Prognostic value of cystatin C in patients with nasopharyngeal carcinoma: a retrospective study of 1063 patients

Yuan, Jing; Xu, Miao; Li, Jing; Li, Ning; Chen, Li Zhen; Feng, Qi Sheng; Zeng, Yi Xin

doi:10.6061/clinics/2016(06)09

Cited by 14 publications

(12 citation statements)

References 19 publications

(20 reference statements)

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“…The present prognostic model consisting of 13 prognostic variables: age, BMI, HGB, PLT, LMR, CRP, CAR, GLOB, AGR, LDH, Cys-C, ALI, and PNI. All the prognosis variables had been reported to be associated with survival in NPC patients except ALI [24][25][26][27][28][29][30], these were credible evidence supporting our analysis results. The ALI was devised to assess the degree of systemic inflammation in patients with advanced non-small cell lung cancer patients [31].…”

Section: Discussionsupporting

confidence: 89%

A novel prognostic model predicts overall survival in patients with nasopharyngeal carcinoma based on clinical features and blood biomarkers

Lai¹,

Zhang²,

Lv³

et al. 2020

Preprint

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Background To develop and validate a novel prognostic model to estimate overall survival (OS) in nasopharyngeal carcinoma (NPC) patients based on clinical features and blood biomarkers. And assess its incremental value to TNM staging system, clinical treatment, and Epstein-Barr virus DNA (EBV DNA) for individual OS estimation. Methods We retrospectively analyzed 519 consecutive NPC. A prognostic model was generated by using the Lasso-Cox regression model in training cohort (n = 346). Then comparison of predictive accuracy between the novel prognostic model, TNM staging, clinical treatment, and EBV DNA using concordance index (C-index), time-dependent ROC (tdROC), and decision curve analysis (DCA). Subsequently, a nomogram for OS incorporating the prognostic model, TNM staging and clinical treatment was built. Finally, we stratified patients into high- risk and low-risk groups according to the model risk score, and the survival time of these two groups was analyzed using Kaplan–Meier survival plots. All the results were validated in the independent validation cohort (n = 173). Results Using the Lasso-Cox regression, a prognostic model was established consisting of 13 variables with respect to patient prognosis. The C-index, tdROC and DCA all showed the prognostic model had good predictive accuracy and discriminatory power than TNM staging, clinical treatment and EBV DNA in training cohort. Nomogram consisting of the prognostic model, TNM staging, clinical treatment and EBV DNA shown some superior net benefit. According to the model risk score, we split the patients into two subgroups: low- risk (risk score ≤ -1.423) and high-risk (risk score > -1.423). There had significant differences in OS between the two subgroups of patients. In the validation cohort, similar results were obtained. Conclusion The proposed novel prognostic model based on clinical features and serological markers represents a promising signature for estimating OS in NPC patients.

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Section: Discussionsupporting

confidence: 89%

A novel prognostic model predicts overall survival in patients with nasopharyngeal carcinoma based on clinical features and blood biomarkers

Lai¹,

Zhang²,

Lv³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…The present prognostic model consisting of 13 prognostic variables: age, BMI, HGB, PLT, LMR, CRP, CAR, GLOB, AGR, LDH, Cys-C, ALI, and PNI. All the prognosis variables had been reported to be associated with survival in NPC patients except ALI [24][25][26][27][28][29][30], these were credible evidence supporting our analysis results. The ALI was devised to assess the degree of systemic in ammation in patients with advanced non-small cell lung cancer patients [31].…”

Section: Discussionsupporting

confidence: 89%

A novel prognostic model predicts overall survival in patients with nasopharyngeal carcinoma based on clinical features and blood biomarkers

Lai¹,

Zhang²,

Lv³

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: To develop and validate a novel prognostic model to estimate overall survival (OS) in nasopharyngeal carcinoma (NPC) patients based on clinical features and blood biomarkers. And assess its incremental value to TNM staging system, clinical treatment, and Epstein-Barr virus DNA (EBV DNA) for individual OS estimation.Methods: We retrospectively analyzed 519 consecutive NPC. A prognostic model was generated by using the Lasso regression model in training cohort (n = 346). Then comparison of predictive accuracy between the novel prognostic model, TNM staging, clinical treatment, and EBV DNA using concordance index (C-index), time-dependent ROC (tdROC), and decision curve analysis (DCA). Subsequently, a nomogram for OS incorporating the prognostic model, TNM staging and clinical treatment was built. Finally, we stratified patients into high- risk and low-risk groups according to the model risk score, and the survival time of these two groups was analyzed using Kaplan–Meier survival plots. All the results were validated in the independent validation cohort (n = 173).Results: Using the Lasso regression, a prognostic model was established consisting of 13 variables with respect to patient prognosis. The C-index, tdROC and DCA all showed the prognostic model had good predictive accuracy and discriminatory power than TNM staging, clinical treatment and EBV DNA in training cohort. Nomogram consisting of the prognostic model, TNM staging, clinical treatment and EBV DNA shown some superior net benefit. According to the model risk score, we split the patients into two subgroups: low- risk (risk score ≤ -1.423) and high-risk (risk score > -1.423). There had significant differences in OS between the two subgroups of patients. In the validation cohort, similar results were obtained.Conclusions: The proposed novel prognostic model based on clinical features and serological markers represents a promising signature for estimating OS in NPC patients.

show abstract

“…The present prognostic model consisted of 13 prognostic variables: age, BMI, HGB, PLT, LMR, CRP, CAR, GLOB, AGR, LDH, Cys-C, ALI, and PNI. All the prognosis variables were associated with survival in NPC patients except ALI [23][24][25][26][27][28][29]. These were credible evidence supporting our analysis results.…”

Section: Building and Validating A Predictive Nomogramsupporting

confidence: 88%

A novel prognostic model predicts overall survival in patients with nasopharyngeal carcinoma based on clinical features and blood biomarkers

Lai¹,

Zhang²,

Lv³

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: This study aims to develop and validate a novel prognostic model to estimate overall survival (OS) in nasopharyngeal carcinoma (NPC) patients based on clinical features and blood biomarkers. We assessed the model’s incremental value to the TNM staging system, clinical treatment, and Epstein-Barr virus (EBV) DNA copy number for individual OS estimation. Methods: We retrospectively analyzed 519 consecutive patients with NPC. A prognostic model was generated using the Lasso regression model in the training cohort (n= 346). Then we compared the predictive accuracy of the novel prognostic model with TNM staging, clinical treatment, and EBV DNA copy number using concordance index (C-index), time-dependent ROC (tdROC), and decision curve analysis (DCA). Subsequently, we built a nomogram for OS incorporating the prognostic model, TNM staging, and clinical treatment. Finally, we stratified patients into high-risk and low-risk groups according to the model risk score, and the survival time of these two groups was analyzed using Kaplan–Meier survival plots. All results were validated in the independent validation cohort (n = 173). Results: Using the Lasso regression, we established a prognostic model consisting of 13 variables with respect to patient prognosis. The C-index, tdROC and DCA all showed that the prognostic model had good predictive accuracy and discriminatory power in the training cohort than did TNM staging, clinical treatment, and EBV DNA copy number. Nomogram consisting of the prognostic model, TNM staging, clinical treatment, and EBV DNA copy number showed some superior net benefit. Based on the model risk score, we split the patients into two subgroups: low-risk (risk score ≤ -1.423) and high-risk (risk score > -1.423). There were significant differences in OS between the two subgroups of patients. Similar results were observed in the validation cohort.Conclusions: The proposed novel prognostic model based on clinical features and serological markers represents a promising tool for estimating OS in NPC patients.

show abstract

Prognostic value of cystatin C in patients with nasopharyngeal carcinoma: a retrospective study of 1063 patients

Cited by 14 publications

References 19 publications

A novel prognostic model predicts overall survival in patients with nasopharyngeal carcinoma based on clinical features and blood biomarkers

A novel prognostic model predicts overall survival in patients with nasopharyngeal carcinoma based on clinical features and blood biomarkers

A novel prognostic model predicts overall survival in patients with nasopharyngeal carcinoma based on clinical features and blood biomarkers

A novel prognostic model predicts overall survival in patients with nasopharyngeal carcinoma based on clinical features and blood biomarkers

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