scholarly journals A Novel Nomogram Based on Hepatic and Coagulation Function for Evaluating Outcomes of Intrahepatic Cholangiocarcinoma After Curative Hepatectomy: A Multi-Center Study of 653 Patients

2021 ◽  
Vol 11 ◽  
Author(s):  
Yunshi Cai ◽  
Bohan Zhang ◽  
Jiaxin Li ◽  
Hui Li ◽  
Hailing Liu ◽  
...  
Keyword(s):  
Intrahepatic Cholangiocarcinoma ◽  
Calibration Curve ◽  
Prognostic Value ◽  
Roc Curves ◽  
Curve Analysis ◽  
Time Dependent ◽  
Decision Curve Analysis ◽  
Coagulation Function ◽  
Validation Set ◽  
Curative Hepatectomy

Background and AimsHepatic and coagulation function are routine laboratory tests prior to curative hepatectomy. The prognostic value of gamma-glutamyl transpeptidase (GGT) to platelet ratio (GPR) and international normalized ratio (INR) in surgically treated patients with intrahepatic cholangiocarcinoma (ICC) remains unclear.MethodsICC patients received curative hepatectomy in two west China centers were included. Time-dependent ROC curves were conducted to compare established indexes with prognostic value for ICC. GPR-INR score was introduced and evaluated using the Time-dependent AUC curve and Kaplan-Meier survival analysis. A novel nomogram based on the GPR-INR score was proposed; Harrell’s C-index, calibration curve and decision curve analysis were used to assess this nomogram.ResultsA total of 653 patients were included. The areas under ROC curves of GPR and INR in OS and RFS were superior to other indexes. Patients with a high GPR-INR score (1,2) presented significantly decreased overall survival (OS) and recurrence-free survival (RFS); GPR-INR sore, along with several clinicopathological indexes were selected into the nomogram, the calibration curve for OS probability showed good coincidence between the nomogram and the actual surveillance. The C-index of the nomogram was 0.708 (derivation set) and 0.746 (validation set), which was more representative than the C-indexes of the GPR-INR score (0.597, 0.678). In decision curve analysis, the net benefits of the nomogram in derivation and validation set were higher than Barcelona Clinic Liver Cancer staging (BCLC) classification and American Joint Committee on Cancer (AJCC) TNM 8th staging system.ConclusionsThe proposed nomogram generated superior discriminative ability to established staging systems; it is profitable to applicate this nomogram in clinical practice.

scholarly journals Scoring system development and validation for prediction choledocholithiasis before open cholecystectomy

2015 ◽  
Vol 143 (11-12) ◽  
pp. 681-687 ◽  
Author(s):  
Tomislav Pejovic ◽  
Miroslav Stojadinovic
Keyword(s):  
Bile Duct ◽  
Clinical Utility ◽  
Clinical Decision Making ◽  
Prediction Models ◽  
Open Cholecystectomy ◽  
Univariate Analysis ◽  
Curve Analysis ◽  
Decision Curve Analysis ◽  
Scoring Model ◽  
Validation Set

Introduction. Accurate precholecystectomy detection of concurrent asymptomatic common bile duct stones (CBDS) is key in the clinical decision-making process. The standard preoperative methods used to diagnose these patients are often not accurate enough. Objective. The aim of the study was to develop a scoring model that would predict CBDS before open cholecystectomy. Methods. We retrospectively collected preoperative (demographic, biochemical, ultrasonographic) and intraoperative (intraoperative cholangiography) data for 313 patients at the department of General Surgery at Gornji Milanovac from 2004 to 2007. The patients were divided into a derivation (213) and a validation set (100). Univariate and multivariate regression analysis was used to determine independent predictors of CBDS. These predictors were used to develop scoring model. Various measures for the assessment of risk prediction models were determined, such as predictive ability, accuracy, the area under the receiver operating characteristic curve (AUC), calibration and clinical utility using decision curve analysis. Results. In a univariate analysis, seven risk factors displayed significant correlation with CBDS. Total bilirubin, alkaline phosphatase and bile duct dilation were identified as independent predictors of choledocholithiasis. The resultant total possible score in the derivation set ranged from 7.6 to 27.9. Scoring model shows good discriminatory ability in the derivation and validation set (AUC 94.3 and 89.9%, respectively), excellent accuracy (95.5%), satisfactory calibration in the derivation set, similar Brier scores and clinical utility in decision curve analysis. Conclusion. Developed scoring model might successfully estimate the presence of choledocholithiasis in patients planned for elective open cholecystectomy.

scholarly journals Investigating effects of preoperative inflammatory biomarkers on predicting survival outcomes of intrahepatic cholangiocarcinoma after curative resection

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Zeyu Zhang ◽  
Yufan Zhou ◽  
Kuan Hu ◽  
Yun Huang
Keyword(s):  
Intrahepatic Cholangiocarcinoma ◽  
Survival Data ◽  
Roc Curves ◽  
Inflammatory Biomarkers ◽  
Free Survival ◽  
Lymphocyte Ratio ◽  
Lymphocyte To Monocyte Ratio ◽  
Kaplan Meier ◽  
Patient Prognosis ◽  
Curative Hepatectomy

Abstract Introduction Intrahepatic cholangiocarcinoma (ICC) stands as the second most common malignant tumor in the liver with poor patient prognosis. Increasing evidences have shown that inflammation plays a significant role in tumor progression, angiogenesis, and metastasis. However, the prognosis significance of inflammatory biomarkers on recurrence-free survival (RFS) and overall survival (OS) in ICC patients is poorly recognized. Methods ICC patients who underwent curative hepatectomy and diagnosed pathologically were retrospectively analyzed. Inflammatory biomarkers, including neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), and systemic immune-inflammation index (SII), were investigated. Results Receiver operating characteristic (ROC) curves showed no significance in NLR, PLR, and LMR in RFS and OS, while significant results were shown on SII in both RFS (P = 0.035) and OS (P = 0.034) with areas under ROC curve as 0.63 (95%CI 0.52–0.74) and 0.62 (95%CI 0.51–0.72), respectively. Kaplan-Meier curves revealed a statistically significant better survival data in SII-low groups on both RFS (P < 0.001) and OS (P < 0.001). The univariate and multivariate analyses revealed that higher level of SII was independently associated with both poorer RFS time and OS time. However, no significant result was shown on NLR, PLR, or LMR. Conclusion SII is an effective prognostic factor for predicting the prognosis of ICC patient undergone curative hepatectomy, while NLR, PLR, and LMR are not associated with clinical outcomes of these patients.

scholarly journals Prognostic nomogram for predicting 5-year overall survival in Chinese patients with high-grade osteosarcoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhihong Yao ◽  
Zunxian Tan ◽  
Jifei Yang ◽  
Yihao Yang ◽  
Cao Wang ◽  
...  
Keyword(s):  
Overall Survival ◽  
Calibration Curve ◽  
Curve Analysis ◽  
Cox Proportional Hazards Model ◽  
Chinese Patients ◽  
Gamma Glutamyl Transferase ◽  
High Grade ◽  
Decision Curve Analysis ◽  
High Grade Osteosarcoma ◽  
Glutamyl Transferase

AbstractThis study aimed to construct a widely accepted prognostic nomogram in Chinese high-grade osteosarcoma (HOS) patients aged ≤ 30 years to provide insight into predicting 5-year overall survival (OS). Data from 503 consecutive HOS patients at our centre between 12/2012 and 05/2019 were retrospectively collected. Eighty-four clinical features and routine laboratory haematological and biochemical testing indicators of each patient at the time of diagnosis were collected. A prognostic nomogram model for predicting OS was constructed based on the Cox proportional hazards model. The performance was assessed by the concordance index (C-index), receiver operating characteristic curve and calibration curve. The utility was evaluated by decision curve analysis. The 5-year OS was 52.1% and 2.6% for the nonmetastatic and metastatic patients, respectively. The nomogram included nine important variables based on a multivariate analysis: tumour stage, surgical type, metastasis, preoperative neoadjuvant chemotherapy cycle, postoperative metastasis time, mean corpuscular volume, tumour-specific growth factor, gamma-glutamyl transferase and creatinine. The calibration curve showed that the nomogram was able to predict 5-year OS accurately. The C-index of the nomogram for OS prediction was 0.795 (range, 0.703–0.887). Moreover, the decision curve analysis curve also demonstrated the clinical benefit of this model. The nomogram provides an individualized risk estimate of the 5-year OS in patients with HOS aged ≤ 30 years in a Chinese population-based cohort.

Prognosis viewing for nasopharyngeal carcinoma treated with intensity-modulated radiation therapy: application of nomogram and decision curve analysis

2019 ◽  
Vol 50 (2) ◽  
pp. 159-168
Author(s):  
Zhaodong Fei ◽  
Xiufang Qiu ◽  
Mengying Li ◽  
Chuanben Chen ◽  
Yi Li ◽  
...  
Keyword(s):  
Radiation Therapy ◽  
Nasopharyngeal Carcinoma ◽  
Calibration Curve ◽  
Validation Cohort ◽  
Intensity Modulated Radiation Therapy ◽  
Curve Analysis ◽  
Decision Curve Analysis ◽  
Intensity Modulated ◽  
T Stage ◽  
N Stage

Abstract Objective To view and evaluate the prognosis factors in patients with nasopharyngeal carcinoma (NPC) treated with intensity modulated radiation therapy using nomogram and decision curve analysis (DCA). Methods Based on a primary cohort comprising consecutive patients with newly confirmed NPC (n = 1140) treated between January 2014 and December 2015, we identified independent prognostic factors of overall survival (OS) to establish a nomogram. The model was assessed by bootstrap internal validation and external validation in an independent validation cohort of 460 patients treated between January 2013 and December 2013. The predictive accuracy and discriminative ability were measured by calibration curve, concordance index (C-index) and risk-group stratification. The clinical usefulness was assessed by DCA. Results The nomogram incorporated T-stage, N-stage, age, concurrent chemotherapy and primary tumour volume (PTV). The calibration curve presented good agreement for between the nomogram-predicted OS and the actual measured survival probability in both the primary and validation cohorts. The model showed good discrimination with a C-index of 0.741 in the primary cohort and 0.762 in the validation cohort. The survival curves of different risk-groups were separated clearly. Decision curve analysis demonstrated that the nomogram provided a higher net benefit (NB) across a wider reasonable range of threshold probabilities for predicting OS. Conclusion This study presents a predictive nomogram model with accurate prediction and independent discrimination ability compared with combination of T-stage and N-stage. The results of DCA supported the point that PTV can help improve the prognostic ability of T-stage and should be added to the TNM staging system.

scholarly journals An integrated nomogram combining lncRNAs classifier and clinicopathologic factors to predict the recurrence of head and neck squamous cell carcinoma

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Jie Cui ◽  
Qingquan Wen ◽  
Xiaojun Tan ◽  
Jinsong Piao ◽  
Qiong Zhang ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Head And Neck ◽  
Squamous Cell ◽  
Calibration Curve ◽  
Cox Regression ◽  
Curve Analysis ◽  
Tnm Stage ◽  
Decision Curve Analysis ◽  
Clinicopathologic Factors

AbstractLong non-coding RNAs (lncRNAs) which have little or no protein-coding capacity, due to their potential roles in the cancer disease, caught a particular interest. Our study aims to develop an lncRNAs-based classifier and a nomogram incorporating the lncRNAs classifier and clinicopathologic factors to help to improve the accuracy of recurrence prediction for head and neck squamous cell carcinoma (HNSCC) patients. The HNSCC lncRNAs profiling data and the corresponding clinicopathologic information were downloaded from TANRIC database and cBioPortal. Using univariable Cox regression and Least absolute shrinkage and selection operator (LASSO) analysis, we developed 15-lncRNAs-based classifier related to recurrence. On the basis of multivariable Cox regression analysis results, a nomogram integrating the genomic and clinicopathologic predictors was built. The predictive accuracy and discriminative ability of the inclusive nomogram were confirmed by calibration curve and a concordance index (C-index), and compared with TNM stage system by C-index, receiver operating characteristic (ROC) analysis. Decision curve analysis (DCA) was conducted to evaluate clinical value of our nomogram. Consequently, fifteen recurrence-free survival (RFS) -related lncRNAs were identified, and the classifier consisting of the established 15 lncRNAs could effectively divide patients into high-risk and low-risk subgroup. The prediction ability of the 15-lncRNAs-based classifier for predicting 3- year and 5-year RFS were 0.833 and 0.771. Independent factors derived from multivariable analysis to predict recurrence were number of positive LNs, margin status, mutation count and lncRNAs classifier, which were all embedded into the nomogram. The calibration curve for the recurrence probability showed that the predictions based on the nomogram were in good coincide with practical observations. The C-index of the nomogram was 0.76 (0.72–0.79), and the area under curve (AUC) of nomogram in predicting RFS was 0.809, which were significantly higher than traditional TNM stage and 15-lncRNAs-based classifier. Decision curve analysis further demonstrated that our nomogram had larger net benefit than TNM stage and 15-lncRNAs-based classifier. The results were confirmed externally. In summary, a visually inclusive nomogram for patients with HNSCC, comprising genomic and clinicopathologic variables, generates more accurate prediction of the recurrence probability when compared TNM stage alone, but more additional data remains needed before being used in clinical practice.

P1017 EVALUATION OF THE PROGNOSTIC VALUE OF NON-INVASIVE LIVER FIBROSIS TESTS TO PREDICT MORTALITY BY TIME-DEPENDENT ROC CURVES

2014 ◽  
Vol 60 (1) ◽  
pp. S414
Author(s):  
S. Bertrais ◽  
J. Boursier ◽  
F. Oberti ◽  
I. Fouchard-Hubert ◽  
P. Calès
Keyword(s):  
Liver Fibrosis ◽  
Prognostic Value ◽  
Roc Curves ◽  
Time Dependent ◽  
Non Invasive

scholarly journals Multiparameter MRI Radiomics Model Predicts Preoperative Peritoneal Carcinomatosis in Ovarian Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Xiao Yu Yu ◽  
Jialiang Ren ◽  
Yushan Jia ◽  
Hui Wu ◽  
Guangming Niu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Ovarian Cancer ◽  
Peritoneal Carcinomatosis ◽  
Calibration Curve ◽  
Predictive Accuracy ◽  
Characteristic Curve ◽  
Curve Analysis ◽  
Imaging Features ◽  
Decision Curve Analysis ◽  
Dce Mri

ObjectivesTo evaluate the predictive value of radiomics features based on multiparameter magnetic resonance imaging (MP-MRI) for peritoneal carcinomatosis (PC) in patients with ovarian cancer (OC).MethodsA total of 86 patients with epithelial OC were included in this retrospective study. All patients underwent FS-T2WI, DWI, and DCE-MRI scans, followed by total hysterectomy plus omentectomy. Quantitative imaging features were extracted from preoperative FS-T2WI, DWI, and DCE-MRI images, and feature screening was performed using a minimum redundancy maximum correlation (mRMR) and least absolute shrinkage selection operator (LASSO) methods. Four radiomics models were constructed based on three MRI sequences. Then, combined with radiomics characteristics and clinicopathological risk factors, a multi-factor Logistic regression method was used to construct a radiomics nomogram, and the performance of the radiomics nomogram was evaluated by receiver operating characteristic curve (ROC) curve, calibration curve, and decision curve analysis.ResultsThe radiomics model from the MP-MRI combined sequence showed a higher area under the curve (AUC) than the model from FS-T2WI, DWI, and DCE-MRI alone (0.846 vs. 0.762, 0.830, 0.807, respectively). The radiomics nomogram (AUC=0.902) constructed by combining radiomics characteristics and clinicopathological risk factors showed a better diagnostic effect than the clinical model (AUC=0.858) and the radiomics model (AUC=0.846). The decision curve analysis shows that the radiomics nomogram has good clinical application value, and the calibration curve also proves that it has good stability.ConclusionRadiomics nomogram based on MP-MRI combined sequence showed good predictive accuracy for PC in patients with OC. This tool can be used to identify peritoneal carcinomatosis in OC patients before surgery.

scholarly journals Prediction of vaginal birth after cesarean delivery in southeast China: A retrospective cohort study

2020 ◽  
Author(s):  
Hua-Le Zhang ◽  
Liang-Hui Zheng ◽  
Li-Chun Cheng ◽  
Zhao-Dong Liu ◽  
Lu Yu ◽  
...  
Keyword(s):  
Cesarean Section ◽  
Pregnant Women ◽  
Success Rate ◽  
Gestational Age ◽  
Curve Analysis ◽  
Inclusion Criteria ◽  
Vaginal Birth After Cesarean ◽  
Training Set ◽  
Decision Curve Analysis ◽  
Validation Set

Abstract Objective To develop and validate a nomogram to better predict the vaginal birth after cesarean (VBAC) on the premise of clinical guide application. Methods We retrospectively identified hospitalised pregnant women who trial of labor after cesarean (TOLAC) between October 2015 and October 2017 using data from the Fujian Provincial Maternity and Children's Hospital. The inclusion criteria were as follows: Singleton pregnant women whose gestational age was above 37 weeks and underwent a primary cesarean section. Sociodemographic data and Clinical Characteristics were extracted. The samples were randomly divided into a training set and a validation set. Least absolute shrinkage and selection operator (LASSO) regression were used to select variables and construct of VBAC success rate in training set. The validation of the nomogram was performed using the concordance index (C-index), decision curve analysis (DCA), and calibration curves in the validation set. For comparison with published VBAC prediction models, the Grobman’s model was used. Results Among the 708 pregnant women included according to inclusion criteria, 586 (82.77%) patients were successfully for VBAC. In multivariate logistic regression models, Maternal height (OR, 1.11; 95% CI, 1.04 to 1.19), maternal BMI at delivery (OR, 0.89; 95% CI, 0.79 to 1.00), fundal height (OR, 0.71; 95% CI, 0.58 to 0.88), cervix Bishop score (OR, 3.27; 95% CI, 2.49 to 4.45), maternal age at delivery (OR, 0.90; 95% CI, 0.82 to 0.98), gestational age (OR, 0.33; 95% CI, 0.17 to 0.62) and history of vaginal delivery (OR, 2.92; 95% CI, 1.42 to 6.48) were independently associated with successful VBAC. The predictive model was constructed showed better discrimination in the validation series than Grobman’s model (c-index 0.906 VS 0.694, respectively). Decision curve analysis revealed that the new model resulted in a better clinical net benefit than the Grobman’s model. Conclusions The promotion of VBAC is helpful to reduce the cesarean section rate in China. On the basis of following the clinical practice guidelines, the TOLAC prediction model helps to improve the success rate of VBAC and has a potential contribution to the reduction of secondary cesarean section.

scholarly journals Development and Validation of Nomograms for Predicting Prognosis in Intrahepatic Cholangiocarcinoma Patients with Lymph Node Metastasis

2021 ◽  
Author(s):  
xianmao shi ◽  
Xing Sun ◽  
Xin Qin ◽  
Ze Su ◽  
Zhaoshan Fang ◽  
...  
Keyword(s):  
Prognostic Factors ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Intrahepatic Cholangiocarcinoma ◽  
Clinical Decision Making ◽  
Curve Analysis ◽  
Individual Treatment ◽  
Decision Curve Analysis ◽  
Node Metastasis ◽  
Calibration Curves

Abstract Background. Lymph node metastasis (LNM) is one of the common metastatic sites of in advanced-stage intrahepatic cholangiocarcinoma (ICC), and the prognosis of ICC patients with LNM is worse than patients without it. Our study aimed to identify the prognostic factors of ICC patients with LNM, and develop an effective nomogram to quantify the prognosis of ICC patients with LNM.Methods. We retrospectively reviewed the data of ICC patients between 2010 and 2015 in the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate logistic analysis were used to determine the independent predictors for LNM in patients with ICC. Univariate and multivariate Cox analyses were used to identify the independent prognostic factors for ICC patients with LNM. Finally, two nomograms for predicting overall survival (OS) and cause-specific survival (CSS) were established, and the nomogram of predicting OS was evaluated by calibration curves, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA).Results. A total of 1539 patients with ICC were enrolled into our analysis, including 381 cases (24.76%) with LNM at initial diagnosis and 1158 cases (75.24%) without it. The independent risk factors for LNM in newly diagnosed ICC patients are age, T stage, and tumor size. The independent prognostic factors for ICC patients with LNM are grade, chemotherapy, and surgery of primary site. For the prognostic nomogram for OS, the AUCs of 6-, 12-, and 24-months were 0.809, 0.780, and 0.755 in the training set and 0.806, 0.780, and 0.753 in the testing set, respectively. The calibration curves and decision curve analysis indicated the good performance of the nomogram.Conclusions. The individualized nomogram could predict OS of ICC patients with LNM with good performance, which could be served as an effective tool for prognostic evaluation and individual treatment strategies optimization in ICC patients with LNM, and clinical utility may benefit for clinical decision-making.

scholarly journals Development and validation of a preoperative nomogram for predicting patients with impacted ureteral stone: a retrospective analysis

BMC Urology ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Chenglu Wang ◽  
Lu Jin ◽  
Xinyang Zhao ◽  
Boxin Xue ◽  
Min Zheng
Keyword(s):  
Calibration Curve ◽  
Ureteral Stone ◽  
Multivariate Logistic Regression Analysis ◽  
Curve Analysis ◽  
Ureteral Stones ◽  
Ureteral Calculus ◽  
Validation Group ◽  
Net Benefit ◽  
Decision Curve Analysis ◽  
Development And Validation

Abstract Background To develop and validate a practical nomogram for predicting the probability of patients with impacted ureteral stone. Methods Between June 2020 to March 2021, 214 single ureteral stones received ureteroscopy lithotripsy (URSL) were selected in development group. While 82 single ureteral stones received URSL between April 2021 to May 2021 were included in validation group. Independent factors for predicting impacted ureteral stone were screened by univariate and multivariate logistic regression analysis. The relationship between preoperative factors and stone impaction was modeled according to the regression coefficients. Discrimination and calibration were estimated by area under the receiver operating characteristic (AUROC) curve and calibration curve respectively. Clinical usefulness of the nomogram was evaluated by decision curve analysis. Results Age, ipsilateral stone treatment history, hydronephrosis and maximum ureteral wall thickness (UWTmax) at the portion of stone were identified as independent predictors for impacted stone. The AUROC curve of development and validation group were 0.915 and 0.882 respectively. Calibration curve of two groups showed strong concordance between the predicted and actual probabilities. Decision curve analysis showed that the predictive nomogram had a superior net benefit than UWTmax for all examined probabilities. Conclusions We developed and validated an individualized model to predict impacted ureteral stone prior to surgery. Through this prediction model, urologists can select an optimal treatment method and decrease intraoperative and postoperative complications for patients with impacted ureteral calculus.

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