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 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.

Comparative study of preoperative prediction scores for lymph node metastasis in gastric and colon cancer patients

2020 ◽  
Author(s):  
Xiangjian Zheng ◽  
Xiaodong Chen ◽  
Min Li ◽  
Chunmeng Li ◽  
Xian Shen
Keyword(s):  
Risk Factors ◽  
Colon Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Cancer Patients ◽  
Serum Markers ◽  
Tumor Characteristics ◽  
Node Metastasis ◽  
Preoperative Prediction ◽  
Independent Risk Factors

Abstract Background: Surgery combined with chemo-radiotherapy is a recognized model for the treatment of gastric and colon cancers. Lymph node metastasis determines the patient's surgical or comprehensive treatment plan. This analytical study aims to compare preoperative prediction scores to better predict lymph node metastasis in gastric and colon cancer patients.Methods: This study comprised 768 patients, which included 312 patients with gastric cancer and 462 with colon cancer. Preoperative clinical tumor characteristics, serum markers, and immune indices were evaluated using single-factor analysis. Logistic analysis was designed to recognize independent predictors of lymph node metastasis in these patients. The independent risk factors were integrated into preoperative prediction scores, which were accurately assessed using receiver operating characteristic (ROC) curves.Results: Results showed that serum markers (CA125, hemoglobin, albumin), immune indices (S100, CD31, d2–40), and tumor characteristics (pathological type, size) were independent risk factors for lymph node metastasis in patients with gastric and colon cancer. The preoperative prediction scores reliably predicted lymph node metastasis in gastric and colon cancer patients with a higher area under the ROC curve (0.901). The area was 0.923 and 0.870 in gastric cancer and colon cancer, respectively. Based on the ROC curve, the ideal cutoff value of preoperative prediction scores to predict lymph node metastasis was established to be 287. Conclusion: The preoperative prediction scores is a useful indicator that can be applied to predict lymph node metastasis in gastric and colon cancer patients.

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 Nomograms for Predicting the Lymph Node Metastasis in Early Gastric Cancer by Gender: A Retrospective Multicentric Study

2021 ◽  
Vol 11 ◽  
Author(s):  
Wannian Sui ◽  
Zhangming Chen ◽  
Chuanhong Li ◽  
Peifeng Chen ◽  
Kai Song ◽  
...  
Keyword(s):  
Risk Factors ◽  
Gastric Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Depth Of Invasion ◽  
Male And Female ◽  
Node Metastasis ◽  
Independent Risk Factors ◽  
Medical University ◽  
Training Sets

BackgroundLymph node metastasis (LNM) has a significant impact on the prognosis of patients with early gastric cancer (EGC). Our aim was to identify the independent risk factors for LNM and construct nomograms for male and female EGC patients, respectively.MethodsClinicopathological data of 1,742 EGC patients who underwent radical gastrectomy and lymphadenectomy in the First Affiliated Hospital, Second Affiliated Hospital, and Fourth Affiliated Hospital of Anhui Medical University between November 2011 and April 2021 were collected and analyzed retrospectively. Male and female patients from the First Affiliated Hospital of Anhui Medical University were assigned to training sets and then from the Second and Fourth Affiliated Hospitals of Anhui Medical University were enrolled in validation sets. Based on independent risk factors for LNM in male and female EGC patients from the training sets, the nomograms were established respectively, which was also verified by internal validation from the training sets and external validation from the validation sets.ResultsTumor size (odd ratio (OR): 1.386, p = 0.030), depth of invasion (OR: 0.306, p = 0.001), Lauren type (OR: 2.816, p = 0.000), lymphovascular invasion (LVI) (OR: 0.160, p = 0.000), and menopause (OR: 0.296, p = 0.009) were independent risk factors for female EGC patients. For male EGC patients, tumor size (OR: 1.298, p = 0.007), depth of invasion (OR: 0.257, p = 0.000), tumor location (OR: 0.659, p = 0.002), WHO type (OR: 1.419, p = 0.001), Lauren type (OR: 3.099, p = 0.000), and LVI (OR: 0.131, p = 0.000) were independent risk factors. Moreover, nomograms were established to predict the risk of LNM for female and male EGC patients, respectively. The area under the ROC curve of nomograms for female and male training sets were 87.7% (95% confidence interval (CI): 0.8397–0.914) and 94.8% (95% CI: 0.9273–0.9695), respectively. For the validation set, they were 92.4% (95% CI: 0.7979–1) and 93.4% (95% CI: 0.8928–0.9755), respectively. Additionally, the calibration curves showed good agreements between the bias-corrected prediction and the ideal reference line for both training sets and validation sets in female and male EGC patients.ConclusionsNomograms based on risk factors for LNM in male and female EGC patients may provide new insights into the selection of appropriate treatment methods.

scholarly journals Comparative Study of Preoperative Scores to Predict Lymph Node Metastasis in Patients With Gastric and Colon Cancers: A Case Control Study

2020 ◽  
Author(s):  
Xiangjian Zheng ◽  
Xiaodong Chen ◽  
Min Li ◽  
Chunmeng Li ◽  
Xian Shen
Keyword(s):  
Risk Factors ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Roc Curve ◽  
Serum Markers ◽  
Tumor Characteristics ◽  
Immune Markers ◽  
Node Metastasis ◽  
Colon Cancers ◽  
Independent Risk Factors

Abstract Background:Surgery combined with chemo-radiotherapy is a recognized model for the treatment of gastric and colon cancers. Lymph node metastasis determines the patient's surgical or comprehensive treatment plan.The purpose of this study was to compare preoperative scores to better predict lymph node metastasis in patients with gastric and colon cancers.Methods:A total of 768 patients with gastric cancer (312) and colon cancer (462) were enrolled in our study. Preoperative serum markers, immune markers, and clinical tumor characteristics were evaluated by single-factor analysis. Logistic analysis was used to identify independent predictors of lymph node metastasis in patients with gastric and colon cancers. These independent risk factors were integrated into preoperative scores, which was evaluated by receiver operating characteristic (ROC) curves.Results:The result showed that serum markers (CA125, hemoglobin, albumin), immune markers (S100, CD31, d2–40), and tumor characteristics (pathological type, tumor size) were independent risk factors for lymph node metastasis in patients with gastric and colon cancers. The preoperative scores reliably predicted lymph node metastasis in patients with gastric and colon cancers with a higher area under the ROC curve (0.901). Compared to the other independent risk factors, the area under the ROC curve of this indicator was 0.923 and 0.870, for gastric and colon cancers, respectively. Based on the ROC curve, the ideal cutoff value of preoperative scores to predict lymph node metastasis was established to be 287. Conclusion: The preoperative scores is a useful indicator that could be used to predict lymph node metastasis in patients with gastric and colon cancers.

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.

Independent risk factors for lymph node metastasis in 2623 patients with Non–Small cell lung cancer

2020 ◽  
Vol 34 ◽  
pp. 256-260
Author(s):  
Xinying Xue ◽  
Xuelei Zang ◽  
Yuxia Liu ◽  
Dongliang Lin ◽  
Tianjiao Jiang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Lung Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Small Cell Lung ◽  
Node Metastasis ◽  
Independent Risk Factors

scholarly journals Nomograms Combining Ultrasonic Features With Clinical and Pathological Features for Estimation of Delphian Lymph Node Metastasis Risk in Papillary Thyroid Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Qi Qi ◽  
Pan Xu ◽  
Cheng Zhang ◽  
Suping Guo ◽  
Xingzhi Huang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Lymph Nodes ◽  
Clinical Pathology ◽  
Diagnostic Model ◽  
Pathological Features ◽  
Node Metastasis ◽  
Significant Difference ◽  
Independent Risk Factors

BackgroundThis work explores the clinical significance of Delphian lymph nodes (DLN) in thyroid papillary carcinoma (PTC). At the same time, a nomogram is constructed based on clinical, pathological, and ultrasonic (US) features to evaluate the possibility of DLN metastasis (DLNM) in PTC patients. This is the first study to predict DLNM using US characteristics.MethodsA total of 485 patients, surgically diagnosed with PTC between February 2017 and June 2021, all of whom underwent thyroidectomy, were included in the study. Using the clinical, pathological, and US information of patients, the related factors of DLNM were retrospectively analyzed. The risk factors associated with DLNM were identified through univariate and multivariate analyses. According to clinical + pathology, clinical + US, and clinical + US + pathology, the predictive nomogram for DLNM was established and validated.ResultsOf the 485 patients with DLN, 98 (20.2%) exhibited DLNM. The DLNM positive group had higher positive rates of central lymph node metastasis (CLNM), lateral lymph node metastasis (LLNM), and T3b–T4b thyroid tumors than the negative rates. The number of CLNM and LLNM lymph nodes in the DLNM+ group was higher as compared to that in the DLNM- group. Multivariate analysis demonstrated that the common independent risk factors of the three prediction models were male, bilaterality, and located in the isthmus. Age ≥45 years, located in the lower pole, and nodural goiter were protective factors. In addition, the independent risk factors were classified as follows: (I) P-extrathyroidal extension (ETE) and CLNM based on clinical + pathological characteristics; (II) US-ETE and US-CLNM based on clinical + US characteristics; and (III) US-ETE and CLNM based on clinical +US + pathological features. Better diagnostic efficacy was reported with clinical + pathology + US diagnostic model than that of clinical + pathology diagnostic model (AUC 0.872 vs. 0.821, p = 0.039). However, there was no significant difference between clinical + pathology + US diagnostic model and clinical + US diagnostic model (AUC 0.872 vs. 0.821, p = 0.724).ConclusionsThis study found that DLNM may be a sign that PTC is more invasive and has extensive lymph node metastasis. By exploring the clinical, pathology, and US characteristics of PTC progression to DLNM, three prediction nomograms, established according to different combinations of features, can be used in different situations to evaluate the transfer risk of DLN.

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