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 A Novel RNA-Seq-Based Model for Preoperative Prediction of Lymph Node Metastasis in Oral Squamous Cell Carcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Bo Qiao ◽  
Min Zhao ◽  
Jing Wu ◽  
Huan Wu ◽  
Yiming Zhao ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Oral Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Rna Seq ◽  
Node Metastasis ◽  
Preoperative Prediction

Objective. To develop and validate a novel RNA-seq-based nomogram for preoperative prediction of lymph node metastasis (LNM) for patients with oral squamous cell carcinoma (OSCC). Methods. RNA-seq data for 276 OSCC patients (including 157 samples with LNM and 119 without LNM) were downloaded from TCGA database. Differential expression analysis was performed between LNM and non-LNM of OSCC. These samples were divided into a training set and a test set by a ratio of 9 : 1 while the relative proportion of the non-LNM and LNM groups was kept balanced within each dataset. Based on clinical features and seven candidate RNAs, we established a prediction model of LNM for OSCC using logistic regression analysis. Tenfold crossvalidation was utilized to examine the accuracy of the nomogram. Decision curve analysis was performed to evaluate the clinical utility of the nomogram. Results. A total of 139 differentially expressed RNAs were identified between LNM and non-LNM of OSCC. Seven candidate RNAs were screened based on FPKM values, including NEURL1, AL162581.1 (miscRNA), AP002336.2 (lncRNA), CCBE1, CORO6, RDH12, and AC129492.6 (pseudogene). Logistic regression analysis revealed that the clinical N stage (p<0.001) was an important factor to predict LNM. Moreover, three RNAs including RDH12 (p value < 0.05), CCBE1 (p value < 0.01), and AL162581.1 (p value < 0.05) could be predictive biomarkers for LNM in OSCC patients. The average accuracy rate of the model was 0.7661, indicating a good performance of the model. Conclusion. Our findings constructed an RNA-seq-based nomogram combined with clinicopathology, which could potentially provide clinicians with a useful tool for preoperative prediction of LNM and be tailored for individualized therapy in patients with OSCC.

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.

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 The prognostic value of tumor size in penile cancer: A Surveillance,Epidemiology, and End Results database study

2020 ◽  
Author(s):  
Wenwen Zheng ◽  
Zhiyu Zhang ◽  
Xilei Xie ◽  
Weiwei Zhu ◽  
Kangqi Li ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Logistic Regression Analysis ◽  
Tumor Size ◽  
Prognostic Value ◽  
Penile Cancer ◽  
Node Metastasis ◽  
End Results

Abstract Background: The objective of this study was to investigate the prognostic value of tumor size on cancer-specific mortality (CSM) and lymph node metastasis for patients with penile squamous cell carcinoma (PSCC).Method: The patients diagnosed with PSCC between 2004 and 2015 were selected from the Surveillance, Epidemiology, and End Results (SEER) database. Restricted cubic spline functions were calculated to characterize the association between tumor size and the risk of CSM. The competing-risks model was used to evaluate the impact of tumor size on the cumulative incidence of CSM. The logistic regression analysis was performed to examine the association between tumor size and lymph node metastasis.Results: Totally, 1365 PSCC patients were analyzed, with 52.3% having tumors ≤30 mm, and 47.7% >30 mm. The restricted cubic splines showed that the risks of CSM increased as tumors enlarged. Following adjustment of competing events, the PSCC patients with tumors >30 mm were more likely to succumb to CSM in comparison with those with tumors ≤30 mm (hazard ratio [HR]=1.57, 95% confidence interval [CI]: 1.23-2.01, P<0.001). In subgroup analyses, tumor size >30 mm was significantly associated with an increased risk of CSM relative to tumor size ≤30 mm among patients with T1 (HR=1.56, 95%CI: 1.03-2.37, P=0.036) and T3 (HR=2.51, 95%CI: 1.41-4.45, P=0.002) classifications. On logistic regression analysis, tumors >30 mm were significantly associated with lymph node metastasis (odds ratio [OR]=1.46, 95% CI: 1.03-2.07, P=0.034).Conclusion: Larger tumors (>30 mm) were significantly associated with higher risks of CSM and increased likelihood of lymph node metastasis for PSCC patients, which could be integrated into the development of a staging system for penile cancer.

scholarly journals Can CA-125 Predict Lymph Node Metastasis in Epithelial Ovarian Cancers in Turkish Population?

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Sinem Sudolmuş ◽  
Nadiye Köroğlu ◽  
Gökhan Yıldırım ◽  
Volkan Ülker ◽  
Ahmet Gülkılık ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Logistic Regression Analysis ◽  
Geometric Mean ◽  
Turkish Population ◽  
Ca 125 ◽  
Node Metastasis ◽  
Preoperative Serum

Objective. The role of single preoperative serum CA-125 levels in predicting pelvic or paraaortic lymph node metastasis in patients operated for epithelial ovarian cancer has been investigated.Methods. 176 patients diagnosed with epithelial ovarian carcinoma after staging laparotomy between January 2002 and May 2010 were evaluated retrospectively.Results. The mean, geometric mean, and median of preoperative serum CA-125 levels were 632,6, 200,29, and 191,5 U/mL, respectively. The cut-off value predicting lymph node metastases in the ROC curve was 71,92 U/mL, which is significant in logistic regression analysis (P=0.005). The preoperative log CA-125 levels were also statistically significant in predicting lymph node metastasis in logistic regression analysis (P=0.008).Conclusions. The tumor marker CA-125, which increases with grade independent of the effect of stage in EOC, is predictive of lymph node metastasis with a high rate of false positivity in Turkish population. The high false positive rate may obscure the predictive value of CA-125.

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 The prognostic value of tumor size in penile cancer: A Surveillance, Epidemiology, and End Results database study

2021 ◽  
Author(s):  
Wenwen Zheng ◽  
Zhiyu Zhang ◽  
Wei Jiang ◽  
Jiaojiao Chen ◽  
Shengqiang Yu ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Logistic Regression Analysis ◽  
Tumor Size ◽  
Prognostic Value ◽  
Penile Cancer ◽  
Node Metastasis ◽  
End Results

Abstract Background The objective of this study was to investigate the prognostic value of tumor size on cancer-specific mortality (CSM) and lymph node metastasis for patients with penile squamous cell carcinoma (PSCC). Methods The patients diagnosed with PSCC between 2004 and 2015 were selected from the Surveillance, Epidemiology, and End Results (SEER) database. Restricted cubic spline functions were calculated to characterize the association between tumor size and the risk of CSM. The competing-risks model was used to evaluate the impact of tumor size on the cumulative incidence of CSM. The logistic regression analysis was performed to examine the association between tumor size and lymph node metastasis. Results Totally, 1365 PSCC patients were analyzed, with 52.3% having tumors ≤ 30 mm, and 47.7% >30 mm. The restricted cubic splines showed that the risks of CSM increased as tumors enlarged. Following adjustment of competing events, the PSCC patients with tumors > 30 mm were more likely to succumb to CSM in comparison with those with tumors ≤ 30 mm (hazard ratio [HR] = 1.57, 95% confidence interval [CI]: 1.23–2.01, P < 0.001). In subgroup analyses, tumor size > 30 mm was significantly associated with an increased risk of CSM relative to tumor size ≤ 30 mm among patients with T1 (HR = 1.56, 95%CI: 1.03–2.37, P = 0.036) and T3 (HR = 2.51, 95%CI: 1.41–4.45, P = 0.002) classifications. On logistic regression analysis, tumors > 30 mm were significantly associated with lymph node metastasis (odds ratio [OR] = 1.46, 95% CI: 1.03–2.07, P = 0.034). Conclusions Larger tumors (> 30 mm) were significantly associated with higher risks of CSM and increased likelihood of lymph node metastasis for PSCC patients, which could be integrated into the development of a staging system for penile cancer.

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