Impact of Malignant Ulcer Size on Lymph Node Stages in Gastric Cancer with Ulcerative Growth

2011 ◽  
Vol 59 (114) ◽  
Author(s):  
Chaoyang Xu ◽  
Jiangguo Shen ◽  
Shuduo Xie ◽  
Zhinong Jiang ◽  
Wenjun Chen ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Lymph Node ◽  
Ulcer Size ◽  
Malignant Ulcer

BIOPSY OF THE SIGNALING LYMPH NODES IN GASTRIC CANCER - CURRENT STATUS OF THE PROBLEM AND PROSPECTS

2018 ◽  
Vol 64 (3) ◽  
pp. 335-344
Author(s):  
Aleksey Karachun ◽  
Yuriy Pelipas ◽  
Oleg Tkachenko ◽  
D. Asadchaya
Keyword(s):  
Gastric Cancer ◽  
Lymph Node ◽  
Sentinel Lymph Node ◽  
Lymph Nodes ◽  
Stomach Cancer ◽  
Current Status ◽  
Regional Lymph Nodes ◽  
Systematic Analysis ◽  
Tumor Spread ◽  
Special Value

The concept of biopsy of sentinel lymph node as the first lymph node in the pathway of lymphogenous tumor spread has been actively discussed over the past decades and has already taken its rightful place in breast and melanoma surgery. The goal of this method is to exclude vain lymphadenectomy in patients without solid tumor metastases in regional lymph nodes. In the era of minimally invasive and organ-saving operations interventions it seems obvious an idea to introduce a biopsy of sentinel lymph node in surgery of early gastric cancer. Meanwhile the complexity of lymphatic system of the stomach and the presence of so-called skip metastases are factors limiting the introduction of a biopsy of sentinel lymph node in stomach cancer. This article presents a systematic analysis of biopsy technology of signaling lymph node as well as its safety and oncological adequacy. Based on literature data it seems to us that the special value of biopsy of sentinel lymph nodes in the future will be in the selection of personalized surgical tactics for stomach cancer.

Lymph node regression after neoadjuvant chemotherapy: A predictor of survival in gastric cancer

2019 ◽  
Author(s):  
Marina Alessandra Pereira ◽  
Marcus Fernando Kodama Pertille Ramos ◽  
Andre Roncon Dias ◽  
Leonardo Cardili ◽  
Renan Ribeiro e Ribeiro ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Lymph Node ◽  
Neoadjuvant Chemotherapy

scholarly journals Positive lymph node ratio is an index in predicting prognosis for remnant gastric cancer with insufficient retrieved lymph node in R0 resection

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Honghu Wang ◽  
Hao Qi ◽  
Xiaofang Liu ◽  
Ziming Gao ◽  
Iko Hidasa ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Lymph Node ◽  
Lymph Nodes ◽  
Lymph Node Ratio ◽  
Prognostic Index ◽  
Staging System ◽  
R0 Resection ◽  
Remnant Gastric Cancer ◽  
Curative Intent ◽  
Node Ratio

AbstractThe staging system of remnant gastric cancer (RGC) has not yet been established, with the current staging being based on the guidelines for primary gastric cancer. Often, surgeries for RGC fail to achieve the > 15 lymph nodes needed for TNM staging. Compared with the pN staging system, lymph node ratio (NR) may be more accurate for RGC staging and prognosis prediction. We retrospectively analyzed the data of 208 patients who underwent R0 gastrectomy with curative intent and who have ≤ 15 retrieved lymph nodes (RLNs) for RGC between 2000 and 2014. The patients were divided into four groups on the basis of the NR cutoffs: rN0: 0; rN1: > 0 and ≤ 1/6; rN2: > 1/6 and ≤ 1/2; and rN3: > 1/2. The 5-year overall survival (OS) rates for rN0, rN1, rN2, and rN3 were 84.3%, 64.7%, 31.5%, and 12.7%, respectively. Multivariable analyses revealed that tumor size (p = 0.005), lymphovascular invasion (p = 0.023), and NR (p < 0.001), but not pN stage (p = 0.682), were independent factors for OS. When the RLN count is ≤ 15, the NR is superior to pN as an important and independent prognostic index of RGC, thus predicting the prognosis of RGC patients more accurately.

scholarly journals Predicting gastric cancer outcome from resected lymph node histopathology images using deep learning

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Xiaodong Wang ◽  
Ying Chen ◽  
Yunshu Gao ◽  
Huiqing Zhang ◽  
Zehui Guan ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Deep Learning ◽  
Lymph Node ◽  
Lymph Nodes ◽  
Main Method ◽  
Learning Framework ◽  
Prognostic Assessment ◽  
N Stage ◽  
Cancer Outcome ◽  
Whole Slide Images

AbstractN-staging is a determining factor for prognostic assessment and decision-making for stage-based cancer therapeutic strategies. Visual inspection of whole-slides of intact lymph nodes is currently the main method used by pathologists to calculate the number of metastatic lymph nodes (MLNs). Moreover, even at the same N stage, the outcome of patients varies dramatically. Here, we propose a deep-learning framework for analyzing lymph node whole-slide images (WSIs) to identify lymph nodes and tumor regions, and then to uncover tumor-area-to-MLN-area ratio (T/MLN). After training, our model’s tumor detection performance was comparable to that of experienced pathologists and achieved similar performance on two independent gastric cancer validation cohorts. Further, we demonstrate that T/MLN is an interpretable independent prognostic factor. These findings indicate that deep-learning models could assist not only pathologists in detecting lymph nodes with metastases but also oncologists in exploring new prognostic factors, especially those that are difficult to calculate manually.

scholarly journals Lymph node metastasis-derived gastric cancer cells educate bone marrow-derived mesenchymal stem cells via YAP signaling activation by exosomal Wnt5a

Oncogene ◽  
2021 ◽  
Vol 40 (12) ◽  
pp. 2296-2308
Author(s):  
Mei Wang ◽  
Xinxin Zhao ◽  
Rong Qiu ◽  
Zheng Gong ◽  
Feng Huang ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Stem Cells ◽  
Bone Marrow ◽  
Mesenchymal Stem Cells ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Metastatic Gastric Cancer ◽  
Gastric Cancer Cells ◽  
Node Metastasis ◽  
Signaling Activation

AbstractLymph node metastasis (LNM), a common metastatic gastric-cancer (GC) route, is closely related to poor prognosis in GC patients. Bone marrow-derived mesenchymal stem cells (BM-MSCs) preferentially engraft at metastatic lesions. Whether BM-MSCs are specifically reprogrammed by LNM-derived GC cells (LNM-GCs) and incorporated into metastatic LN microenvironment to prompt GC malignant progression remains unknown. Herein, we found that LNM-GCs specifically educated BM-MSCs via secretory exosomes. Exosomal Wnt5a was identified as key protein mediating LNM-GCs education of BM-MSCs, which was verified by analysis of serum exosomes collected from GC patients with LNM. Wnt5a-enriched exosomes induced YAP dephosphorylation in BM-MSCs, whereas Wnt5a-deficient exosomes exerted the opposite effect. Inhibition of YAP signaling by verteporfin blocked LNM-GC exosome- and serum exosome-mediated reprogramming in BM-MSCs. Analysis of MSC-like cells obtained from metastatic LN tissues of GC patients (GLN-MSCs) confirmed that BM-MSCs incorporated into metastatic LN microenvironment, and that YAP activation participated in maintaining their tumor-promoting phenotype and function. Collectively, our results show that LNM-GCs specifically educated BM-MSCs via exosomal Wnt5a-elicited activation of YAP signaling. This study provides new insights into the mechanisms of LNM in GC and BM-MSC reprogramming, and will provide potential therapeutic targets and detection indicators for GC patients with LNM.

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