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

Abstract

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 ($$\mathrm{n}=1635$$ 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 ($$\ge 1000$$ ≥ 1000 vs.$$<1000$$ < 1000 , HR: 1.92), LAR ($$>6.15$$ > 6.15 vs.$$\le 6.15$$ ≤ 6.15 , HR: 2.05), NLR ($$>4.84$$ > 4.84 vs. $$\le 4.84$$ ≤ 4.84 HR: 1.54), and PLR ($$>206.33$$ > 206.33 vs.$$\le 206.33$$ ≤ 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.

Nomogram Based on Hemoglobin, Albumin, Lymphocyte and Platelet Score to Predict Overall Survival in Patients with T3-4N0-1 Nasopharyngeal Carcinoma

“…Previous researches showed that LAR was significantly related to a relatively short OS and PFS in NPC. 41 , 42 These findings illustrated the adverse effects of nutritional deficiencies on survival of patients with NPC and the importance of nutritional interventions.…”

Survival Prediction Landscape: An In-Depth Systematic Literature Review on Activities, Methods, Tools, Diseases, and Databases

“…A deeper analysis of existing survival predictors reveals that among the 74 studies 54 utilized publicly accessible data from three key databases: the Cancer Genome Atlas Program (TCGA) 17 , NCI Genomic Data Commons (GDC) 18 , and the Gene Expression Omnibus (GEO) 31, 32, 72, 73, 80, 82, 87, 90, 91, 130, 131 . Apart from public databases, there also exist private databases that have been utilized in existing survival prediction studies 66,75,81,112,113,117,118 . However, these private databases often restrict data access and may require extensive research proposals for data retrieval.…”

“…Table 8 provides information about 44 diseases and the corresponding survival prediction algorithms utilized in these diseases. A deeper analysis of Table 8 shows that Cox-PH and lasso Cox-PH models have been extensively utilized for disease specific survival prediction i.e., ASCVD 29,111 , bladder cancer 40,82 , colorectal cancer 74–77 , hepatocellular carcinoma 43,86,87 , ovarian cancer 88–90,103 , lung adenocarcinoma 101 , heart failure 118 , HER2-negative metastatic breast cancer 67 , pancreatic cancer 26,71 , trauma 120 , nasopharyngeal carcinoma 66 , triple-negative breast cancer 68 , lymphoma 85 , breast cancer 40,81,82 , ovarian cancer 88–90,103 , and lower-grade glioma 80 , cardiovascular disease 112,114–117 , invasive ductal carcinoma 70 , liver transplantation 119 , gastric cancer 42 , lung cancer 27 , esophageal squamous cell carcinoma 79 , glioma 69 , and liver cancer 41 . RSF has been employed in 13 studies for 6 diseases namely, ASCVD 29 , bladder cancer 82 , gastrointestinal cancer 30 , cervical cancer 73 , liver transplantation 119 , and heart failure 118 .…”

A Novel Prognostic Model Predicts Outcomes in Non-Metastatic Nasopharyngeal Carcinoma Based on Inflammation, Nutrition, and Coagulation Signature

“…In fact, a few composite hematological indexes have been proposed in some studies; for example, Chen et al 26 and Zhou et al 28 used high-density lipoprotein cholesterol, apolipoprotein A-1, EBV VCA-IgA, and EA-IgA and Xiang et al 27 Nomograms are widely used for cancer prognosis, primarily because of their ability to reduce statistical predictive models into a single numerical estimate of the probability of an event, such as death or recurrence. [17][18][19][20][21][22][23][24][25][26][27][28] In our study, nomogram_CHS was constructed based on MVA results (Node classification, chemotherapy (yes/no), EBV-DNA, and CHS). The nomogram_CHS had better prognostic efficiency than TNM-8 with a higher C-index in the training (0.728 vs 0.646) and validation cohort (0.893 vs 0.622).…”

“…[17][18][19][20][21][22][23][24][25][26][27][28] However, most of these previous studies had some shortcomings, such as (1) including some unconventional biomarkers, [19][20][21][22][23][24][25][26] (2) not considering hematological and clinical measures when constructing the nomogram, 17,18 and (3) not integrating these measures to construct a composite signature to guide the clinical decision-making process. [17][18][19][20][21][22][23][24][25][26] Therefore, this study aimed to evaluate the prognostic and predictive value of a circulating hematological signature (CHS), which was constructed using a panel of indicators of inflammation, nutrition, and coagulation biomarkers. The CHS was then utilized to develop a potential prognostic model for predicting distant metastasis-free survival (DMFS) and OS in patients with non-metastatic NPC to guide individualized treatment.…”