Prognostic role of the advanced lung cancer inflammation index in cancer patients: a meta-analysis

Hua, Xin; Chen, Jing; Wu, Ying; Sha, Jun; Han, Shuhua; Zhu, Xiaoyun

doi:10.1186/s12957-019-1725-2

Cited by 45 publications

(35 citation statements)

References 60 publications

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“…The ALI was devised to assess the degree of systemic inflammation in patients with advanced non-small cell lung cancer patients [31]. Subsequently, ALI had also been demonstrated to be a prognostic factor of survival in some cancers [32][33][34]. The difference between the ALI and other inflammatory markers was that the ALI contained not only indices related to inflammation but also the body mass index (BMI), which was reported to correlate with the sarcopenic status [33].…”

Section: Discussionmentioning

confidence: 99%

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: Discussionmentioning

confidence: 99%

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 ALI was devised to assess the degree of systemic in ammation in patients with advanced non-small cell lung cancer patients (Jafri et al 2013). Subsequently, ALI had also been demonstrated to be a prognostic factor of survival in some cancers (Hua et al 2019;Park et al 2017;Shibutani et al 2019). The difference between the ALI and other in ammatory markers was that the ALI contained not only indices related to in ammation but also the body mass index (BMI), which was reported to correlate with the sarcopenic status (Shibutani et al 2019).…”

Section: Discussionmentioning

confidence: 99%

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-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. 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 ALI was devised to assess the degree of systemic in ammation in patients with advanced non-small cell lung cancer patients [31]. Subsequently, ALI had also been demonstrated to be a prognostic factor of survival in some cancers [32][33][34]. The difference between the ALI and other in ammatory markers was that the ALI contained not only indices related to in ammation but also the body mass index (BMI), which was reported to correlate with the sarcopenic status [33].…”

Section: Discussionmentioning

confidence: 99%

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

Prognostic role of the advanced lung cancer inflammation index in cancer patients: a meta-analysis

Cited by 45 publications

References 60 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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