scholarly journals Nomogram Based on Preoperative Clinical Characteristics to Predict the Risk of Lymph Node Metastasis in Hepatocellular Carcinoma

2020 ◽  
Author(s):  
Rongyu Wei ◽  
Shuqun Li ◽  
Liying Ren ◽  
Junxiong Yu ◽  
Weijia Liao
Keyword(s):  
Risk Factors ◽  
Hepatocellular Carcinoma ◽  
Logistic Regression ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Clinical Characteristics ◽  
Predictive Accuracy ◽  
Hepatitis B Surface Antigen ◽  
Area Under The Curve ◽  
Node Metastasis

Abstract Background: There are limitations in judging the occurrence of lymph node metastasis (LNM) in hepatocellular carcinoma (HCC) before surgery. The purpose of this study was to establish a preoperative nomogram for predicting the risk of LNM in HCC and to explore its clinical utility.Methods: A total of 195 HCC patients undergoing radical hepatectomy were retrospectively analyzed. According to the presence or absence of LNM, the patients were divided into two groups, and the clinical characteristics of the two groups were compared. Risk factors for LNM were assessed based on logistic regression, and a nomogram was established. The receiver operating characteristic (ROC) curve was used to calculate area under the curve (AUC) of the logistic regression model, and the predictive accuracy of the nomogram was evaluated by the concordance index (C-index). The clinical efficacy of the nomogram was detected by decision curve analysis (DCA).Results: Logistic analysis revealed hepatitis B surface antigen (HBsAg) (HR = 3.50, 95% CI, 1.30-9.42, P = 0.013), globulin (HR = 2.46, 95% CI, 1.05-5.75, P = 0.039), neutrophil to lymphocyte ratio (NLR) (HR = 7.64, 95% CI, 3.22-18.11, P < 0.001) and tumor size (HR = 3.86, 95% CI, 1.26-11.88 P = 0.018) were independent risk factors for lymph node metastasis in HCC. The nomogram was established based on the above 4 variables, and the AUC was 0.835 (95% CI, 0.780-0.890). The calibration curve showed that the model has good predictive ability, and DCA indicates good predictive effect.Conclusions: The nomogram established by analyzing the preoperative clinical characteristics is a simple tool that can predict the risk of lymph node metastasis in HCC patients and guide clinicians to make better clinical decisions.

scholarly journals Development and Validation a Nomogram for Predicting the Third Station Lymph Node Metastasis in Early Gastric Cancer

2021 ◽  
Author(s):  
Xiaoxiao Wang ◽  
Cong Li ◽  
Mengjie Fang ◽  
Liwen Zhang ◽  
Lianzhen Zhong ◽  
...  
Keyword(s):  
Risk Factors ◽  
Gastric Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Lymph Nodes ◽  
Validation Cohort ◽  
Predictive Accuracy ◽  
Clinical Risk Factors ◽  
Node Metastasis ◽  
Clinical Risk

Abstract Background:This study aimed to evaluate the value of radiomic nomogram in predicting lymph node metastasis in T1-2 gastric cancer according to the No. 3 station lymph nodes.Methods:A total of 159 T1-2 gastric cancer (GC) patients who had undergone surgery with lymphadenectomy between March 2012 and November 2017 were retrospectively collected and divided into a primary cohort (n = 80) and a validation cohort (n = 79). Radiomic features were extracted from both tumor region and No. 3 station lymph nodes (LN) based on computed tomography (CT) images per patient. Then, key features were selected using minimum redundancy maximum relevance algorithm and fed into two radiomic signatures, respectively. Meanwhile, the predictive performance of clinical risk factors was studied. Finally, a nomogram was built by merging radiomic signatures and clinical risk factors and evaluated by the area under the receiver operator characteristic curve (AUC) as well as decision curve.Results: Two radiomic signatures, reflecting phenotypes of the tumor and LN respectively, were significantly associated with LN metastasis. A nomogram incorporating two radiomic signatures and CT-reported LN metastasis status showed good discrimination of LN metastasis in both the primary cohort (AUC: 0.915; 95% confidence interval [CI]: 0.832-0.998) and validation cohort (AUC: 0.908; 95%CI: 0.814-1.000). The decision curve also indicated its potential clinical usefulness.Conclusions:The nomogram received favorable predictive accuracy in predicting No.3 station LN metastasis in T1-2 GC, and could assist the choice of therapy.

scholarly journals Preoperative Risk Assessment of Lymph Node Metastasis in cT1 Lung Cancer: A Retrospective Study from Eastern China

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Chengyan Zhang ◽  
Guanchao Pang ◽  
Chengxi Ma ◽  
Jingni Wu ◽  
Pingli Wang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Lung Cancer ◽  
Logistic Regression ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Predictive Model ◽  
Smoking History ◽  
Lymph Node Status ◽  
Node Metastasis ◽  
Node Status

Background. Lymph node status of clinical T1 (diameter≤3 cm) lung cancer largely affects the treatment strategies in the clinic. In order to assess lymph node status before operation, we aim to develop a noninvasive predictive model using preoperative clinical information. Methods. We retrospectively reviewed 924 patients (development group) and 380 patients (validation group) of clinical T1 lung cancer. Univariate analysis followed by polytomous logistic regression was performed to estimate different risk factors of lymph node metastasis between N1 and N2 diseases. A predictive model of N2 metastasis was established with dichotomous logistic regression, externally validated and compared with previous models. Results. Consolidation size and clinical N stage based on CT were two common independent risk factors for both N1 and N2 metastases, with different odds ratios. For N2 metastasis, we identified five independent predictors by dichotomous logistic regression: peripheral location, larger consolidation size, lymph node enlargement on CT, no smoking history, and higher levels of serum CEA. The model showed good calibration and discrimination ability in the development data, with the reasonable Hosmer-Lemeshow test (p=0.839) and the area under the ROC being 0.931 (95% CI: 0.906-0.955). When externally validated, the model showed a great negative predictive value of 97.6% and the AUC of our model was better than other models. Conclusion. In this study, we analyzed risk factors for both N1 and N2 metastases and built a predictive model to evaluate possibilities of N2 metastasis of clinical T1 lung cancers before the surgery. Our model will help to select patients with low probability of N2 metastasis and assist in clinical decision to further management.

Inverse Correlation between Age and Risk of Lymph Node Metastasis in Bladder Cancer

2020 ◽  
Author(s):  
Wenchao Ma ◽  
Tiantian Wang ◽  
Yadong Guo ◽  
Ruiliang Wang ◽  
Ji Liu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Bladder Cancer ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Marital Status ◽  
Inverse Correlation ◽  
Factors Affecting ◽  
Node Metastasis

Abstract Background Bladder cancer (BCa) is the most common malignant tumor in humans and brings about a huge burden on the international community and on the families of those it affects. Lymph node metastasis (LNM) is an important factor affecting the prognosis of BCa. This study aimed to investigate the risk factors affecting LNM.Patients and Methods This study involved 5517 patients who underwent BCa-related surgery between 2006 and 2015. The multivariate logistic regression analysis was used to evaluate the association between age and LNM. The overall survival (OS) and cancer-specific survival (CSS) were analyzed using the Kaplan–Meier method. The multivariable Cox regression model was used to evaluate independent risk factors affecting OS and CSS.Results We retrieved 5517 cases from SEER database, including 148 patients aged 40-49 years, 726 aged 50-59 years, 1541 aged 60-69 years, 1538 aged 70-79 years and 1564 aged 80+ years. The rates of LNM were 20.27%, 16.94%, 11.94%, 9.95% and 6.46% for patients aged 40-49, 50-59, 60-69, 70-79 and 80+ years. We found an inverse correlation between age at diagnosis and risk of LNM from the logistic regression analysis in three modules(Module 1: P-value for trend, crude, no adjustment < 0.001; Module 2: P-value for trend adjusted for sex, race, insurance status, and marital status < 0.001; Module 3: P-value for trend adjusted for sex, race, insurance, marital status, size, grade, and metastasis < 0.001). Compared with patients aged 40–49 years, patients aged 50–59 years (OR = 0.752; 95% CI, 0.470–1.204; P = 0.236), 60–69 years (OR = 0.517; 95% CI, 0.329–0.815; P = 0.004), 70–79 years (OR = 0.375; 95% CI, 0.237–0.595; P < 0.001), and 80+ years (OR = 0.248; 95% CI, 0.154–0.398; P < 0.001) had a lower risk of LNM.ConclusionsYounger age at diagnosis was associated with a higher risk of LNM in patients with BCa. Excepting this, grade and metastasis were also risk factors for LNM.

scholarly journals Risk factors for lymph node metastasis in gastric neuroendocrine tumor: a retrospective study

BMC Surgery ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xianghui Li ◽  
Lihua Shao ◽  
Xiaofeng Lu ◽  
Zhengyang Yang ◽  
Shichao Ai ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Lymph Node ◽  
Retrospective Study ◽  
Lymph Node Metastasis ◽  
Neuroendocrine Tumor ◽  
Tumor Location ◽  
Expression Level ◽  
Ki 67 ◽  
Node Metastasis

Abstract Background Lymph node metastasis (LNM) plays a vital role in the determination of clinical outcomes in patients with gastric neuroendocrine tumor (G-NET). Preoperative identification of LNM is helpful for intraoperative lymphadenectomy. This study aims to investigate risk factors for LNM in patients with G-NET. Methods We performed a retrospective study involving 37 patients in non-LNM group and 82 patients in LNM group. Data of demographics, preoperative lab results, clinical–pathological results, surgical management, and postoperative situation were compared between groups. Significant parameters were subsequently entered into logistic regression for further analysis. Results Patients in LNM group exhibited older age (p = 0.011), lower preoperative albumin (ALB) (p = 0.003), higher carcinoembryonic antigen (CEA) (p = 0.020), higher International normalized ratio (p = 0.034), longer thrombin time (p = 0.018), different tumor location (p = 0.005), higher chromogranin A positive rate (p = 0.045), and higher Ki-67 expression level (p = 0.002). Logistic regression revealed ALB (p = 0.043), CEA (p = 0.032), tumor location (p = 0.013) and Ki-67 (p = 0.041) were independent risk factors for LNM in G-NET patients. Conclusions ALB, CEA, tumor location, and Ki-67 expression level correlate with the risk of LNM in patients with G-NET.

scholarly journals Predictive Nomogram and Risk Factors for Lymph Node Metastasis in Bladder Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Zijian Tian ◽  
Lingfeng Meng ◽  
Xin Wang ◽  
Tongxiang Diao ◽  
Maolin Hu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Bladder Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Predictive Accuracy ◽  
Characteristic Curve ◽  
Tumor Grade ◽  
Lymph Node Status ◽  
Node Metastasis ◽  
Independent Risk Factors

Lymph node metastasis (LNM) is an important prognostic factor for bladder cancer (BCA) and determines the treatment strategy. This study aimed to determine related clinicopathological factors of LNM and analyze the prognosis of BCA. A total of 10,653 eligible patients with BCA were randomly divided into training or verification sets using the 2004–2015 data of the Surveillance, Epidemiology, and End Results database. To identify prognostic factors for the overall survival of BCA, we utilized the Cox proportional hazard model. Independent risk factors for LNM were evaluated via logistic regression analysis. T-stage, tumor grade, patient age and tumor size were identified as independent risk factors for LNM and were used to develop the LNM nomogram. The Kaplan-Meier method and competitive risk analyses were applied to establish the influence of lymph node status on BCA prognosis. The accuracy of LNM nomogram was evaluated in the training and verification sets. The areas under the receiver operating characteristic curve (AUC) showed an effective predictive accuracy of the nomogram in both the training (AUC: 0.690) and verification (AUC: 0.704) sets. In addition, the calibration curve indicated good consistency between the prediction of deviation correction and the ideal reference line. The decision curve analysis showed that the nomogram had a high clinical application value. In conclusion, our nomogram displayed high accuracy and reliability in predicting LNM. This could assist the selection of the optimal treatment for patients.

scholarly journals Development and validation of prognostic factors for lymph node metastasis in endometrial cancer: A SEER analysis

2020 ◽  
Author(s):  
Xingchen Li ◽  
Yuan Cheng ◽  
Yangyang Dong ◽  
Jingyi Zhou ◽  
Xiao Yang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Predictive Accuracy ◽  
Large Population ◽  
Multivariate Logistic Regression Analysis ◽  
Myometrial Invasion ◽  
Net Benefit ◽  
Clinical Value ◽  
Node Metastasis

Abstract Objective: The purpose of this study was to develop and validate a nomogram that can be used to predict lymph node metastasis (LNM) in patients with endometrial carcinoma (EC). Methods: Clinical data of EC patients diagnosed between 2004 and 2015 were retrieved from the Surveillance, Epidemiology, and End Results Program (SEER) registry. The nomogram was constructed using independent risk factors chosen using a multivariate logistic regression analysis. Accuracy was validated for both groups using discrimination analysis and calibration curves. The predictive accuracy and clinical value of the nomogram and Mayo criteria were compared using decision curve analysis (DCA). Results: The final study group consisted of 63,836 women that met specific inclusion criteria. The factors that were identified in the multivariate analysis to be notable predictors of LNM were age, tumor size, histological type, cervical stromal invasion, tumor grade, and myometrial invasion. These risk factors were included in the nomogram. Discriminations of the nomogram and Mayo criteria were 0.848 (95% CI: 0.843-0.853) and 0.806 (95%CI: 0.801-0.812), respectively. In the validation group, the AUC values were 0.847 (95%CI: 0.840-0.857) and 0.804 (95%CI: 0.796-0.813) for the nomogram and the Mayo criteria, respectively ( P <0.01). Calibration plots showed that training and validation cohorts were well-calibrated. DCA revelaed that by using the nomogram always had a positive net benefit compared to using the Mayo criteria. Conclusions: A nomogram was developed to predict LNM in EC patients based on a large population-based analysis. The nomogram showed good performance for predicting LNM in patients with EC.

A practical nomogram predicting the prognosis of hepatocellular carcinoma patients with lymph node metastasis: A population-based analysis

2020 ◽  
Author(s):  
Kai Zhang ◽  
Changcheng Tao ◽  
Jianxiong Wu ◽  
Weiqi Rong
Keyword(s):  
Risk Factors ◽  
Hepatocellular Carcinoma ◽  
Prognostic Factors ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Lymph Nodes ◽  
Cox Regression ◽  
Lymph Nodes Metastasis ◽  
Node Metastasis ◽  
Independent Risk Factors

Abstract Background: Lymph node (LN) metastasis is associated with poor survival outcomes in patients with hepatocellular carcinoma (HCC) patients and because of the reported low probability of lymph node metastasis, research into the Anchorprognoses of such patients is difficult to conduct. In this study, we aimed to develop a nomogram model to predict the prognosis of HCC patients with lymph node metastasis. Methods: HCC patients diagnosed with LN metastasis from 2010 to 2015 were enrolled from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate Cox regression and lasso regression were used to screen prognostic factors. Cox multiple-factor analysis was used to investigate the independent risk factors for survival. We developed a prognostic nomograms using these independent risk factors. The predictive performance of our nomogram model was evaluated according to the concordance index (C-index) and calibration curve. The net clinical benefit was assessed via decision curve analysis (DCA). Patients were divided into different risk groups according to the model. A survival curve was drawn using the Kaplan-Meier method and the difference was compared by the log-rank test. Results: There were 944 patients in the training cohort and 402 patients in the validation cohort. Grade, T stage, surgery to the liver, chemotherapy, radiation recode, AFP, fibrosis score, tumor size group, M stage were selected as independent prognostic factors, and we developed nomograms using these variables. The calibration curves for probability of survival showed good agreement between the prediction by our model and actual observation in both the training and validation groups. DCA indicated that the nomogram had positive net benefits. Conclusions: The nomogram can accurately predict the prognosis of HCC patients with lymph nodes metastasis and provide a reasonable basis for treatment. Keywords: Hepatocellular carcinoma, prognosis, lymph nodes metastasis, prediction model, nomogram

Clinical Characteristics of Hepatocellular Carcinoma With an Extensive Lymph Node Metastasis at Diagnosis

2002 ◽  
Vol 25 (3) ◽  
pp. 318-323 ◽  
Author(s):  
Tatsuya Abe ◽  
Junji Furuse ◽  
Masahiro Yoshino ◽  
Taira Kinoshita ◽  
Masaru Konishi ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Clinical Characteristics ◽  
Node Metastasis ◽  
Extensive Lymph Node

scholarly journals Risk factors for lymph node metastasis in gastric neuroendocrine tumor: a retrospective study

2020 ◽  
Author(s):  
Xianghui Li ◽  
Meng Wang ◽  
Xiaofeng Lu ◽  
Zhengyang Yang ◽  
Shichao AI ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Lymph Node ◽  
Retrospective Study ◽  
Lymph Node Metastasis ◽  
Neuroendocrine Tumor ◽  
Tumor Location ◽  
Expression Level ◽  
Ki 67 ◽  
Node Metastasis

Abstract Background: Lymph node metastasis (LNM) plays a vital role in the determination of clinical outcome in patients with gastric neuroendocrine tumor (G-NET). Preoperative identification of LNM is helpful for intraoperative lymphadenectomy. This study aims to investigate risk factors for LNM in patients with G-NET. Method: We performed a retrospective study involving 37 patients in non-LNM group and 82 patients in LNM group. Data of demographics, preoperative lab result, clinical pathological results, surgical management and postoperative situation were compared between groups. Significant parameters were subsequently entered into logistic regression for further analysis. Results: Patients in LNM group exhibited older age (p=0.011), lower preoperative albumin (ALB) (p=0.003), higher carcinoembryonic antigen (CEA) (p=0.020), higher International normalized ratio (p=0.034), longer thrombin time (p=0.018), different tumor location (p=0.005), higher chromogranin A positive rate (p=0.045), and higher Ki-67 expression level (p=0.002). Logistic regression revealed ALB (p=0.043), CEA (p=0.032), tumor location (p=0.013) and Ki-67 (p=0.041) were independent risk factors for LNM in G-NET patients. Conclusions: ALB, CEA, tumor location and Ki-67 expression level correlate with the risk of LNM in patients with G-NET.

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