The role of radical surgery followed by adjuvant therapy for high-risk early-stage cervical carcinoma patients with pelvic lymph node metastasis

2000 ◽  
Vol 93 (1) ◽  
pp. 85-90 ◽  
Author(s):  
Hao Lin ◽  
Chan-Chao ChangChien ◽  
Eng-Yen Huang ◽  
Hock-Liew Eng ◽  
Chao-Chan Huang
Keyword(s):  
Lymph Node ◽  
High Risk ◽  
Lymph Node Metastasis ◽  
Adjuvant Therapy ◽  
Cervical Carcinoma ◽  
Radical Surgery ◽  
Early Stage ◽  
Node Metastasis ◽  
Pelvic Lymph Node Metastasis

Uterine corpus invasion as a risk factor in patients with early-stage cervical carcinoma receiving radical surgery.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e17012-e17012
Author(s):  
Yifan Li ◽  
Ning Li ◽  
Lingying Wu
Keyword(s):  
Risk Factors ◽  
Risk Factor ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Cervical Carcinoma ◽  
Radical Surgery ◽  
Early Stage ◽  
Univariate Analysis ◽  
Uterine Corpus ◽  
Node Metastasis

e17012 Background: To explore whether pathologically verified uterine corpus invasion (UCI) is a risk factor for patients with early-stage (IB1-IIA2) cervical carcinoma receiving radical surgery. Methods: A mathed-case comparison of early-stage cervical carcinoma patients with pathologically verified UCI to patients without UCI on a 1:1 ratio was conducted. High risk factors (lymph node metastasis, paremetrial invasion, vaginal margin invasion) and intermediate risk factors (lymphovascular space invasion (LVSI) and deep stromal invasion) were completely matched between UCI and non-UCI groups. Kaplan-Meier and Log-rank test were applied for univariate analysis, and COX proportional hazard regression models were used for multivariate analysis. Results: 1320 consecutive patients with cervical carcinoma received surgery in our centerfrom Jan. 1st2009 to Dec 31st2014. 79 (5.98%) cases with UCI were identified. Median follow-up time was 43 months. There were 22 cases with recurrence. In UCI group, the recurrence rate was 20.3% (16/79), and in non-UCI group the recurrence rate was 7.6% (6/79). On univariate analysis, SCC, neoadjuvant chemotherapy (NACT), lymph node metastasis, parametrial invasion, LVSI, deep stromal invasion, vaginal invasion and UCI were significantly associated with disease free survival (DFS). After multivariate analysis, UCI ( p= 0.02, RR3.832, 95% CI1.235-11.893)and lymph node metastasis ( p= 0.042, RR 2.890, 95% CI1.038-8.045) were still independent risk factors for deceased DFS. Conclusions: Pathologically verified uterine corpus invasion might be an independent risk factor for decreased DFS in patients with early-stage cervical carcinoma receiving radical surgery.

scholarly journals Comprehensive analysis of lymph nodes metastasis associated genes in cervical cancer and its significance in treatment and prognosis

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ping Yang ◽  
Youqin Ruan ◽  
Zhiling Yan ◽  
Yang Gao ◽  
Hongying Yang ◽  
...  
Keyword(s):  
Lymph Node ◽  
High Risk ◽  
Lymph Node Metastasis ◽  
Cervical Carcinoma ◽  
Risk Score ◽  
Risk Groups ◽  
Node Metastasis ◽  
Prognostic Gene ◽  
Prognostic Gene Signature ◽  
Score Model

Abstract Background Cervical carcinoma is one of the most common malignant tumors of the female reproductive system. Lymph nodes metastasis, the most common metastasis, which can be detected even in small-size tumor patients, results in worse prognosis. Therefore, it is of great significance to explore novel lymph nodes metastasis associated biomarkers, which can predict the prognosis and provide a good reference for clinical decision making in cervical carcinoma patients. However, systematic and comprehensive studies related to the key molecules in lymph node metastasis in cervical carcinoma patients are still absent. Methods Transcriptome and clinical data of 307 cervical carcinoma patients were obtained from The Cancer Genome Atlas (TCGA). Then, survival of patients with and without lymph node metastasis was analyzed by Kaplan-Meier (K-M) curves. Differential expressed genes (DEGs) were detected between tumor and control samples using limma package and defined as lymph node metastasis related genes. Univariate and multivariate Cox regression analyses were carried out to screen robust prognostic gene signature. The risk score model and nomogram for predicting survival were constructed based on prognostic gene signature. The performance of the risk score model was evaluated by operating characteristic (ROC) curves. Based on risk score, patients were divided into low- and high- risk groups. DEGs, functional enrichment analysis and tumor microenvironment (immune infiltration and expressions of immune checkpoints) were detected in low- and high-risk groups. Results A total of 103 lymph node metastasis-associated genes were identified. Univariate and multivariate Cox regression analyses identified TEKT2, LPIN2, FABP4 and CXCL2 as prognostic gene signature. The risk score model was constructed and validated in cervical carcinoma patients. 345 DEGs identified between high- and low-risk groups were significantly enriched into immune-related biological processes. Furthermore, we found that the immune infiltration and expressions of immune checkpoints were significantly different between low- and high-risk groups. Conclusion Our study revealed that lymph node metastasis played an important role in the prognosis of cervical carcinoma patients. Furthermore, we established a risk score model based on lymph node metastasis related genes, which could accurately predict the survival of cervical carcinoma patients. Besides, our findings in tumor microenvironments of low- and high-risk groups improved our understanding of the relationship between lymph node metastasis related genes and cervical carcinoma.

A Predictive Model Using Histopathologic Characteristics of Early-Stage Type 1 Endometrial Cancer to Identify Patients at High Risk for Lymph Node Metastasis

2015 ◽  
Vol 22 (13) ◽  
pp. 4224-4232 ◽  
Author(s):  
Sofiane Bendifallah ◽  
Geoffroy Canlorbe ◽  
Enora Laas ◽  
Florence Huguet ◽  
Charles Coutant ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
Lymph Node ◽  
High Risk ◽  
Lymph Node Metastasis ◽  
Predictive Model ◽  
Early Stage ◽  
Node Metastasis
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