scholarly journals Score for the Overall Survival Probability Scores of Fibrosarcoma Patients after Surgery: A Novel Nomogram-Based Risk Assessment System

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yuyuan Chen ◽  
Changxing Chi ◽  
Dedian Chen ◽  
Sanjun Chen ◽  
Binbin Yang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Overall Survival ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Curve Analysis ◽  
Training Set ◽  
Characteristic Curves ◽  
Decision Curve Analysis ◽  
Receiver Operating Characteristic Curves ◽  
Independent Risk Factors

Background. The primary purpose of this study was to determine the risk factors affecting overall survival (OS) in patients with fibrosarcoma after surgery and to develop a prognostic nomogram in these patients. Methods. Data were collected from the Surveillance, Epidemiology, and End Results database on 439 postoperative patients with fibrosarcoma who underwent surgical resection from 2004 to 2015. Independent risk factors were identified by performing Cox regression analysis on the training set, and based on this, a prognostic nomogram was created. The accuracy of the prognostic model in terms of survival was demonstrated by the area under the curve (AUC) of the receiver operating characteristic curves. In addition, the prediction consistency and clinical value of the nomogram were validated by calibration curves and decision curve analysis. Results. All included patients were divided into a training set (n = 308) and a validation set (n = 131). Based on univariate and multivariate analyses, we determined that age, race, grade, and historic stage were independent risk factors for overall survival after surgery in patients with fibrosarcoma. The AUC of the receiver operating characteristic curves demonstrated the high predictive accuracy of the prognostic nomogram, while the decision curve analysis revealed the high clinical application of the model. The calibration curves showed good agreement between predicted and observed survival rates. Conclusion. We developed a new nomogram to estimate 1-year, 3-year, and 5-year OS based on the independent risk factors. The model has good discriminatory performance and calibration ability for predicting the prognosis of patients with fibrosarcoma after surgery.

scholarly journals SUMOylation Regulator-Related Molecules Can Be Used as Prognostic Biomarkers for Glioblastoma

2021 ◽  
Vol 9 ◽  
Author(s):  
Xiaozhi Li ◽  
Yutong Meng
Keyword(s):  
Overall Survival ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Enrichment Analysis ◽  
Single Nucleotide Variant ◽  
Survival Curves ◽  
Single Nucleotide ◽  
Characteristic Curves ◽  
Receiver Operating Characteristic Curves ◽  
The Relationship

IntroductionSUMOylation is one of the post-translational modifications. The relationship between the expression of SUMOylation regulators and the prognosis of glioblastoma is not quite clear.Materials and MethodsThe single nucleotide variant data, the transcriptome data, and survival information were acquired from The Cancer Genome Atlas, Gene Expression Omnibus, and cBioportal database. Wilcoxon test was used to analyze differentially expressed genes between glioblastoma and normal brain tissues. Gene set enrichment analysis was conducted to find the possible functions. One risk scoring model was built by the least absolute shrinkage and selection operator Cox regression. Kaplain–Meier survival curves and receiver operating characteristic curves were applied to evaluate the effectiveness of the model in predicting the prognosis of glioblastoma.ResultsSingle-nucleotide variant mutations were found in SENP7, SENP3, SENP5, PIAS3, RANBP2, USPL1, SENP1, PIAS2, SENP2, and PIAS1. Moreover, UBE2I, UBA2, PIAS3, and SENP1 were highly expressed in glioblastoma, whereas PIAS1, RANBP2, SENP5, and SENP2 were downregulated in glioblastoma. Functional enrichment analysis showed that the SUMOylation regulators of glioblastoma might involve cell cycle, DNA replication, and other functions. A prognostic model of glioblastoma was constructed based on SUMOylation regulator-related molecules (ATF7IP, CCNB1IP1, and LBH). Kaplain–Meier survival curves and receiver operating characteristic curves showed that the model had a strong ability to predict the overall survival of glioblastoma.ConclusionThis study analyzed the expression of 15 SUMOylation regulators in glioblastoma. The risk assessment model was constructed based on the SUMOylation regulator-related genes, which had a strong predictive ability for the overall survival of patients with glioblastoma. It might provide targets for the study of the relationship between SUMOylation and glioblastoma.

Comparative Assessment of Clinical Benefit Using the ESMO-Magnitude of Clinical Benefit Scale Version 1.1 and the ASCO Value Framework Net Health Benefit Score

2019 ◽  
Vol 37 (4) ◽  
pp. 336-349 ◽  
Author(s):  
Nathan I. Cherny ◽  
Elisabeth G.E. de Vries ◽  
Urania Dafni ◽  
Elizabeth Garrett-Mayer ◽  
Shannon E. McKernin ◽  
...  
Keyword(s):  
Overall Survival ◽  
Receiver Operating Characteristic ◽  
Clinical Benefit ◽  
Operating Characteristic ◽  
Health Benefit ◽  
Progression Free Survival ◽  
Characteristic Curves ◽  
Free Survival ◽  
Receiver Operating Characteristic Curves ◽  
Receiver Operating

PURPOSE To better understand the European Society for Medical Oncology-Magnitude of Clinical Benefit Scale version 1.1 (ESMO-MCBS v1.1) and the ASCO Value Framework Net Health Benefit score version 2 (ASCO-NHB v2), ESMO and ASCO collaborated to evaluate the concordance between the frameworks when used to assess clinical benefit attributable to new therapies. METHODS The 102 randomized controlled trials in the noncurative setting already evaluated in the field testing of ESMO-MCBS v1.1 were scored using ASCO-NHB v2 by its developers. Measures of agreement between the frameworks were calculated and receiver operating characteristic curves used to define thresholds for the ASCO-NHB v2 corresponding to ESMO-MCBS v1.1 categories. Studies with discordant scoring were identified and evaluated to understand the reasons for discordance. RESULTS The correlation of the 102 pairs of scores for studies in the noncurative setting is estimated to be 0.68 (Spearman’s rank correlation coefficient; overall survival, 0.71; progression-free survival, 0.67). Receiver operating characteristic curves identified thresholds for ASCO-NHB v2 for facilitating comparisons with ESMO-MCBS v1.1 categories. After applying pragmatic threshold scores of 40 or less (ASCO-NHB v2) and 2 or less (ESMO-MCBS v1.1) for low benefit and 45 or greater (ASCO-NHB v2) and 4 to 5 (ESMO-MCBS v1.1) for substantial benefit, 37 discordant studies were identified. Major factors that contributed to discordance were different approaches to evaluation of relative and absolute gain for overall survival and progression-free survival, crediting tail of the curve gains, and assessing toxicity. CONCLUSION The agreement between the frameworks was higher than observed in other studies that sought to compare them. The factors that contributed to discordant scores suggest potential approaches to improve convergence between the scales.

A conditional approach for the receiver operating characteristic curve construction to evaluate diagnostic test performance in a family-matched case–control design

2021 ◽  
pp. 096228022199595
Author(s):  
Yalda Zarnegarnia ◽  
Shari Messinger
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Case Control ◽  
Paired Data ◽  
Characteristic Curves ◽  
Receiver Operating Characteristic Curves ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Receiver operating characteristic curves are widely used in medical research to illustrate biomarker performance in binary classification, particularly with respect to disease or health status. Study designs that include related subjects, such as siblings, usually have common environmental or genetic factors giving rise to correlated biomarker data. The design could be used to improve detection of biomarkers informative of increased risk, allowing initiation of treatment to stop or slow disease progression. Available methods for receiver operating characteristic construction do not take advantage of correlation inherent in this design to improve biomarker performance. This paper will briefly review some developed methods for receiver operating characteristic curve estimation in settings with correlated data from case–control designs and will discuss the limitations of current methods for analyzing correlated familial paired data. An alternative approach using conditional receiver operating characteristic curves will be demonstrated. The proposed approach will use information about correlation among biomarker values, producing conditional receiver operating characteristic curves that evaluate the ability of a biomarker to discriminate between affected and unaffected subjects in a familial paired design.

Development and Validation of a Nomogram for Renal Involvement in Primary Sjögren Syndrome Patients: A Retrospective Analysis

2021 ◽  
Author(s):  
Xinshi Huang ◽  
Xiaobing Wang ◽  
Dinglai Yu
Keyword(s):  
Risk Factors ◽  
Prediction Model ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Renal Involvement ◽  
Calibration Plot ◽  
Receiver Operating Characteristic Curves ◽  
Logistic Analysis ◽  
Validation Set ◽  
Multivariate Logistic Analysis

Abstract Objective To establish and validate a nomogram for individualized prediction of renal involvement in pSS patients. Methods A total of 1293 patients with pSS from the First Affiliated Hospital of Wenzhou Medical University between January 2008 to January 2020 were recruited and further analyzed retrospectively. The patients were randomly divided into a development set (70%, n = 910) and a validation set (30%, n = 383). The univariable and multivariate logistic regression were performed to analyze the risk factors of renal involvement in pSS. Based on the regression β coefficients derived from multivariate logistic analysis, an individualized nomogram prediction model was developed. The prediction model of discrimination and calibration was evaluated with the area under the receiver operating characteristic curves and Calibration plot. Results Multivariate logistic analysis showed that hypertension, anemia, albumin, uric acid, anti-Ro52, hematuria and Chisholm-Mason grade were independent risk factors of renal involvement in pSS. The area under the receiver operating characteristic curves were 0.797 and 0.750 respectively in development set and validation set, indicating the nomogram had a good discrimination capacity. The Calibration plot showed nomogram had a strong concordance performance between the prediction probability and the actual probability. Conclusion The individualized nomogram for pSS patients those who had renal involvement could be used by clinicians to predict the risk of pSS patients developing into renal involvement and improve early screening and intervention.

Sign in / Sign up

Export Citation Format

Share Document