Novel perioperative parameters-based nomograms for survival outcomes in upper tract urothelial carcinoma after radical nephroureterectomy.

2019 ◽  
Vol 37 (7_suppl) ◽  
pp. 414-414
Author(s):  
Ping Tan ◽  
Lu Yang ◽  
Hang Xu ◽  
Qiang Wei
Keyword(s):  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Prediction Models ◽  
Validation Cohort ◽  
Fibrinogen Level ◽  
External Validation ◽  
Cox Proportional Hazards ◽  
Radical Nephroureterectomy ◽  
Cancer Specific Survival ◽  
Node Metastasis

414 Background: Recently, several postoperative nomograms for cancer-specific survival (CSS) after radical nephroureterectomy (RNU) were proposed, while they did not incorporate the same variables; meanwhile, many preoperative blood-based parameters, which were recently reported to be related to survival, were not included in their models. In addition, no nomogram for overall survival (OS) was available to date. Methods: The full data of 716 patients were available. The whole cohort was randomly divided into two cohorts: the training cohort for developing the nomograms (n = 508) and the validation cohort for validating the models (n = 208). Univariate and multivariate Cox proportional hazards regression models were used for establishing the prediction models. The discriminative accuracy of nomograms were measured by Harrell’s concordance index (C-index). The clinical usefulness and net benefit of the predictive models were estimated and visualized by using Decision curve analyses (DCA). Results: The median follow-up time was 42.0 months (IQR: 18.0-76.0). For CSS, tumor size, grade and pT stage, lymph node metastasis, NLR, PLR and fibrinogen level were identified as independent risk factors in the final model; while tumor grade and pT stage, lymph node metastasis, PLR, Cys-C and fibrinogen level were identified as independent predictors for OS model. The C-index for CSS prediction was 0.82 (95%CI: 0.79-0.85), and the OS nomogram model had an accuracy of 0.83 (95%CI: 0.80-0.86). The results of bootstrapping showed no deviation from the ideal. The calibration plots for the probability of CSS and OS at 3 or 5-year after RNU showed a favorable agreement between the prediction by the nomograms and actual observation. In the external validation cohort, the C-indexes of the nomograms for predicting CSS and OS were 0.79 (95%CI: 0.74-0.84) and 0.80 (95%CI: 0.75-0.85), respectively. As indicated by calibration plots, optimal agreement was observed between prediction and observation in the external cohort. Conclusions: The nomograms developed and validated based on preoperative blood-based parameters were superior to any single variable for predicting CSS and OS after RNU.

scholarly journals A Clinical-Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Gallbladder Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Xingyu Liu ◽  
Xiaoyuan Liang ◽  
Lingxiang Ruan ◽  
Sheng Yan
Keyword(s):  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Gallbladder Cancer ◽  
Prediction Models ◽  
Validation Cohort ◽  
External Validation ◽  
Training Cohort ◽  
Clinical Variables ◽  
Node Metastasis ◽  
Preoperative Prediction

ObjectivesThe aim of the current study was to develop and validate a nomogram based on CT radiomics features and clinical variables for predicting lymph node metastasis (LNM) in gallbladder cancer (GBC).MethodsA total of 353 GBC patients from two hospitals were enrolled in this study. A Radscore was developed using least absolute shrinkage and selection operator (LASSO) logistic model based on the radiomics features extracted from the portal venous-phase computed tomography (CT). Four prediction models were constructed based on the training cohort and were validated using internal and external validation cohorts. The most effective model was then selected to build a nomogram.ResultsThe clinical-radiomics nomogram, which comprised Radscore and three clinical variables, showed the best diagnostic efficiency in the training cohort (AUC = 0.851), internal validation cohort (AUC = 0.819), and external validation cohort (AUC = 0.824). Calibration curves showed good discrimination ability of the nomogram using the validation cohorts. Decision curve analysis (DCA) showed that the nomogram had a high clinical utility.ConclusionThe findings showed that the clinical-radiomics nomogram based on radiomics features and clinical parameters is a promising tool for preoperative prediction of LN status in patients with GBC.

scholarly journals Development and validation of a nomogram for preoperative prediction of lymph node metastasis in early gastric cancer

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Xiao-Yi Yin ◽  
Tao Pang ◽  
Yu Liu ◽  
Hang-Tian Cui ◽  
Tian-Hang Luo ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Early Gastric Cancer ◽  
Cancer Patients ◽  
Validation Cohort ◽  
External Validation ◽  
Training Cohort ◽  
Node Metastasis ◽  
Gastric Cancer Patients

Abstract Background The status of lymph nodes in early gastric cancer is critical to make further clinical treatment decision, but the prediction of lymph node metastasis remains difficult before operation. This study aimed to develop a nomogram that contained preoperative factors to predict lymph node metastasis in early gastric cancer patients. Methods This study analyzed the clinicopathologic features of 823 early gastric cancer patients who underwent gastrectomy retrospectively, among which 596 patients were recruited in the training cohort and 227 patients in the independent validation cohort. Significant risk factors in univariate analysis were further identified to be independent variables in multivariable logistic regression analysis, which were then incorporated in and presented with a nomogram. And internal and external validation curves were plotted to evaluate the discrimination of the nomogram. Results Totally, six independent predictors, including the tumor size, macroscopic features, histology differentiation, P53, carbohydrate antigen 19-9, and computed tomography-reported lymph node status, were enrolled in the nomogram. Both the internal validation in the training cohort and the external validation in the validation cohort showed the nomogram had good discriminations, with a C-index of 0.82 (95%CI, 0.78 to 0.86) and 0.77 (95%CI, 0.60 to 0.94) respectively. Conclusions Our study developed a new nomogram which contained the most common and significant preoperative risk factors for lymph node metastasis in patients with early gastric cancer. The nomogram can identify early gastric cancer patients with the high probability of lymph node metastasis and help clinicians make more appropriate decisions in clinical practice.

Deep learning nomogram for predicting lymph node metastasis using computed tomography image in cervical cancer

2021 ◽  
pp. 028418512110589
Author(s):  
Peijun Li ◽  
Bao Feng ◽  
Yu Liu ◽  
Yehang Chen ◽  
Haoyang Zhou ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Cervical Cancer ◽  
Deep Learning ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Validation Cohort ◽  
External Validation ◽  
Lymph Node Status ◽  
Internal Validation ◽  
Node Metastasis

Background Deep learning (DL) has been used on medical images to grade, differentiate, and predict prognosis in many tumors. Purpose To explore the effect of computed tomography (CT)-based deep learning nomogram (DLN) for predicting cervical cancer lymph node metastasis (LNM) before surgery. Material and Methods In total, 418 patients with stage IB-IIB cervical cancer were retrospectively enrolled for model exploration (n = 296) and internal validation (n = 122); 62 patients from another independent institution were enrolled for external validation. A convolutional neural network (CNN) was used for DL features extracting from all lesions. The least absolute shrinkage and selection operator (Lasso) logistic regression was used to develop a deep learning signature (DLS). A DLN incorporating the DLS and clinical risk factors was proposed to predict LNM individually. The performance of the DLN was evaluated on internal and external validation cohorts. Results Stage, CT-reported pelvic lymph node status, and DLS were found to be independent predictors and could be used to construct the DLN. The combination showed a better performance than the clinical model and DLS. The proposed DLN had an area under the curve (AUC) of 0.925 in the training cohort, 0.771 in the internal validation cohort, and 0.790 in the external validation cohort. Decision curve analysis and stratification analysis suggested that the DLN has potential ability to generate a personalized probability of LNM in cervical cancer. Conclusion The proposed CT-based DLN could be used as a personalized non-invasive tool for preoperative prediction of LNM in cervical cancer, which could facilitate the choice of clinical treatment methods.

scholarly journals A nomogram prediction model for lymph node metastasis in endometrial cancer patients

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Zhiling Wang ◽  
Shuo Zhang ◽  
Yifei Ma ◽  
Wenhui Li ◽  
Jiguang Tian ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Endometrial Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
External Validation ◽  
Clinicopathological Parameters ◽  
Bootstrap Sample ◽  
Multivariate Logistic Regression ◽  
Node Metastasis ◽  
Increased Risk

Abstract Background This study aimed to explore the risk factors for lymph node metastasis (LNM) in patients with endometrial cancer (EC) and develop a clinically useful nomogram based on clinicopathological parameters to predict it. Methods Clinical information of patients who underwent staging surgery for EC was abstracted from Qilu Hospital of Shandong University from January 1st, 2005 to June 31st, 2019. Parameters including patient-related, tumor-related, and preoperative hematologic examination-related were analyzed by univariate and multivariate logistic regression to determine the correlation with LNM. A nomogram based on the multivariate results was constructed and underwent internal and external validation to predict the probability of LNM. Results The overall data from the 1517 patients who met the inclusion criteria were analyzed. 105(6.29%) patients had LNM. According the univariate analysis and multivariate logistic regression analysis, LVSI is the most predictive factor for LNM, patients with positive LVSI had 13.156-fold increased risk for LNM (95%CI:6.834–25.324; P < 0.001). The nomogram was constructed and incorporated valuable parameters including histological type, histological grade, depth of myometrial invasion, LVSI, cervical involvement, parametrial involvement, and HGB levels from training set. The nomogram was cross-validated internally by the 1000 bootstrap sample and showed good discrimination accuracy. The c-index for internal and external validation of the nomogram are 0.916(95%CI:0.849–0.982) and 0.873(95%CI:0.776–0.970), respectively. Conclusions We developed and validated a 7-variable nomogram with a high concordance probability to predict the risk of LNM in patients with EC.

scholarly journals The clinical significance of preoperative serum fibrinogen levels and platelet counts in patients with gallbladder carcinoma

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Peng Cao ◽  
Lei Jiang ◽  
Liang-Yi Zhou ◽  
Yan-Ling Chen
Keyword(s):  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Distant Metastasis ◽  
Fibrinogen Level ◽  
Tnm Stage ◽  
Tumor Depth ◽  
Platelet Counts ◽  
Node Metastasis ◽  
Preoperative Serum ◽  
Level Group

Abstract Background Gallbladder carcinoma (GBC) was the most common malignancy of biliary tract. Patients with malignancies frequently present with activated coagulation pathways, which might potentially related to tumor progression and prognosis. The purpose of the study was to investigate the clinical significance of preoperative serum fibrinogen levels and platelet counts in GBC patients. Methods The preoperative fasting serum fibrinogen levels and platelet counts of 58 patients with GBC were measured by AUV2700 automatic biochemical analyzer, as well as 60 patients with cholesterol polyps and 60 healthy volunteers. Kaplan–Meier survival analysis was applied to show the correction between fibrinogen levels and outcome after surgery. Results The fibrinogen levels of patients with GBC were significantly higher than healthy gallbladder and cholesterol polyp of gallbladder (p < 0.001 and p < 0.001, respectively). In GBC, fibrinogen levels were associated with tumor depth (p = 0.001), lymph node metastasis (p = 0.002), distant metastasis (p < 0.001) and Tumor Node Metastasis (TNM) stage (p < 0.001). The levels in TNM stage IV disease were significantly higher than stage III or stage I + II disease (p = 0.048 and p < 0.001, respectively), and in TNM stage III disease were significantly higher than stage I + II disease (p = 0.002). Furthermore, the overall survival was better in low fibrinogen level group than in high fibrinogen level group (p < 0.001). However, thrombocytosis was not significantly associated with overall survivals (p > 0.05) in multivariate analysis. Conclusions The preoperative serum fibrinogen levels and platelet counts might be reliable biomarkers for the occurance of disease, tumor depth, lymph node metastasis, distant metastasis and advanced TNM stage in patients with GBC. The serum fibrinogen levels might be a prognostic factor to predict outcome for GBC patients suffering from surgery treatment. Anticoagulation therapy might be considered to control cancer progression in future studies.

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 Lymph Node Dissections for T3T4 Stage Penile Cancer Patients Without Preoperatively Detectable Lymph Node Metastasis Bring More Survival Benefits: A Propensity Matching Analysis

2021 ◽  
Vol 11 ◽  
Author(s):  
Han Li ◽  
Yucheng Ma ◽  
Zhongyu Jian ◽  
Xi Jin ◽  
Liyuan Xiang ◽  
...  
Keyword(s):  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Cancer Patients ◽  
Penile Cancer ◽  
Cox Regression ◽  
Regression Analyses ◽  
Node Dissection ◽  
Cancer Specific Survival ◽  
Node Metastasis ◽  
Propensity Matching

Background and AimsThe current guidelines for the treatment of penile cancer patients with clinically non-invasive normal inguinal lymph nodes are still broad, so the purpose of this study is to determine which patients are suitable for lymph node dissection (LND).MethodsHistologically confirmed penile cancer patients (primary site labeled as C60.9-Penis) from 2004 to 2016 in the Surveillance, Epidemiology, and Results database were included in this analysis. Univariate and multivariate Cox regression analyses were applied to determine an overall estimate of LND on overall survival and cancer-specific survival. A 1:1 propensity matching analysis (PSM) was applied to enroll balanced baseline cohort, and further Kaplan–Meier (KM) survival analysis was used to get more reliable results.ResultsOut of 4,458 histologically confirmed penile cancer patients with complete follow-up information, 1,052 patients were finally enrolled in this analysis. Age, pathological grade, T stage, and LND were identified as significant predictors for overall survival (OS) in the univariate Cox analysis. In the multivariate Cox regression, age, pathological grade, T stage, and LND were found significant. The same results were also found in the univariate and multivariate Cox regression analyses for cancer-specific survival (CSS). After the successful PSM, further KM analysis revealed that LND could bring significant OS and CSS benefits for T3T4 patients without lymph node metastasis.ConclusionLymph node dissection may bring survival benefits for penile cancer patients without preoperatively detectable lymph node metastasis, especially for T3T4 stage patients. Further randomized control trial is needed.

Transcriptomics based prediction of metastasis in TNBC patients: Challenges in cross-platforms validation

2021 ◽  
Author(s):  
Naorem Leimarembi Devi ◽  
Anjali Dhall ◽  
Sumeet Patiyal ◽  
Gajendra P. S. Raghava
Keyword(s):  
Breast Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Prediction Models ◽  
Machine Learning Techniques ◽  
Lymph Node Status ◽  
P Value ◽  
Gene Signatures ◽  
Node Metastasis ◽  
Microarray Datasets

Triple-negative breast cancer (TNBC) is more prone to metastasis and recurrence than other breast cancer subtypes. This study aimed to identify genes that can act as diagnostic biomarkers for predicting lymph node metastasis in TNBC patients. The transcriptomic data of TNBC with or without lymph node metastasis was acquired from TCGA, and the differentially expressed genes were identified. Further, logistic-regression method has been used to identify the top 15 genes (or 15 gene signatures) based on their ability to predict metastasis (AUC>0.65). These 15 gene signatures were used to develop machine learning techniques based prediction models; Gaussian Naive Bayes classifier outperformed other with AUC>0.80 on both training and validation datasets. The best model failed drastically on nine independent microarray datasets obtained from GEO. We investigated the reason for the failure of our best model, and it was observed that the certain genes in 15 gene signatures were showing opposite regulating trends, i.e., genes are upregulated in TCGA-TNBC patients while it is downregulated on other microarray datasets or vice-versa. In conclusion, the 15 gene signatures may act as diagnostic markers for the detection of lymph node metastatic status in TCGA dataset, but quite challenging across multiple platforms. We also identified the prognostic potential of the 15 selected genes and found that overexpression of ZNRF2, FRZB, and TCEAL4 was associated with poor survival with HR>2.3 and p-value≤0.05. In order to provide services to the scientific community, we developed a webserver named 'MTNBCPred' for the prediction of metastatic and non-metastatic lymph node status of TNBC patients (http://webs.iiitd.edu.in/raghava/mtnbcpred/ ).

Sign in / Sign up

Export Citation Format

Share Document