An overall survival predictive nomogram to identify high-risk patients among locoregionally advanced nasopharyngeal carcinoma: Developed based on the SEER database and validated institutionally

Abstract

Objective:

Locoregionally advanced nasopharyngeal carcinoma (LA-NPC) patients, even at the same stage, have different prognoses. We aim to construct a prognostic nomogram for predicting the overall survival (OS) to identify the high-risk LA-NPC patients.

Materials and methods:

Histologically diagnosed WHO type II and type III LA-NPC patients in the Surveillance, Epidemiology, and End Results (SEER) database were enrolled as the training cohort (n= 421), and LA-NPC patients from Shantou University Medical College Cancer Hospital (SUMCCH) served as the external validation cohort (n= 763). Variables were determined in the training cohort through Cox regression to form a prognostic OS nomogram, which was verified in the validation cohort, and compared with traditional clinical staging using the concordance index (C-index), Kaplan–Meier curves, calibration curves and decision curve analysis (DCA). Patients with scores higher than the specific cut-off value determined by the nomogram were defined as high-risk patients. Subgroup analyses and high-risk group determinants were explored.

Results:

Our nomogram had a higher C-index than the traditional clinical staging method (0.67 vs. 0.60, p<0.001). Good agreement between the nomogram-predicted and actual survival were shown in the calibration curves and DCA, indicating a clinical benefit of the nomogram. High-risk patients identified by our nomogram had worse prognosis than the other groups, with a 5-year overall survival (OS) of 60.4%. Elderly patients at advanced stage and without chemotherapy had a tendency for high risk than the other patients.

Conclusions:

Our OS predictive nomogram for LA-NPC patients is reliable to identify high-risk patients.

1 Introduction

Nasopharyngeal carcinoma (NPC) is a relatively uncommon malignant tumor with extremely unbalanced geographical global distribution (1). There were about 133,354 new cases of NPC in 2020 according to the International Agency for Research on Cancer, of which >70% were in east and southeast Asia (1, 2). Unfortunately, since early nasopharyngeal carcinoma is relatively asymptomatic, at diagnosis, more than 70% of NPC cases are classified as locoregionally advanced disease (LA-NPC: stage III-IVB in editions of TNM staging system before 2016; stage III-IVA after the 2016 edition) (3, 4), which has markedly unsatisfactory prognosis compared to early stage (3). However, even though LA-NPC patients are at the same TNM stages and are treated with identical or similar regimens, they still have diverse survival rates, with approximately 30-40% eventually developing distant metastasis after undergoing radical treatment (5, 6). Due to inherent individual heterogeneity, the traditional TNM staging system has limited accuracy in prognosis prediction. An increasing number of approaches have been used to develop improved predictive models to identify higher-risk and lower-risk LA-NPC populations for the purpose of trimming treatment strategy so as to achieve better outcomes and/or to reduce treatment side-effects. Construction of a nomogram is one of the most popular research directions (7–15). By integrating variables identified to have a significant impact on outcome, nomograms are excellent visualization tools for the estimation of survival rates, but also identify the critical parameters responsible for survival (15). Reported studies examining into some specific subgroups of NPC, including some tumor stage (16–22), patient age (20, 23–26), histologic type (27–29) and even race (30–32), in a wide scope based on the Surveillance, Epidemiology, and End Results (SEER) data, have shown the potential and ability to provide a better reference for clinical decisions (16, 21, 23). However, few studies have specifically focused on LA-NPC patients as a group using the SEER data. In this study, we develop a prognostic nomogram to predict the survival of LA-NPC patients by using the SEER database, and validate the nomogram with the patient data of our institution. Ultimately, we acquired a nomogram capable of better identifying high-risk LA-NPC patients than traditional TNM staging.

2 Materials and methods

2.1 Patient selection

Data of 6738 NPC cases was downloaded from SEER*Stat software, version 8.4.1 and rooted from the Incidence-SEER Research Plus Data, 17 Registries, Nov 2021 Sub (2000–2019). Selection statement: (Site recode ICD-O-3/WHO 2008 = “Nasopharynx”) AND (Behavior code ICD-O-3 = “Malignant”) AND (Diagnostic confirmation = “Microscopically confirmed”) AND (Histology recode - broad groupings = “8050-8089: squamous cell neoplasms”). The inclusion criteria were as follows (1): histopathologically confirmed NPC (2); histological subtypes of World Health Organization (WHO) type II nonkeratinizing squamous cell carcinoma (NKC, 8072, 8073) and WHO type III basaloid squamous cell carcinoma (BSCC, 8083) categorized by the International Classification of Diseases of Oncology, Third Edition (ICD-O-3) (3); stage III, IVA, IVB according to the 7th edition of the American Joint Committee on Cancer (AJCC) staging system; and (4) received radiotherapy. There were 421 eligible LA-NPC cases, which served as our training cohort. An additional set of 763 consecutive LA-NPC patients, treated between 2012-2014 in Shantou University Medical College Cancer Hospital (SUMCCH), were used to establish the external validation cohort.

2.2 Variable collection

Clinical variables were collected in both cohorts: gender, age, pathological subtypes according to the WHO, tumor (T) stage, node (N) stage and clinical stage according to AJCC (7th edition), chemotherapy, overall survival (OS) and survival status. For the external validation cohort, we collected clinical information of patients with LA-NPC (7th AJCC stage III-IVB) attending SUMCCH, who were then restaged using magnetic resonance imaging, according to the 8th edition of the AJCC staging system (7^th edition: III-IVB = 8^th edition: III-IVA). After re-staging, 758 patients with LA-NPC were selected for further analysis. Clinical factors in the two datasets were compared using the independent samples t-test for continuous variables and chi squared test for categorical variables. The continuous variable age was converted into an ordinal categorical variable using X-Tile software (Version 3.6.1, Yale University) for determining the optimal cut-off value (33).

2.3 Model development

The univariate Cox regression algorithm was used for the training cohort to analyze clinicopathological predictors of OS, and predictors with p-values ≤0.15 were selected for multivariate Cox analysis (enter selection). Finally, our nomogram for predicting OS at the 1-, 3- and 5-year stage was constructed using the function nomogram(), by which each patient’s corresponding total point was calculated.

2.4 Model evaluation & comparison

Internal 1,000-times bootstrap resampling was performed to evaluate the performance and optimism of the developed model according to the Transport Reporting of a Multivariable Prediction Model for Individual Prognosis of Diagnosis (TRIPOD) statement (34). The OS prediction model was verified using the external validation cohort. In the validation cohort, the model was also tested using the 8th edition of the AJCC/UICC staging system for staging patients. The discrimination, calibration and clinical effectiveness of the prediction model were metrices to determine the performance of the model (15) and was compared with the traditional TNM staging system. The discriminative ability of the predictive model was measured by the bias-adjusted C-Index (“rms” package) with its 95% confidence interval (CI) between which the ‘‘compareC” package and the function of cindex() in the “pec” package were used to quantify and visualize the differences (15). Patients were sorted by values of their corresponding total points, after which we used the function of quantile() to produce 2 cut-off values and stratify patients into high-risk, intermediate-risk and low-risk groups. The discriminative ability of the predictive model was measured by the adjusted C-index as described above (15). Kaplan–Meier survival curves showing the different risk subgroups were compared using a log-rank test. To visualize the OS of actual occurrence versus nomogram prediction, the function of calibrate() was used to construct the calibration plot (15). Decision curve analysis (DCA), in the ggDCA package, was used to assess the clinical utility of the predictive model (15, 35).

2.5 Subgroup analysis

The nomogram was validated for the subgroups of stage III and stage IV patients from the external cohort. Subtypes of stage III and stage IV were regarded as predictive factors. C-indices and risk stratification ability were compared between the nomogram and the subtypes.

2.6 Identification of prognostic factors for the high-risk group

To make the factors easier to analyze at a glance, we made scatter plots for visual representation of the distribution of the high-risk patients among different categories. We performed multivariate logistic regression analysis to investigate the independent influencing factors of high-risk patients in the validation cohort and Dominance analysis was then used to evaluate the relative importance of independent predictors.

2.7 Statistical analysis

R software (version 4.2.0) and SPSS (version 20.0) were used for the statistical analysis. We regarded metrices with p-values <0.05 (two sided) as having statistical significance. An overview of the research design is shown in Figure 1.

Figure 1

3 Results

3.1 Patient baseline characteristics

The median follow-up for the SEER dataset was 59 months (range 1-119) and 169 deaths (40.1%) were observed. The median follow-up for the SUMCCH patients was 89 months (range: 3-117) and 205 deaths (26.9%) were observed. Patient demographic and clinical characteristics of the two datasets are illustrated in Table 1. All characteristics except gender and chemotherapy were significantly different between the two datasets.

3.2 Univariate & multivariate analysis of the training cohort

Univariate Cox analysis indicated that clinical variables age, T stage, N stage and chemotherapy were associated with OS (p ≤ 0.10). These significant clinical characteristics were included in multivariate Cox analysis, which further confirmed them to be critical factors influencing OS (Table 2). Older patients and those with more advanced T stage had poorer prognosis, whereas patients who underwent chemotherapy seemed more likely to have better outcomes.

3.3 Nomogram details

The above prognostic variables were included in our nomogram for OS based on the multivariate Cox analysis (Figure 2). The probability of the 3- and 5-year OS for an individual could be predicted through this nomogram. In the nomogram for each patient, each parameter axis has a value, and a straight line can be drawn upward to the corresponding points axis. After adding up the scores, the sum is marked on the total points axis and a line drawn downward to the survival axis gives the patient’s predicted OS. For example, a 60-year-old (23.5 points) LA-NPC patient at TNM stage T3 (4.2 points), N2 (20.8 points) M0, who received radiotherapy and chemotherapy (0 points) is assigned 62.3 total points and the estimated probability of 3- and 5-year OS is 79.0% and 72.0%, respectively, according to the nomogram.

Figure 2

3.4 Nomogram discrimination

In the training cohort, the bias-corrected C-indices for the nomogram and clinical stage were 0.63 (95%CI: 0.61-0.69) and 0.57 (95%CI: 0.53-0.61), respectively. In the external validation cohort, based on the 7^th edition of the AJCC staging system, the C-index of the nomogram was still higher than that of the clinical stage, being 0.67 (95%CI: 0.64-0.71) versus 0.60 (95%CI: 0.57-0.64), respectively (Figure 3). After restaging based on the 8^th edition, the C-indices of the nomogram and clinical stage were 0.68 (95%CI: 0.64-0.71) and 0.60 (95%CI: 0.57-0.64), respectively, almost identical to the values based on the 7^th edition. The C-indices of the nomogram are always significantly higher compared to those of the clinical stage in three cohorts with p-values all <0.001. Furthermore, the p-values showed no significant difference between using the 7^th and the 8^th editions (0.94 for nomograms and 0.82 for the clinical stage). In the training cohort, we stratified patients into three groups on the basis of the cut-off values of the risk scores determined by the multivariate Cox analysis: a low-risk group (≤48.5, n=148), an intermediate-risk group (> 48.5 and < 93.5, n=136), and a high-risk group (≥93.5, n=137). In the training cohort, we also observed significant differences for OS rates among the low-risk, intermediate-risk and high-risk groups, with the 5-year OS rates being 76.0%, 69.2%, and 46.6%, respectively. Likewise, in the validation cohort, there were significant differences among the three risk groups, with the 5-year OS rates being 87.5% (n=433), 74.3% (n=188) and 60.4% (n=142), respectively, based on the 7^th version of the TNM staging system, or 87.7% (n=447), 72.1% (n=173) and 60.7% (n=138), respectively, after restaging based on the 8^th edition. The 5-year OS of the high-risk patients was significantly worse than that of the intermediate or low-risk group. Kaplan–Meier curves between the different groups showed greater separation with the nomogram than those based on clinical stages in both the training and validation cohorts (Figure 4). The high-risk group identified by the nomogram had a median survival of 94 months in both validation cohorts, whereas those groups classified by the clinical stage did not reach a median survival. In summary, the nomogram can always separate the high-risk group survival curve away from the others with lower median survival time and a significant p-value while the clinical stage cannot, which means the nomogram had greater ability to discriminate the prognosis outcomes for the low-risk, intermediate-risk and high-risk subgroups, as compared to the traditional staging system.

Figure 3

Figure 4

3.5 Nomogram calibration and DCA

The calibration curve in Figure 5 nearly coincided with the actual survival, indicating that the model is well calibrated. The consequences of DCA illustrate that our nomogram is of higher clinical net benefit than traditional clinical staging for forecasting OS.

Figure 5

3.6 Subgroup analysis

As shown in the Kaplan–Meier curves, the nomogram was easily able to identify the high-risk LA-NPC patients in stage III and stage IV, with 5-year OS rates 67.7% and 56.8%, respectively. As shown in the Figure 6, the high-risk group survival curves identified by the nomogram are always separated from the others with significantly lower median survival time (p-value < 0.01) while the curves of the subgroups crossed, failing to stratify the risk of the patients. Therefore, our nomogram had better discriminative ability than subgroups.

Figure 6

3.7 Independent factors for the high-risk group

Scatter plots in Figure 7 give obvious visual cues about how the high-risk patients and non-high-risk patients are clustered or varies in different categories. Elderly patients at advanced stages and without chemotherapy were more prone to have high risk than the other patients. Patients at or above a certain age (≥64) were uniformly identified as high-risk patients and this complete separation age leads to a warning when we were performing the multivariate logistic regression analysis in R software. Age was the most crucial determinant for high risk, and dominance analysis demonstrated that T stage was second in importance (average contribution, 0.52), followed by chemotherapy (0.10) and N stage (0.04) (Figure 7).

Figure 7

4 Discussion

To best of our knowledge, this is the first OS predictive nomogram for LA-NPC patients built from the SEER database and validated with a large sample institutionally. In this retrospective cohort study, we developed a novel prognostic tool based on clinical risk factors from the SEER database and showed an improved the ability to predict OS in LA-NPC using external data in our own center. Results show that our model can categorize patients into high-risk, intermediate-risk and low-risk groups with significantly different OS, which the current TNM staging system cannot do. Furthermore, our classifier performed significantly better than the subgroups at risk stratification. Our verified nomogram is a simple and accurate method for predicting OS in LA-NPC patients and provides new options for individualized treatment which complement the traditional and time-honored TNM staging system. To those high-risk patients identified by the nomogram, their poorer outcomes challenge the consistent treatment put forward by the NCCN guidelines, and the treatment intensity deserves to be carefully weighed.

SEER is an authoritative source for cancer statistics in the United States where the incidence is low but WHO type I is observed as the predominant histological type. However, type I is so uncommon in endemic areas. In the south China, patients histologically diagnosed with WHO type II and type III NPC accounts for greater than 95% of all NPC (1, 36, 37), and are radioresponsive and associated with Epstein-Barr virus (EBV) infection, and have better prognosis (1, 38). Therefore, we excluded the type II during the model construction, making it more generalizable to NPC patients in epidemic areas, including those from our institution.

Apart from the heterogeneous prognoses among the locoregionally advanced NPC, the current National Comprehensive Cancer Network (NCCN) Guideline recommends the same treatment for the different patient groups (CCRT + AC and IC + CCRT (2A) and CCRT (2B)) (39) for all stage II-IVA NPC. This catch−all strategy treats LA-NPC patients equally without discrimination and limits the clinicians’ access to prescribe personalized treatments. Recently, CSCO and ASCO issued joint guidelines for evidence-based stratified recommendations, in which de-intensified treatment was recommended for stage II and T3N0 patients (RT alone for T2N0, CCRT for T2N1 and T3N0) (40). A relatively lower risk has been reported in patients with T3N0 NPC compared to other LA-NPC patients due to lack of nodal involvement (41). N0 patients have therefore often been excluded from clinical trials and other studies when it came to locally regional disease (13, 42–44). However, as shown by our Kaplan–Meier survival curves (Figure 6), patients with T3-4N0 did not display better outcomes than the other groups, which could be due to the small number of the patients. Additional research is needed to analyze the prognosis of negative nodal metastasis LA-NPC patients.

Our data shows that in our nomogram age is the most dominant prognostic factor. In several previous studies based on the SEER database, the survival of older NPC patients has been reported to be inferior to that of the younger patients (24, 26), which was also verified by Yan et al. in their own institution (23). Consistent with the findings from SEER, data from the China National Mortality Surveillance System (NMSS) demonstrated that mortality and years of life lost (YLL) due to NPC increases with age (45). Age always appears as one of the significant parameters in the published NPC nomograms combined with other variables (7, 8, 12, 14, 46). Advanced age is a major impediment for oncologists to prescribe the guideline-recommended treatment. According a population-based analysis of 3880 NPC patients, younger patients were more likely to receive chemotherapy (26). On the other hand, elderly NPC patients have poorer tolerance to radiotherapy (47). Comorbidity has been found to be associated with increased overall mortality in elderly cancer patients, which may result from the delay of diagnosis, the ineligibility for radiotherapy and chemotherapy (48, 49). Therefore, the management of comorbidities must be taken into account in clinics.

Radiotherapy combined with chemotherapy has been the standard treatment for LA-NPC patients since the landmark Intergroup 0099 randomized trial (50, 51). In the era of intensity-modulated radiotherapy (IMRT), distant metastasis instead of locoregional recurrence has become the main cause of treatment failure (52). Consistent with our result, the use of chemotherapy was a protective factor for LA-NPC patients and patients without chemotherapy were more likely to be in the high-risk category. Xiang et al. analyzed 1452 patients from the SEER database and verified that chemotherapy offers a significant substantial survival benefit for stage III NPC patients (18). Luo et al. explored the efficacy of chemotherapy of 729 patients from the SEER database and indicated that chemoradiotherapy could improve the survival of IVA patients (16). However, research into the optimum chemotherapy strategies for LA-NPC patients in regard to sequence, dose, and cycle are still under study.

This study has some limitations. On one hand, we ensured the consistency of the histological types of the cohorts at the expense of using a larger sample size from SEER. On the other hand, due to the inevitable limitations of the SEER database, we could not obtain some important information, such as plasma Epstein-Barr virus (EBV) DNA, chemotherapy regimens, radiotherapy details, and comorbidity status. All in all, our nomogram remains to be fully verified in more data centers and it can be improved with more information based on a larger sample size or our center data for LA-NPC prognosis prediction. In this way, we can target high-risk patients more precisely.

In conclusion, we built an OS predictive nomogram that can help clinicians better predict the prognosis of LA-NPC patients, compared with the traditional tumor-node-metastasis staging method, and identify those at high risk for more personalized treatment.

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