Establishment and Validation of Nomogram Based on Combination of Pretreatment C-Reactive Protein/Albumin Ratio-EBV DNA Grade in Nasopharyngeal Carcinoma Patients Who Received Concurrent Chemoradiotherapy

C-reactive protein to albumin ratio predict responses to programmed cell death-1 inhibitors in hepatocellular carcinoma patients

BACKGROUND

Over the years, programmed cell death-1 (PD-1) inhibitors have been routinely used for hepatocellular carcinoma (HCC) treatment and yielded improved survival outcomes. Nonetheless, significant heterogeneity surrounds the outcomes of most studies. Therefore, it is critical to search for biomarkers that predict the efficacy of PD-1 inhibitors in patients with HCC.

AIM

To investigate the role of the C-reactive protein to albumin ratio (CAR) in evaluating the efficacy of PD-1 inhibitors for HCC.

METHODS

The clinical data of 160 patients with HCC treated with PD-1 inhibitors from January 2018 to November 2022 at the First Affiliated Hospital of Guangxi Medical University were retrospectively analyzed.

RESULTS

The optimal cut-off value for CAR based on progression-free survival (PFS) was determined to be 1.20 using x-tile software. Cox proportional risk model was used to determine the factors affecting prognosis. Eastern Cooperative Oncology Group performance status [hazard ratio (HR) = 1.754, 95% confidence interval (95%CI) = 1.045-2.944, P = 0.033], CAR (HR = 2.118, 95%CI = 1.057-4.243, P = 0.034) and tumor number (HR = 2.932, 95%CI = 1.246-6.897, P = 0.014) were independent prognostic factors for overall survival. CAR (HR = 2.730, 95%CI = 1.502-4.961, P = 0.001), tumor number (HR = 1.584, 95%CI = 1.003-2.500, P = 0.048) and neutrophil to lymphocyte ratio (HR = 1.120, 95%CI = 1.022-1.228, P = 0.015) were independent prognostic factors for PFS. Two nomograms were constructed based on independent prognostic factors. The C-index index and calibration plots confirmed that the nomogram is a reliable risk prediction tool. The ROC curve and decision curve analysis confirmed that the nomogram has a good predictive effect as well as a net clinical benefit.

CONCLUSION

Overall, we reveal that the CAR is a potential predictor of short- and long-term prognosis in patients with HCC treated with PD-1 inhibitors. If further verified, CAR-based nomogram may increase the number of markers that predict individualized prognosis.

Nomogram incorporating Epstein-Barr virus DNA and a novel immune-nutritional marker for survival prediction in nasopharyngeal carcinoma

BACKGROUND

Since Immune response, nutritional status and Epstein-Barr Virus (EBV) DNA status have been confirmed to be relevant to the prognosis of patients with nasopharyngeal carcinoma (NPC), we believe that the combination of these factors is of great value for improving the predictive ability. LA (lymphocytes × albumin), a novel indicator, had not been studied yet in NPC. We combined it with EBV DNA and used nomograms to increase the accuracy of prognosis.

METHODS

A total of 688 NPC patients were retrospectively reviewed and further divided into training and validation cohort randomly. Kaplan-Meier analyses were used to to distinguish the different survival outcomes. Multivariate Cox analyses were used to identify the independent prognostic factors for progression-free survival (PFS) and overall survival (OS). Calibration curves, concordance indexes (C-indexes) and decision curve analyses (DCA) were used to evaluate the nomograms' predictive value.

RESULTS

Patients with low LA and positive EBV DNA correlated with poorer 5-year PFS and OS (all P < 0.005). In multivariate Cox analyses, LA and EBV DNA were both confirmed to be independent prognostic factors for PFS and OS (all P < 0.05). Prognostic nomograms incorporating LA and EBV DNA achieved ideal C-indexes of 0.69 (95% CI: 0.65-0.73) and 0.77 (95% CI: 0.71-0.82) in the prediction of PFS and OS. Otherwise, the calibration curves and DCA curves also revealed that our nomograms had pleasant predictive power.

CONCLUSIONS

LA is a novel and powerful biomarker for predicting clinical outcomes in NPC. Our nomograms based on LA and EBV DNA can predict individual prognosis more accurately and effectively.

Prognostic significance of the novel nutrition-inflammation marker of lymphocyte-C-reactive protein ratio in patients with nasopharyngeal carcinoma receiving concurrent chemoradiotherapy

Background

Recent studies indicate that the novel lymphocyte-C-reactive protein ratio (LCR) is strongly associated with the survival of various tumors, but its prognostic value in nasopharyngeal carcinoma (NPC) is understudied. This study aimed to explore the relationship between LCR and overall survival (OS) in NPC and develop a predictive model.

Methods

A total of 841 NPC patients who received concurrent chemoradiotherapy (CCRT) between January 2010 and December 2014 were retrospectively enrolled and randomly divided into a training cohort (n = 589) and a validation cohort (n = 252), and 122 patients between January 2015 and March 2015 were included as an additional validation cohort. Univariate and multivariate Cox analyses were performed to identify variables associated with OS and construct a predictive nomogram. The predictive accuracy of the nomogram was evaluated and independently validated.

Results

The LCR score differentiated NPC patients into two groups with distinct prognoses (HR = 0.53; 95% CI: 0.32-0.89, P = 0.014). Multivariate analysis showed that age, T stage, N stage, EBV-DNA status, and LCR score were independently associated with OS, and a predictive nomogram was developed. The nomogram had a good performance for the prediction of OS [C-index = 0.770 (95% CI: 0.675-0.864)]. and outperformed the traditional staging system [C-index = 0.589 (95% CI: 0.385-0.792)]. The results were internally and additionally validated using independent cohorts.

Conclusion

The pretreatment LCR could independently predict the overall survival in NPC patients. A novel LCR-based prognostic model of an easy-to-use nomogram was established, and it outperformed the conventional staging system in terms of predictive power. Further external verification remains necessary.

Development and validation of a risk prediction model for overall survival in patients with nasopharyngeal carcinoma: a prospective cohort study in China

Objective

Nasopharyngeal carcinoma (NPC) is prevailing in Southern China, characterized by distinct geographical distribution. Aimed to predict the overall survival (OS) of patients with nasopharyngeal carcinoma, this study developed and validated nomograms considering demographic variables, hematological biomarkers, and oncogenic pathogens in China.

Methods

The clinicopathological and follow-up data of the nasopharyngeal carcinoma patients obtained from a prospective longitudinal cohort study in the Chongqing University Cancer Hospital between Jan 1, 2017 and Dec 31, 2019 (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{n}=1635$$\end{document}n=1635). Cox regression model was used to tested the significance of all available variables as prognostic factors of OS. And independent prognostic factors were identified based on multivariable analysis to model nomogram. Concordance index (C-index), area under the receiver operating characteristic (AUC), calibration curve, and decision curve analysis (DCA) were measured to assess the model performance of nomogram.

Results

Data was randomly divided into a training cohort (1227 observers, about 70% of data) and a validation group (408 observers, about 30% of data). At multivariable analysis, the following were independent predictors of OS in NPC patients and entered into the nomogram: age (hazard ratio [HR]: 1.03), stage (stage IV vs. stage I–II, HR: 4.54), radiotherapy (Yes vs. No, HR: 0.43), EBV (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ge 1000$$\end{document}≥1000 vs.\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$<1000$$\end{document}<1000, HR: 1.92), LAR (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$>6.15$$\end{document}>6.15 vs.\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\le 6.15$$\end{document}≤6.15, HR: 2.05), NLR (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$>4.84$$\end{document}>4.84 vs. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\le 4.84$$\end{document}≤4.84 HR: 1.54), and PLR (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$>206.33$$\end{document}>206.33 vs.\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\le 206.33$$\end{document}≤206.33, HR: 1.79). The C-indexes for training cohort at 1-, 3- and 5-year were 0.73, 0.83, 0.80, respectively, in the validation cohort, the C-indexes were 0.74 (95% CI 0.63–0.86), 0.80 (95% CI 0.73–0.87), and 0.77 (95% CI 0.67–0.86), respectively. The calibration curve demonstrated that favorable agreement between the predictions of the nomograms and the actual observations in the training and validation cohorts. In addition, the decision curve analysis proved that the nomogram model had the highest overall net benefit.

Conclusion

A new prognostic model to predict OS of patients with NPC was developed. This can offer clinicians treatment making and patient counseling. Furthermore, the nomogram was deployed into a website server for use.