducted to identify the hub lncRNAs associated with lymph node metastasis. A logistic regression analysis was conducted to construct the risk score model. The relationship between the risk scores and the key lncRNAs and the infiltration of the immune cell subtypes was also explored. A total of 3 common lncRNAs were identified between the differentially expressed lncRNA set and the hub lncRNA set; that is, zinc finger protein 582-antisense RNA 1 (ZNF582-AS1), metastasis-associated lung adenocarcinoma transcript 1 (MALAT1), and actin filament associated protein 1-antisense RNA 1 (AFAP1-AS1). The following formula was used to calculate the risk score: risk score =1.31 + 0.51 * ZNF582-AS1 - 0.66 * MALAT1 - 0.50 * AFAP1-AS1. The receiver operating characteristic curve showed that the areas under the curve for the risk score, ZNF582-AS1, MALAT1, and AFAP1-AS1 were 0.975, 0.793, 0.685, and 0764, respectively (P<0.05). The risk score was positively correlated with immune cell subtype infiltration. ZNF582-AS1, MALAT1, and AFAP1-AS1 are the key lncRNAs involved in the lymph node metastasis of breast cancer. Our risk score model, which was based on ZNF582-AS1, MALAT1 and AFAP1-AS1, can accurately predict the risk of breast cancer lymph node metastasis. ZNF582-AS1, MALAT1, and AFAP1-AS1 are potential biomarkers for the lymph node metastasis of breast cancer.

ScRNA-seq reveals the correlation between M2 phenotype of tumor-associated macrophages and lymph node metastasis of breast cancer.

The process of lymphatic metastasis was proved to be associated with podoplanin-expressing macrophages in breast cancer (BC). This study aimed to investigate the role of the M2 phenotype of tumor-associated macrophages and mine the key M2 macrophages-related genes for lymph node metastasis in BC. We downloaded the GSE158399 dataset from the Gene Expression Omnibus (GEO) database, which includes transcriptomic profiles of individual cells from primary tumors, negative lymph nodes (NLNs), and positive lymph nodes (PLNs) of breast cancer patients. The cell subsets were identified by clustering analysis after quality control of the scRNA-seq using Seurat. The activation and migration capability of M2 macrophages were evaluated with R package "GSVA". The key M2 macrophages-related genes were screened from the differential expressed genes (DEGs) and M2 macrophages activation and migration gene sets collected from MSigDB database. Our analysis identified three main cell types in primary tumors, NLNs, and PLNs: basal cells, luminal cells, and immune cell subsets. The further cell type classification of immune cell subsets indicated M2 macrophages accumulation in NLs and PLs. The GSVA enrichment scores for activation and migration capability were increased significantly in M2 macrophages from primary tumors than NLNs and PLNs (p-value < 0.001). Seven M2 macrophages activation-related and 15 M2 macrophages migration-related genes were significantly up-regulated in primary tumors than NLNs and PLNs. The proportion and GSVA enrichment scores for activation and migration of M2 macrophages may be potential markers for lymph node metastasis in breast cancer. Our study demonstrated that twenty-two up-regulated mRNA may be possible therapeutic targets for lymph node metastasis in breast cancer.

Further predictive value of lymphovascular invasion explored via supervised deep learning for lymph node metastases in breast cancer

Lymphovascular invasion, specifically lymph-blood vessel invasion (LBVI), is a risk factor for metastases in breast invasive ductal carcinoma (IDC) and is routinely screened using hematoxylin-eosin histopathological images. However, routine reports only describe whether LBVI is present and does not provide other potential prognostic information of LBVI. This study aims to evaluate the clinical significance of LBVI in 685 IDC cases and explore the added predictive value of LBVI on lymph node metastases (LNM) via supervised deep learning (DL), an expert-experience embedded knowledge transfer learning (EEKT) model in 40 LBVI-positive cases signed by the routine report. Multivariate logistic regression and propensity score matching analysis demonstrated that LBVI (OR 4.203, 95% CI 2.809-6.290, P<0.001) was a significant risk factor for LNM. Then, the EEKT model trained on 5780 image patches automatically segmented LBVI with a patch-wise Dice similarity coefficient of 0.930 in the test set and output counts, location, and morphometric features of the LBVIs. Some morphometric features were beneficial for further stratification within the 40 LBVI-positive cases. The results showed that LBVI in cases with LNM had a higher short-to-long side ratio of the minimum rectangle (MR) (0.686 vs. 0.480, P=0.001), LBVI-to-MR area ratio (0.774 vs. 0.702, P=0.002), and solidity (0.983 vs. 0.934, P=0.029) compared to LBVI in cases without LNM. The results highlight the potential of DL to assist pathologists in quantifying LBVI and, more importantly, in exploring added prognostic information from LBVI.

Expression analysis of cytokines IL-5, IL-6, IL-8, IL-17 and VEGF in breast cancer patients.

To investigate the relationship between changes in peripheral blood vascular endothelial growth factor (VEGF), interleukin-5 (IL-5), interleukin-6 (IL-6), interleukin-8 (IL-8) and interleukin-17 (IL-17) concentrations in breast cancer patients and their significance and clinical value in breast cancer staging and invasive metastasis. From September 2021 to April 2022, 60 breast cancer patients from Chongqing Medical University Hospital No. 2022 were enrolled in the breast breast cancer surgery group, while 30 patients with benign breast disease were enrolled in the control group during the same period. Venous blood samples were collected at admission and 1 week after surgery to determine the expression of these factors in serum. Statistical methods such as Wilcoxon test and Spearman correlation analysis were used to analyze the relationship between the above factors and the clinicopathological characteristics of the patients. By analyzing data from patients with benign and malignant breast tumors, an association was found with serum levels of IL-6, IL-17 and VEGF. Their respective areas under the operating characteristic curve were 0.649, 0.734 and 0.656 (P < 0.05). There were significant differences in the cytokine expression levels of IL-17 and VEGF in different molecular typing (P values were 0.008 and 0.040, respectively). The expression levels of IL-17 and VEGF were higher in HER-2 receptor-positive and triple-negative patients than in hormone receptor-positive patients (P < 0.05). Also, no significant correlation was found between the various cytokines mentioned in the article and breast cancer lymph node metastasis and Tumor Node Metastasis stage (TNM stage). In addition, in the breast cancer surgery group, postoperative VEGF levels were lower (P < 0.05) and IL-6 levels were higher (P < 0.05) compared to preoperative levels. Serum IL-6, IL-17, and VEGF are strongly associated with breast cancer development and can be used as a reference indicators for breast cancer diagnosis. In addition, post-operative VEGF levels decreases and IL-6 levels increases compared to pre-operative levels, which can also be used as an a postoperative follow-up indicator. In contrast, IL-5 and IL-8 have not found to be significantly associated with breast cancer patients in this study, which requires further study.

Label Cleaning Multiple Instance Learning: Refining Coarse Annotations on Single Whole-Slide Images.

Annotating cancerous regions in whole-slide images (WSIs) of pathology samples plays a critical role in clinical diagnosis, biomedical research, and machine learning algorithms development. However, generating exhaustive and accurate annotations is labor-intensive, challenging, and costly. Drawing only coarse and approximate annotations is a much easier task, less costly, and it alleviates pathologists' workload. In this paper, we study the problem of refining these approximate annotations in digital pathology to obtain more accurate ones. Some previous works have explored obtaining machine learning models from these inaccurate annotations, but few of them tackle the refinement problem where the mislabeled regions should be explicitly identified and corrected, and all of them require a - often very large - number of training samples. We present a method, named Label Cleaning Multiple Instance Learning (LC-MIL), to refine coarse annotations on a single WSI without the need for external training data. Patches cropped from a WSI with inaccurate labels are processed jointly within a multiple instance learning framework, mitigating their impact on the predictive model and refining the segmentation. Our experiments on a heterogeneous WSI set with breast cancer lymph node metastasis, liver cancer, and colorectal cancer samples show that LC-MIL significantly refines the coarse annotations, outperforming state-of-the-art alternatives, even while learning from a single slide. Moreover, we demonstrate how real annotations drawn by pathologists can be efficiently refined and improved by the proposed approach. All these results demonstrate that LC-MIL is a promising, lightweight tool to provide fine-grained annotations from coarsely annotated pathology sets.

Microenvironment components and spatially resolved single-cell transcriptome atlas of breast cancer metastatic axillary lymph nodes.

As an indicator of clinical prognosis, lymph node metastasis of breast cancer has drawn great attention. Many reports have revealed the characteristics of metastatic breast cancer cells, however, the effect of breast cancer cells on the microenvironment components of lymph nodes and spatial transcriptome atlas remains unclear. In this study, by integrating single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics, we investigate the transcriptional profiling of six surgically excised lymph node samples and the spatial organization of one positive lymph node. We identify the existence of osteoclast-like giant cells (OGC) which have high expressions of CD68 and CD163, the biomarkers of tumor-associated macrophages (TAMs). Through a spatially resolved transcriptomic method, we find that OGCs are scattered among metastatic breast cancer cells. In the lymph node microenvironment with breast cancer cell infiltration, TAMs are enriched in protumoral pathways including NF-κB signaling pathways and NOD-like receptor signaling pathways. Further subclustering demonstrates the potential differentiation trajectory in which macrophages develop from a state of active chemokine production to a state of active lymphocyte activation. This study is the first to integrate scRNA-seq and spatial transcriptomics in the tumor microenvironment of axillary lymph nodes, offering a systematic approach to delve into breast cancer lymph node metastasis.

Different Imaging Modalities for the Diagnosis of Axillary Lymph Node Metastases in Breast Cancer: A Systematic Review and Network Meta-Analysis of Diagnostic Test Accuracy.

Accurate diagnosis of axillary lymph node metastasis (ALNM) of breast cancer patients is important to guide local and systemic treatment. To evaluate the diagnostic performance of different imaging modalities for ALNM in patients with breast cancer. Systematic review and network meta-analysis (NMA). Sixty-one original articles with 8011 participants. 1.5 T and 3.0 T. We used the QUADAS-2 and QUADAS-C tools to assess the risk of bias in eligible studies. The identified articles assessed ultrasonography (US), MRI, mammography, ultrasound elastography (UE), PET, CT, PET/CT, scintimammography, and PET/MRI. We used random-effects conventional meta-analyses and Bayesian network meta-analyses for data analyses. We used sensitivity and specificity, relative sensitivity and specificity, superiority index, and summary receiver operating characteristic curve (SROC) analysis to compare the diagnostic value of different imaging modalities. Sixty-one studies evaluated nine imaging modalities. At patient level, sensitivities of the nine imaging modalities ranged from 0.27 to 0.84 and specificities ranged from 0.84 to 0.95. Patient-based NMA showed that UE had the highest superiority index (5.95) with the highest relative sensitivity of 1.13 (95% confidence interval [CI]: 0.93-1.29) among all imaging methods when compared to US. At lymph node level, MRI had the highest superiority index (6.91) with highest relative sensitivity of 1.13 (95% CI: 1.01-1.23) and highest relative specificity of 1.11 (95% CI: 0.95-1.23) among all imaging methods when compared to US. SROCs also showed that UE and MRI had the largest area under the curve (AUC) at patient level and lymph node level of 0.92 and 0.94, respectively. UE and MRI may be superior to other imaging modalities in the diagnosis of ALNM in breast cancer patients at the patient level and the lymph node level, respectively. Further studies are needed to provide high-quality evidence to validate our findings. 3 TECHNICAL EFFICACY: Stage 2.

Editorial for "Different Imaging Modalities for the Diagnosis of Axillary Lymph Node Metastases in Breast Cancer: A Systematic Review and Network Meta-Analysis".

Editorial for "Different Imaging Modalities for the Diagnosis of Axillary Lymph Node Metastases in Breast Cancer: A Systematic Review and Network Meta-Analysis".

Identification of misdiagnosis by deep neural networks on a histopathologic review of breast cancer lymph node metastases

The frozen section (FS) diagnoses of pathology experts are used in China to determine whether sentinel lymph nodes of breast cancer have metastasis during operation. Direct implementation of a deep neural network (DNN) in clinical practice may be hindered by misdiagnosis of the algorithm, which affects a patient's treatment decision. In this study, we first obtained the prediction result of the commonly used patch-DNN, then we present a relative risk classification and regression tree (RRCART) to identify the misdiagnosed whole-slide images (WSIs) and recommend them to be reviewed by pathologists. Applying this framework to 2362 WSIs of breast cancer lymph node metastasis, test on frozen section results in the mean area under the curve (AUC) reached 0.9851. However, the mean misdiagnosis rate (0.0248), was significantly higher than the pathologists’ misdiagnosis rate (p < 0.01). The RRCART distinguished more than 80% of the WSIs as a high-accuracy group with an average accuracy reached to 0.995, but the difference with the pathologists’ performance was not significant (p > 0.01). However, the other low-accuracy group included most of the misdiagnoses of DNN models. Our research shows that the misdiagnosis from deep learning model can be further enriched by our method, and that the low-accuracy WSIs must be selected for pathologists to review and the high-accuracy ones may be ready for pathologists to give diagnostic reports.

Identification of a Prognostic Transcriptome Signature for Hepatocellular Carcinoma with Lymph Node Metastasis.

Hepatocellular carcinoma (HCC) is one of the most aggressive malignant tumors, and the prognosis of HCC patients with lymph node metastasis (LNM) is poor. However, robust biomarkers for predicting the prognosis of HCC LNM are still lacking. This study used weighted gene coexpression network analysis of GSE28248 (N = 80) microarray data to identify gene modules associated with HCC LNM and validated in GSE40367 dataset (N = 18). The prognosis-related genes in the HCC LNM module were further screened based on the prognostic curves of 371 HCC samples from TCGA. We finally developed a prognostic signature, PSG-30, as a prognostic-related biomarker in HCC LNM. The HCC subtypes identified by PSG-30-based consensus clustering analysis showed significant differences in prognosis, clinicopathological stage, m6A modification, ferroptosis activation, and immune characteristics. In addition, RAD54B was selected by regression model as an independent risk factor affecting the prognosis of HCC patients with LNM, and its expression was significantly positively correlated with tumor mutational burden and microsatellite instability in high-risk subtypes. Patients with high RAD54B expression had a better prognosis in the immune checkpoint inhibitor-treated cohorts but had a poor prognosis in the HCC sorafenib-treated group. The association of high RAD54B expression with LNM in breast cancer (BRCA) and cholangiocarcinoma and its prognostic effect in BRCA LNM cases suggest the value of RAD54B at the pancancer level. In conclusion, PSG-30 can effectively identify HCC LNM population with poor prognosis, and high-risk patients with high RAD54B expression may be more suitable for immunotherapy.