
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)13044-7
10.1016/j.heliyon.2024.e37013
e37013
Research Article
Prognostic factors and overall survival in pelvic Ewing's sarcoma and chordoma: A comparative SEER database analysis
Tang Wanyun a
Li Runzhuo b1
Lai Xiaoying a
Yu Xiaohan c
He Renjian herenjian1978@163.com
a1⁎
a Department of Orthopedics, Zigong First People's Hospital, Zigong, China
b Department of Digestion,The First People's Hospital of Yibin, Yibin, China
c Department of General Surgery, Dandong Central Hospital, China Medical University, Dandong, China
⁎ Corresponding author. herenjian1978@163.com
1 These authors have contributed equally to this work and are Co-Corresponding Authors.

28 8 2024
15 9 2024
28 8 2024
10 17 e370134 4 2024
24 8 2024
26 8 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Background

This study aimed to develop and validate nomograms to predict overall survival (OS) for pelvic Ewing's sarcoma (EWS) and chordoma, identify prognostic factors, and compare outcomes between the two conditions.

Methods

We identified patients diagnosed with pelvic EWS or chordoma from the SEER database (2001–2019). Independent risk factors were identified using univariate and multivariate Cox regression analyses, and these factors were used to construct nomograms predicting 3-, 5-, and 10-year OS. Validation methods included AUC, calibration plots, C-index, and decision curve analysis (DCA). Kaplan-Meier curves and log-rank tests compared survival differences between low- and high-risk groups.

Results

The study included 1175 patients (EWS: 611, chordoma: 564). Both groups were randomly divided into training (70 %) and validation (30 %) cohorts. OS was significantly higher for chordoma. Multivariate analysis showed year of diagnosis, income, stage, and surgery were significant for EWS survival, while age, time to treatment, stage, and surgery were significant for chordoma survival. Validation showed the nomograms had strong predictive performance and clinical utility.

Conclusions

The nomograms reliably predict overall survival (OS) in pelvic EWS and chordoma, helping to identify high-risk patients early and guide preventive measures. The study also found that survival rates are significantly higher for chordoma, highlighting different prognostic profiles between EWS and chordoma.

Keywords

Pelvic Ewing's sarcoma
Pelvic chordoma
Prediction model
Nomogram
SEER database
==== Body
pmc1 Introduction

Ewing's sarcoma (EWS) and chordoma are rare primary malignant bone tumors [1,2]. EWS has an estimated incidence of 2.9 per million per year, occurring predominantly in children and adolescents, while chordoma occurs in 0.08 per 100,000, typically in older adults [3,4]. Both demonstrate a predilection for the axial skeleton and pelvic localization [5,6]. With multimodal therapy incorporating chemotherapy, radiation, and surgery, the 5-year overall survival (OS) rate for non-metastatic EWS now exceeds 70 %, but remains only 30–40 % when metastases are present at diagnosis [7]. Chordomas have a relatively better prognosis, with reported 5-year OS ranging from 80 to 90 % % for sacral tumors and only 30–50 % for recurrent disease [8].

Several prognostic factors have been reported to significantly influence clinical outcomes in EWS and chordoma independently, including age, tumor size, surgical resection extent, radiation response, and histological grade [7,9]. However, due to the rarity of these malignancies, most studies were small, retrospective case series examining.

Pelvic EWS and chordoma share several similarities, including anatomical location, locally aggressive behavior, and challenging surgical margins [[10], [11], [12], [13]]. However, they differ in patient demographics, metastatic potential, and sensitivity to chemotherapy/radiation. Understanding how prognostic factors and survival may differ between pelvic EWS and chordoma can better guide tailored treatment decision making for these rare tumors.

In this study, we utilize the Surveillance, Epidemiology, and End Results (SEER) database to conduct a comparative analysis of prognostic factors and overall survival between pelvic EWS and chordoma. Findings aim to delineate clinical and demographic differences between these rare pelvic malignancies to inform management.

2 Background

EWS is a highly aggressive tumor primarily affecting children and young adults, characterized by its origin in bone or soft tissue [14]. It is associated with specific chromosomal translocations, most commonly t(11; 22)(q24; q12), which results in the EWSR1-FLI1 fusion gene [15]. Chordoma, on the other hand, is a slow-growing, malignant bone tumor thought to arise from remnants of the notochord, with a predilection for the axial skeleton, including the sacrococcygeal region [16].

The pelvis is a common site for both EWS and chordoma, contributing to significant morbidity and challenging therapeutic management due to the complex anatomy and the need for extensive surgical resection [17,18]. Prognosis for patients with pelvic EWS and chordoma varies widely, influenced by factors such as age, tumor size, metastasis at diagnosis, and treatment modalities including surgery, chemotherapy, and radiotherapy [[19], [20], [21], [22], [23], [24]].

Despite advancements in treatment, the prognosis for EWS remains poor, with a 5-year survival rate of approximately 65 % in localized cases and significantly lower in metastatic disease [25]. Chordoma, although generally slower growing, also presents a therapeutic challenge due to its high recurrence rate and resistance to conventional chemotherapy, with a 5-year survival rate ranging from 50 % to 80 %, depending on the location and extent of the disease [19,[26], [27], [28], [29]].

3 Literature review

We also summarize many recent similar studies in Supplementary Table 1. For EWS, the study by Mathew et al. [30] focused on EWS in non-skeletal locations with a sample size of 47 cases, finding that tumor location and positive surgical margins significantly impacted prognosis. Dimosthenis Andreou et al. [21] studied 1411 cases of pelvic EWS, identifying tumor location, treatment strategy, radiotherapy, poor histological features, incomplete bone resection, and tumor biopsy at the same institution as key prognostic factors.

Additionally, Alvarez-San Nicolas et al. [31] analyzed 90 cases of EWS in the limbs, discovering that poor treatment response, pelvic location, and the age group of 12–17 years were important prognostic factors. Wang et al. [32], in their analysis of 2059 cases of EWS and osteosarcoma, highlighted age, surgery, staging, primary location, tumor size, and histological type as critical factors influencing prognosis.

Zhan et al. [22] and Dai et al. [24] studied 1120 and 772 cases of EWS, respectively, finding that age, gender, primary location, tumor size, N stage, and M stage were significant prognostic factors. Zheng et al. [23], in their analysis of 1130 cases of EWS, found that younger patients, smaller tumors, absence of bone metastasis, and localized tumors had better prognoses, with surgery and chemotherapy positively impacting survival rates.

Other studies, such as those by Hsu et al., Chen et al., Jiang et al., Zhou et al., and Li et al. (2022) [23,25,33,34], also explored prognostic factors for EWS across different sample sizes and tumor locations, confirming the importance of factors such as age, race, tumor stage, surgery, chemotherapy, and metastasis.

For chordoma, Ouyang et al. [20] and Huang et al. [26] identified age, tumor size, histology, primary site, and the extent of surgical resection as critical determinants of prognosis in chordoma patients. Similarly, Li et al. [29] focused on chordomas of the pelvis and spine, underscoring the role of age, localized tumor involvement, and radical resection in influencing patient outcomes. Lin et al. [19] and Liu et al. [28] also reported that age, tumor size, disease extent, and surgical treatment were key prognostic factors, emphasizing the consistency of these findings across different studies and anatomical locations.

Moreover, studies by Teng et al. [35] and Huang et al. [27] explored chordomas located at the skull base and spine, respectively, and highlighted similar prognostic factors including primary site, disease stage, histological type, and the importance of surgical intervention. These studies collectively reinforce the significance of early diagnosis and comprehensive surgical management as cornerstones of effective chordoma treatment, regardless of tumor location.

Comparative studies between EWS and chordoma are limited, particularly those focusing on pelvic tumors. The SEER database, with its comprehensive collection of cancer incidence and survival data, provides an invaluable resource for examining the prognostic factors and survival outcomes of these rare malignancies. This study aims to fill the gap in the literature by providing a comparative analysis of pelvic EWS and chordoma, utilizing robust statistical methods to develop and validate predictive nomograms for OS, thereby aiding in the early identification of high-risk patients and informing clinical decision-making.

4 Methods

4.1 Data source and data extraction

In this study, we conducted a retrospective cohort study of patients diagnosed with pelvic EWS and chordoma. The SEER database is a population-based cancer registry that collects data on cancer incidence and survival from 17 cancer registries in the United States (2001–2019). Patient consent was waived for this observational study because it used de-identified data from the public SEER database.

Inclusion criteria were as follows: (1) Diagnosis confirmed by histopathological examination. (2) Ewing sarcoma and chordoma were diagnosed during the period from 2001 to 2019, with the pelvic bone as the site of origin. (3) Survival time is known and >1 month. The exclusion criteria were: (1) Unknown whether they underwent surgery. (2) Unknown radiation therapy. (3) Unknown chemotherapy. (4) Unknown stage.(5) Survival time≤1 month. A total of 1175 patients were eligible for final analysis. The inclusion and exclusion flowchart is shown in Fig. 1. Demographic and tumor data were obtained from the SEER*Stat software (version 8.4.0) for patients with pelvic EWS and chordoma.Fig. 1 The flowchart of including and dividing patients.

Fig. 1

We gathered patient demographic details, including age, gender, income, race, and regional origin; tumor characteristics, such as year of diagnosis, time from diagnosis to treatment, number of malignant tumors, and stage; treatment details, encompassing surgery, radiotherapy, and chemotherapy; and follow-up information, including survival status and survival time. Data extraction from electronic records of patients with pelvic EWS and chordoma at the SEER database was conducted by three trained researchers.

4.2 Outcome measures

The primary outcome of the survival analysis was overall survival (OS), defined as the time from diagnosis of pelvic EWS and chordoma to death from any cause. For individuals lost to follow-up before death, the last follow-up time is typically considered the time of death. OS was used to assess patients' survival status.

The survival curve, a Kaplan-Meier curve, depicts the survival status of patients over time. It is a crucial tool in survival analysis, enabling the comparison of survival differences between various cohorts.

Prognostic factors are characteristics associated with patient survival and can be used to predict the prognosis of individual patients.

4.3 Construction, validation of the nomogram and statistical analysis

Categorical variables were presented as frequencies and percentages (%) and compared using chi-squared tests. Univariate Cox regression analyses were performed to evaluate the association between each potential risk factor and overall survival (OS). Variables with a p-value <0.05 in the univariate analysis were included in the multivariate Cox regression analysis to identify independent predictors. Kaplan-Meier curves were generated to illustrate the impact of these independent risk factors on OS in patients with pelvic EWS and chordoma. Multicollinearity in the multivariate model was assessed using variance inflation factors (VIFs). Based on the multivariate analysis, a predictive nomogram for 3-, 5-, and 10-year OS for both EWS and chordoma was developed using R software in the training set.

To evaluate the nomogram's performance, a receiver operating characteristic (ROC) curve was plotted, and the area under the curve (AUC) was calculated to assess sensitivity and specificity. Calibration plots were generated to examine the nomogram's accuracy. Decision curve analysis (DCA) was used to assess the clinical utility of the predictive model by determining whether it improves forecasted net benefit. Patients were divided into low- and high-risk groups based on the nomogram score. Kaplan-Meier curves and log-rank analysis were used to compare survival differences between low-risk and high-risk groups.

Data analysis was conducted using SPSS version 26.0 (IBM Corp., USA) for statistical analysis and R version 4.0.3 (R Foundation for Statistical Computing, USA) for nomogram construction.

A graphical abstract has been included to visually summarize the main findings and contributions of our research (Supplementary eFig. 1).

5 Results

5.1 Baseline clinical and demographic characteristics of patients

A total of 1175 patients with pelvic EWS(n = 611) and chordoma(n = 564) were included. Pelvic EWS patients were randomly divided into training cohort 70 % (n = 431) and validation cohort 30 % (N = 180). Pelvic chordoma patients were also randomly divided into training cohort 70 % (n = 396) and validation cohort 30 % (n = 168)(Fig. 1).

Table 1 provides the baseline characteristics of the pelvic EWS and chordoma groups, respectively. Chordoma patients tended to be older than EWS patients (60 % vs 3 % aged ≥60 years). The gender ratio was similar between groups. There were no significant differences in income or region. Chordomas were more often diagnosed after 2008, while EWS predominated prior to 2008. Ewing's patients received faster treatment and had more metastatic disease at diagnosis, whereas chordomas were more localized. Ewing's patients received more chemotherapy and radiation; chordomas were more often managed with surgery. The baseline clinical and demographic characteristics of training cohort and validation cohort were similar, as shown in Table 2. Survival rates were significantly higher in the chordoma cohort compared to EWS group (3-year overall survival, 86.5 % VS 66.3 %, p < 0.001; 5-year survival was 79.4 % VS 59.4 %, p < 0.001; 10-year survival was 70.7 % SV 55.3 %, p < 0.001)(Table 3)Table 1 Baseline clinical and demographic characteristics of the Pelvic Ewing's Sarcoma and Chordoma.

Table 1Variables	Total	Ewing's Sarcoma	Chordoma	p-value	
(1175)	(n = 611)	(n = 564)	
Age, years, n (%)	
 <60	821 (70)	595 (97)	226 (40)	<0.001	
 ≥60	354 (30)	16 (3)	338 (60)	
Sex, n (%)	
 Male	456 (39)	366 (60)	353 (63)	0.377	
 Female	719 (61)	245 (40)	211 (37)	
Income (dollar), n (%)	
 ≥75000	173 (15)	95 (16)	78 (14)	0.503	
 55000-75000	498 (42)	263 (43)	235 (42)	
 <55000	504 (43)	253 (41)	251 (45)	
Region, n (%)	
 ≥1 million pop area	787 (67)	406 (66)	381 (68)	0.331	
 <1 million pop area	288 (25)	146 (24)	142 (25)	
 Nonmetropolitan	100 (9)	59 (10)	41 (7)	
Race, n (%)	
 White	1005 (86)	531 (87)	474 (84)	0.263	
 Black	50 (4)	21 (3)	29 (5)	
 Other	120 (10)	59 (10)	61 (11)	
Year of diagnosis, n (%)	
 2001–2008	238 (20)	148 (24)	90 (16)	<0.001	
 2008–2014	424 (36)	221 (36)	203 (36)	
 2015–2019	513 (44)	242 (40)	271 (48)	
Months from diagnosis to treatment, n (%)	
 0	342 (29)	255 (42)	87 (15)	<0.001	
 1	285 (24)	191 (31)	94 (17)	
 2	235 (20)	37 (6)	198 (35)	
 ≥3	313 (27)	128 (21)	185 (33)	
The number of malignant tumor, n (%)	
 1	1020 (87)	584 (96)	436 (77)	<0.001	
 2	122 (10)	24 (4)	98 (17)	
 ≥3	33 (3)	3 (0)	30 (5)	
Stage, n (%)	
Localized	354 (30)	105 (17)	249 (44)	<0.001	
Regional	459 (39)	198 (32)	261 (46)	
Distant	362 (31)	308 (50)	54 (10)	
Radiation, n (%)	
 No	606 (52)	256 (42)	350 (62)	<0.001	
 Yes	569 (48)	355 (58)	214 (38)	
Chemotherapy, n (%)	
 No	561 (48)	24 (4)	537 (95)	<0.001	
 Yes	614 (52)	587 (96)	27 (5)	
Surgery, n (%)	
 No	535 (46)	160 (26)	375 (66)	<0.001	
 Yes	640 (54)	451 (74)	189 (34)	
p-value is from Chi-Squared Test to indicate significant differentiation (P < 0.05 means significant differentiation).

Table 2 Baseline clinical and demographic characteristics of training and validation set in the pelvic Ewing's Sarcoma and Chordoma.

Table 2Variables	Ewing's Sarcoma (n = 611)	Chordoma (n = 564)	
Training set	Validation set	p	Training set	Validation	p	
(n = 431)	(n = 180)	(n = 396)	set (n = 168)	
Age, years, n (%)	
 <60	421 (98)	174 (97)	0.795	159 (40)	67 (40)	0.994	
 ≥60	10 (2)	6 (3)	237 (60)	101 (60)	
Sex, n (%)	
 Male	249 (58)	117 (65)	0.114	252 (64)	101 (60)	0.294	
 Female	182 (42)	63 (35)	144 (36)	67 (40)	
Income (dollar), n (%)	
 ≥75000	69 (16)	26 (14)	0.338	55 (14)	23 (14)	0.331	
 55000-75000	193 (45)	70 (39)	165 (42)	70 (42)	
 <55000	169 (39)	84 (47)	176 (44)	75 (45)	
Region, n (%)	
 ≥1 million pop area	289 (67)	117 (65)	0.357	262 (66)	119 (71)	0.118	
 <1 million pop area	106 (25)	40 (22)	103 (26)	39 (23)	
 Nonmetropolitan	36 (8)	23 (13)	31 (8)	10 (6)	
Race, n (%)	
 White	374 (87)	157 (87)	1.000	333 (84)	141 (84)	0.984	
 Black	15 (3)	6 (3)	25 (6)	4 (2)	
 Other	42 (10)	17 (10)	38 (10)	23 (14)	
Year of diagnosis, n (%)	
 2001–2008	97 (23)	51 (28)	0.329	64 (16)	26 (15)	0.119	
 2008–2014	161 (37)	60 (33)	146 (37)	57 (34)	
 2015–2019	173 (40)	69 (38)	186 (47)	85 (51)	
Months from diagnosis to treatment, n (%)	
 0	175 (41)	80 (44)	0.983	59 (15)	28 (17)	0.728	
 1	139 (32)	52 (29)	68 (17)	26 (15)	
 2	27 (6)	10 (6)	142 (36)	56 (33)	
 ≥3	90 (21)	38 (21)	127 (32)	58 (35)	
The number of malignant tumor, n (%)	
 1	411 (95)	173 (96)	0.537	306 (77)	130 (77)	0.687	
 2	18 (4)	6 (3)	66 (17)	32 (19)	
 ≥3	2 (0)	1 (1)	24 (6)	6 (4)	
Stage, n (%)	
Localized	80 (19)	25 (14)	0.444	179 (45)	70 (42)	0.250	
Regional	134 (31)	64 (36)	178 (45)	83 (49)	
Distant	217 (50)	91 (51)	1.70(0.93)	15 (9)	
Radiation, n (%)	
 No	182 (42)	74 (41)	0.845	156(49.2)	105 (62)	0.915	
 Yes	249 (58)	106 (59)	161(50.8)	63 (38)	
Chemotherapy, n (%)	
 No	15 (3)	9 (5)	0.360	3.94(0.66)	163 (97)	0.338	
 Yes	416 (97)	171 (95)	8.61(2.80)	5 (3)	
Surgery, n (%)	
 No	116 (27)	44 (24)	0.619	1.30(0.55)	112 (67)	0.800	
 Yes	315 (73)	136 (76)	201.97(72.63)	56 (33)	
p < 0.05: statistically significant difference.

Table 3 Comparison of the mortality rate of the pelvic Ewing's Sarcoma and Chordoma.

Table 3Status	3-year	Pa-value	5-year	Pa-value	10-year	Pa-value	
Ewing's Sarcoma	Chordoma	Ewing's Sarcoma	Chordoma	Ewing's Sarcoma	Chordoma	
(n = 611)	(n = 564)	(n = 611)	(n = 564)	(n = 611)	(n = 564)	
Alive	405(66.3)	488(86.5)	<0.001	363(59.4)	488(79.4)	<0.001	338(55.3)	399(70.7)	<0.001	
Dead	206(33.7)	76(13.5)	248(40.6)	116(20.6)	273(44.7)	165(29.3)	
a p-value is from Chi-Squared Test to indicate significant differentiation (P < 0.05 means significant differentiation).

5.2 Independent risk factors in the training set

Univariate and multivariate COX regression analyses revealed several independent risk factors for OS in patients with pelvic EWS and chordoma, respectively. For pelvic EWS, these factors included demonstrated year of diagnosis, income, stage and surgery were significantly associated with survival (Fig. 2A). For chordoma, age, months from diagnosis to treatment, stage and surgery were significantly associated with survival (Fig. 3A). Kaplan-Meier curves depict the influence of these independent risk factors on OS in patients with pelvic EWS and chordoma (Fig. 2, Fig. 3B–E). The variance inflation factors (VIFs) for these risk factors ranged from 1.07 to 1.62 for EWS and from 1.05 to 1.49 for chordoma, indicating the absence of multicollinearity.Fig. 2 (A)Univariate and multivariate analyses of Pelvic Ewing's Sarcoma. (B) Kaplan–Meier curves of Pelvic EWS in different income groups. (C) Kaplan–Meier curves of Pelvic EWS in different years of diagnosis groups. (D) Kaplan–Meier curves of Pelvic EWS in different stage groups. (E) Kaplan–Meier curves of Pelvic EWS in with or without surgery groups.

Fig. 2

Fig. 3 (A)Univariate and multivariate analyses of Chordoma. (B) Kaplan–Meier curves of Pelvic Chordoma in different age groups. (C) Kaplan–Meier curves of Pelvic Chordoma in different numbers of malignant tumor groups. (D) Kaplan–Meier curves of Pelvic Chordoma in different stage groups. (E) Kaplan–Meier curves of Pelvic Chordoma in with or without surgery groups.

Fig. 3

5.3 Nomogram model establishment

Kaplan-Meier curves depict the influence of pelvic EWS and chordoma on OS in patients with pelvic EWS and chordoma (Fig. 4A). Drawing upon multivariate Cox regression analysis, we constructed nomogram models to predict overall survival (OS) for pelvic EWS and chordoma. The EWS nomogram comprises four independent risk factors: year of diagnosis, income, stage, and surgery. The chordoma nomogram also includes four independent risk factors: age, time from diagnosis to treatment, stage, and surgery. Each nomogram factor is assigned a corresponding score, with the overall score reflecting OS. Stage had the greatest impact on EWS prognosis, while surgery had the most significant influence on chordoma prognosis (Fig. 4B and C).Fig. 4 (A) Kaplan–Meier curves of Pelvic EWS and Chordoma groups. (B) Nomogram for 3-, 5-, and 8-year OS of Pelvic EWS. (C) Nomogram for 3-, 5-, and 8-year OS of Pelvic Chordoma.

Fig. 4

5.4 Nomogram model validation

In the training set, the area under the curve (AUC) values reflecting overall survival prediction accuracy were 0.708, 0.697, and 0.689 for 3-, 5-, and 10-year survival in EWS (Fig. 5, Fig. 6, Fig. 7A). In the validation sets, the AUC values were 0.717, 0.735, and 0.724, respectively(Fig. 5, Fig. 6, Fig. 7B). Similarly, the AUC values in the training set were 0.731, 0.762, and 0.716 for 3-, 5-, and 10-year overall survival in chordoma patients (Fig. 8, Fig. 9, Fig. 10A). The corresponding AUC values in the validation sets were 0.803, 0.805, and 0.676 (Fig. 8, Fig. 9, Fig. 10B).Fig. 5 The ROC, calibration curve and decision curves of the nomogram predicting 3-year OS of Pelvic EWS in the training set (A, C and D) and the validation set (B, D and F).

Fig. 5

Fig. 6 The ROC, calibration curve and decision curves of the nomogram predicting 5-year OS of Pelvic EWS in the training set (A, C and D) and the validation set (B, D and F).

Fig. 6

Fig. 7 The ROC, calibration curve and decision curves of the nomogram predicting 10-year OS of Pelvic EWS in the training set (A, C and D) and the validation set (B, D and F).

Fig. 7

Fig. 8 The ROC, calibration curve and decision curves of the nomogram predicting 3-year OS of Pelvic Chordoma in the training set (A, C and D) and the validation set (B, D and F).

Fig. 8

Fig. 9 The ROC, calibration curve and decision curves of the nomogram predicting 5-year OS of Pelvic Chordoma in the training set (A, C and D) and the validation set (B, D and F).

Fig. 9

Fig. 10 The ROC, calibration curve and decision curves of the nomogram predicting 10-year OS of Pelvic Chordoma in the training set (A, C and D) and the validation set (B, D and F).

Fig. 10

Calibration plots demonstrated strong agreement between nomogram-predicted and observed outcomes for 3-, 5-, and 10-year survival in both the training and validation sets (C for training sets and D for validation sets) for Ewing sarcoma (Fig. 5, Fig. 6, Fig. 7C&D) and chordoma (Fig. 8, Fig. 9, Fig. 10C&D). Similarly, decision curve analysis showed that the Ewing sarcoma and chordoma nomograms conferred greater net benefit over non-intervention across a range of threshold probabilities for 3-, 5-, and 10-year survival in the training and validation sets (Ewing sarcoma Fig. 5, Fig. 6, Fig. 7E&F; chordoma Fig. 8, Fig. 9, Fig. 10E&F) (E for training sets and F for validation sets).

In EWS, the high-risk group has significantly worse survival compared to the low-risk group in both the training set (HR 2·80, 95 % CI 2·08–3·81; p < 0·0001) (Fig. 11A) and validation set (HR 3·16, 2·03–4·93; p < 0·0001) (Fig. 11B). The median survival is markedly reduced for the high-risk versus low-risk EWS patients. Similarly, chordoma high-risk patients had significantly worse survival than low-risk patients in the training (HR 3·68, 2·56–5·28; p < 0·0001) (Fig. 12A) and validation sets (HR 3·89, 2·26–6·67; p < 0·0001) (Fig. 12B).Fig. 11 Kaplan–Meier curves of Pelvic EWS in the low- and high-risk groups in the training set (A) and validation set (B).

Fig. 11

Fig. 12 Kaplan–Meier curves of Pelvic Chordoma in the low- and high-risk groups in the training set (A) and validation set (B).

Fig. 12

These metrics substantiate the accurate prediction of 3-, 5-, and 10-year overall survival (OS) for both Ewing sarcoma and chordoma using the respective nomograms. The two nomograms showed strong discrimination and prediction performance, supporting their reliability as valuable clinical tools.

6 Discussion

Our study using the SEER database demonstrates key differences in prognostic factors and overall survival between pelvic EWS and chordoma. Multivariate analysis revealed distinct factors independently influencing overall survival for each malignancy.

For EWS, the year of diagnosis, income status, tumor stage, and surgical treatment were significant in prognosis. The shift towards improved survival over time likely reflects advancement in multimodal therapy incorporating chemotherapy and radiation. Studies have shown that multimodal therapy consisting of radiation therapy to all areas of gross disease and intensive combination chemotherapy has led to complete responses and improved disease-free outcomes in patients with EWS [[36], [37], [38]]. For instance, in a study of nonpelvic, localized EWS of bone, the addition of Adriamycin or bilateral pulmonary radiotherapy to vincristine, dactinomycin, and cyclophosphamide therapy significantly improved survival rates [36]. Similarly, in the management of localized EWS of pelvic and sacral bones, multimodal therapy resulted in a significant advantage in relapse-free survival and overall survival compared to historical control series [37].

Interestingly, higher income was associated with better outcomes in EWS, suggesting potential disparities in access to optimal cancer care. Alsoof et al. observed income was significantly associated with decreased survival in patients with Ewing sarcoma in a prospective observational study (p < 0.001) [39]. Panda et al. conducted a prospective study of 66 patients with Ewing sarcoma in adults over 40 years of age from a low-middle-income country and found 5-year OS were 48.1 % (95 % CI = 34.6–61.6) [40].

Those with higher incomes tend to have better access to high-quality healthcare. They can more easily afford comprehensive health insurance that covers advanced treatments [40,41]. This includes access to major cancer centers and clinical trials for emerging therapies. The financial resources also allow them to pay for expenses not fully covered, like travel costs for care. In addition, those with more disposable income can likely maintain healthier lifestyles. A nutritious diet, regular exercise, and lower stress levels may improve resilience during intensive cancer treatments. Avoiding delays in seeking care due to costs can also lead to earlier diagnosis at more treatable stages.

As expected, lower tumor stage and surgical resection conferred survival benefits. Our research findings are consistent with many previous studies. Compared to existing literature, this study provides a more comprehensive perspective.

Additionally, Fizazi et al. found that metastasis at presentation (P = 0.00001) was an independent prognostic factor for survival in a retrospective cohort study of 182 patients [42]. Joe Lee et al. indicated metastatic disease remained a significant prognostic factor in Ewing sarcoma(HR, 2.74; 95 % CI, 2.14–3.49). The study conducted by Verma found that metastatic disease is associated with OS [43].

In contrast, for chordomas, we analyzed prognostic factors for pelvic chordoma and identified age, time from diagnosis to treatment, tumor stage, and surgical intervention as significant predictors of overall survival in this study. These findings align with the broader literature on chordoma, which consistently highlights the importance of these factors.

Additionally, chordomas characteristically affect older adults, whose comorbidities and performance status may hinder intensive treatment [44]. Expedient management is prudent to avoid further local spread [45]. Similarly, achieving surgical resection is a consistent prognostic factor, but challenging in the pelvis [46].

Beyond differences in prognostic variables, overall survival was significantly higher across all timepoints for chordomas compared to EWS of the pelvis. This survival advantage may relate to chordomas' lack of metastatic potential, allowing primarily local control to impact outcomes [45]. Conversely, metastatic disease burdens prognosis in a substantial proportion of Ewing's patients [47,48].By delineating distinct prognostic profiles, our nomograms provide practical clinical tools to risk stratify pelvic Ewing's and chordoma patients. Accurate assessment of expected outcomes facilitates personalized treatment decisions incorporating patient goals. For example, a young patient with extensive metastatic Ewing may elect palliative radiation over intensive chemotherapy in light of poor predicted survival. An older chordoma patient with medical issues could pursue a course of definitive radiation therapy rather than a high-risk resection.

7 Limitations and strengths

Several limitations should be acknowledged regarding the SEER database and retrospective analyses. This study has several limitations that should be acknowledged. Firstly, the retrospective nature of the analysis using the SEER database inherently carries the potential for selection bias and missing data, which may influence the findings. Secondly, the SEER database lacks detailed information on certain variables that could impact outcomes, such as specific chemotherapy regimens, radiation doses, surgical margins, and molecular characteristics of the tumors. Thirdly, the database does not provide comprehensive data on recurrence or progression-free survival, which are important endpoints in cancer research. Additionally, while the SEER database includes a diverse population, it may not fully represent the entire U.S. population or capture the nuances of care in different healthcare settings. The follow-up period for some patients may not be sufficient to observe long-term outcomes, particularly for chordoma, which is known for its slow progression and late recurrences. Lastly, we did not conduct a more detailed subgroup analysis, such as age.

This study has several strengths. Firstly, it utilizes the large and diverse SEER database, enhancing the generalizability of the findings. The data from 2001 to 2019 provide a comprehensive view of long-term trends and outcomes. Secondly, strict inclusion criteria ensure accurate analysis of well-defined, histopathologically confirmed cases. Multivariate Cox regression identifies key prognostic factors, offering deeper insights into survival influences. Additionally, the validated nomogram for 3-, 5-, and 10-year overall survival provides a useful tool for personalized patient management. The comparative analysis of pelvic EWS and chordoma offers valuable insights for better treatment strategies. Finally, rigorous statistical methods, including Kaplan-Meier and ROC curves, ensure a thorough and credible evaluation of survival outcomes.

7.1 Future works

Future research should focus on several key areas. Prospective studies with larger sample sizes are needed to validate our findings and explore additional survival factors. Detailed molecular and genetic analyses could identify new biomarkers and treatment targets, leading to more personalized therapies. Investigating the impact of specific treatments, including chemotherapy types, advanced radiation techniques, and surgical innovations, could optimize therapy for these rare tumors. Long-term follow-up studies are essential to understand the natural history and durability of treatment responses, especially for chordoma.

In summary, our study delineates distinct prognostic variables in pelvic EWS versus chordomas. Developed nomograms provide means to predict overall survival based on these factors, with the potential to guide individualized treatment decisions. Findings highlight the differing biology and clinical behavior of these rare malignancies.

8 Conclusion

This study successfully developed and validated nomograms for predicting overall survival (OS) in patients with pelvic EWS and chordoma, identifying key prognostic factors and comparing survival outcomes between these two rare conditions. The higher survival rates observed in chordoma patients compared to those with EWS emphasize the need for different strategies in treating these tumors.

Ethics approval and consent to participate

For this type of study formal consent is not required and the Institutional Review Board of Dandong Central Hospital waived the need for informed consent. All procedures performed in studies involving human participants were in accordance with the ethical standards of the Institutional Review Board of Dandong Central Hospital and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Consent for publication

Not applicable.

Data availability statement

Publicly available datasets were analyzed in this study. This data can be found here: https://seer.Cancer.gov/.

Funding

None.

CRediT authorship contribution statement

Wanyun Tang: Funding acquisition, Formal analysis, Conceptualization. Runzhuo Li: Data curation. Xiaoying Lai: Conceptualization. Xiaohan Yu: Data curation. Renjian He: Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A Supplementary data

The following is the supplementary data to this article.Multimedia component 1

Multimedia component 1

Multimedia component 2

Multimedia component 2

Acknowledgments

Not applicable.

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.heliyon.2024.e37013.
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References

1 Akiyama T. Ogura K. Gokita T. Tsukushi S. Iwata S. Nakamura T. Matsumine A. Yonemoto T. Nishida Y. Saita K. Analysis of the infiltrative features of chordoma: the relationship between micro-skip metastasis and postoperative outcomes Ann. Surg Oncol. 25 4 2017 912 919 29181683
2 Poitout D.G. Favre J. Malignant bone tumors: from Ewing's sarcoma to osteosarcoma Biomechanics and Biomaterials in Orthopedics 2016 Springer London London 375 383
3 Bakker S.H. Jacobs W.C.H. Pondaag W. Gelderblom H. Nout R.A. Dijkstra P.D.S. Peul W.C. Vleggeert-Lankamp CLA: chordoma: a systematic review of the epidemiology and clinical prognostic factors predicting progression-free and overall survival Eur. Spine J. 27 12 2018 3043 3058 30220042
4 Esiashvili N. Goodman M. Marcus R.B. Changes in incidence and survival of ewing sarcoma patients over the past 3 decades J. Pediatr. Hematol. Oncol. 30 6 2008 425 430 18525458
5 Mounessi F.S. Lehrich P. Haverkamp U. Willich N. Bölling T. Eich H.T. Pelvic ewing sarcomas Strahlenther. Onkol. 189 4 2013 308 314 23443613
6 Sun X. Hornicek F. Schwab J.H. Chordoma: an update on the pathophysiology and molecular mechanisms Curr Rev Musculoskelet Med 8 4 2015 344 352 26493697
7 Zöllner S.K. Amatruda J.F. Bauer S. Collaud S. de Álava E. DuBois S.G. Hardes J. Hartmann W. Kovar H. Metzler M. Ewing sarcoma-diagnosis, treatment, clinical challenges and future perspectives J. Clin. Med. 10 8 2021 1685 33919988
8 Heery C.R. Chordoma: the quest for better treatment options Oncol Ther 4 1 2016 35 51 28261639
9 Wang B. Tian F. Tong X. Clinical grading system, surgical outcomes and prognostic analysis of cranial base chordomas J Korean Neurosurg Soc 65 3 2022 469 478 35462525
10 Brown J.M. Rakoczy K. Tokson J.H. Jones K.B. Groundland J.S. Ewing sarcoma of the pelvis: clinical features and overall survival Cancer Treat Res Commun 33 2022 100634
11 Guder W.K. Hardes J. Nottrott M. Steffen A.J. Dirksen U. Streitbürger A. Pelvic Ewing sarcoma: a retrospective outcome analysis of 104 patients who underwent pelvic tumor resection at a single supra-regional center J. Orthop. Surg. Res. 15 1 2020
12 Logarajah S. Darwish M.B. Jeyarajah D.R. Pelvic chordoma: a multidisciplinary surgical effort J. Gastrointest. Surg. 26 4 2022 987 988 35083720
13 Ulici V. Hart J. Chordoma Arch. Pathol. Lab Med. 146 3 2022 386 395 34319396
14 Narayanan G. Kamala L.H. Nair S.G. Purushothaman P.N. Kumar A. Kattoor J. Ewing's sarcoma in adolescents and adults - 10-year experience from a tertiary cancer center in India J Cancer Res Ther 20 1 2024 79 84 38554302
15 Zou Y.S. Morsberger L. Hardy M. Ghabrial J. Stinnett V. Murry J.B. Long P. Kim A. Pratilas C.A. Llosa N.J. Complex/cryptic EWSR1::FLI1/ERG gene fusions and 1q jumping translocation in pediatric ewing sarcomas Genes 14 6 2023 1139 37372318
16 Xu J. Shi Q. Lou J. Wang B. Wang W. Niu J. Guo L. Chen C. Yu Y. Huang Y. Chordoma recruits and polarizes tumor-associated macrophages via secreting CCL5 to promote malignant progression J Immunother Cancer 11 4 2023 e006808
17 Burgers J.M.V. Oldenburger F. de Kraker J. van Bunningen B.N.F.M. van der Eijken J.W. Delemarre J.F.M. Staalman C.R. Voûte P.A. Ewing's sarcoma of the pelvis: changes over 25 years in treatment and results European Journal of Cancer 33 14 1997 2360 2367 9616282
18 Efetov S.K. Kochetkov V.S. Puzakov K.B. Tulina I.A. Kozlova P.S. Tsarkov P.V. Combined surgical approach for large sacral chordoma resection with pelvic floor reconstruction - a video vignette Colorectal Dis. 23 2 2021 566 567 33175461
19 Lin K. Song K. Wang S. Jiang L. Wang H. Dong J. Predict overall survival of spinal conventional chordoma: development and assessment of a new predictive nomogram Clin. Neurol. Neurosurg. 197 2020 106174
20 Ouyang C. Sun Y. Li Y. Jiang M. Nong L. Gao G. Prognostic nomogram in middle-aged and elderly patients with chordoma: a SEER-based study J. Orthop. Surg. 32 2 2024 10225536241254208
21 Andreou D. Ranft A. Gosheger G. Timmermann B. Ladenstein R. Hartmann W. Bauer S. Baumhoer D. Berg Hvd Dijkstra P. Which factors are associated with local control and survival of patients with localized pelvic Ewing's sarcoma? A retrospective analysis of data from the euro-EWING99 trial Clin. Orthop. Relat. Res. 478 2 2020 290 302 31580267
22 Zhan H. Mo F. Zhu M. Xu X. Zhang B. Liu H. Dai M. A SEER-based nomogram accurately predicts prognosis in Ewing's sarcoma Sci. Rep. 11 1 2021
23 Zhou Z. Wang J. Fang L. Ma J. Guo M. A nomogram for predicting overall survival in patients with Ewing sarcoma: a SEER-based study BMC Musculoskelet Disord 21 1 2020
24 Dai K.-N. Li A.-B. An efficient nomogram to predict overall survival of patients with pediatric Ewing's sarcoma: a population-based study Int. J. Graph Multimed. 14 2021 6101 6109
25 Li W. Dong S. Lin Y. Wu H. Chen M. Qin C. Li K. Zhang J. Tang Z.-R. Wang H. A tool for predicting overall survival in patients with Ewing sarcoma: a multicenter retrospective study BMC Cancer 22 1 2022
26 Huang J.-F. Chen D. Sang C.-M. Zheng X.-Q. Lin J.-L. Lin Y. Ni W.-F. Wang X.-Y. Li Y.M. Wu A.-M. Nomogram for individualized prediction and prognostic factors for survival in patients with primary spinal chordoma: a population-based longitudinal cohort study World Neurosurgery 128 2019 e603 e614 31054340
27 Huang Z. Fan Z. Zhao C. Sun H. A novel nomogram for predicting cancer-specific survival in patients with spinal chordoma: a population-based analysis Technol. Cancer Res. Treat. 20 2021 153303382110365
28 Liu Q. Ren J. Feng H. Nomograms for predicting long‐term overall survival and cancer-specific survival in chordoma: a population‐based study Future Oncol. 18 24 2022 2687 2699
29 Li J. Ma C. Yuan X. Li N. Xu Y. Guo J.-Y. Liao H. Competing risk nomogram for predicting prognosis of patients with spinal and pelvic chordoma: a SEER-based retrospective study Eur. Spine J. 32 4 2023 1334 1344 36805321
30 Mathew J. Arjunan R. Dasappa A. Namachivayam A. Prognostic factors and clinical outcomes in extraskeletal ewing sarcoma: a cohort study Ann. Surg Oncol. 30 5 2022 3084 3094 36564656
31 Alvarez-SanNicolas J. Gracia-Alegría I. Trullols-Tarragó L. Peiró-Ibáñez A. Lamas-Gómez C. Prognostic factors and survival in Ewing's sarcoma treated by limb salvage surgery Clin. Transl. Oncol. 21 10 2019 1 9 30627983
32 Wang J. Zhanghuang C. Tan X. Mi T. Liu J. Jin L. Li M. Zhang Z. He D. A nomogram for predicting cancer-specific survival of osteosarcoma and Ewing's sarcoma in children: a SEER database analysis Front. Public Health 10 2022 837506
33 Hsu C.-J. Ma Y. Xiao P. Hsu C.-C. Wang D. Fok M.N. Peng R. Xu X. Lu H. Overall survival comparison between pediatric and adult Ewing sarcoma of bone and adult nomogram construction: a large population-based analysis Front Pediatr 11 2023
34 chen L. Long C. Liu J. Xing F. Duan X. Characteristics and prognosis of pelvic Ewing sarcoma: a SEER population-based study PeerJ 7 2019 e7710
35 Teng C. Yang Q. Xiong Z. Ye N. Li X. Multivariate analysis and validation of the prognostic factors for skull base chordoma Front Surg 8 2021
36 Burgert E.O. Nesbit M.E. Garnsey L.A. Gehan E.A. Herrmann J. Vietti T.J. Cangir A. Tefft M. Evans R. Thomas P. Multimodal therapy for the management of nonpelvic, localized Ewing's sarcoma of bone: intergroup study IESS-II J. Clin. Orthod. 8 9 1990 1514 1524
37 Evans R.G. Nesbit M.E. Gehan E.A. Garnsey L.A. Burgert O. Vietti T.J. Cangir A. Tefft M. Thomas P. Askin F.B. Multimodal therapy for the management of localized Ewing's sarcoma of pelvic and sacral bones: a report from the second intergroup study J. Clin. Orthod. 9 7 1991 1173 1180
38 Vietti T.J. Gehan E.A. Nesbit M.E. Burgert E.O. Pilepich M. Tefft M. Kissane J. Pritchard D.J. Multimodal therapy in metastatic Ewing's sarcoma: an Intergroup Study Natl. Cancer Inst. Monogr. 56 1981 279 284 7029296
39 Alsoof D. Kasthuri V. Homer A. Glueck J. McDonald C.L. Kuris E.O. Daniels A.H. County rurality is associated with increased tumor size and decreased survival in patients with ewing sarcoma Orthop. Rev. 15 2023
40 Panda G. Chandrasekharan A. Das S. Bhargava P. Srinivas S. Laskar S. Mokal S. Rekhi B. Khanna N. Menon N. Outcomes of Ewing sarcoma in adults over 40 years of age from a low-middle income country ecancer 16 2022
41 Finkelstein D.M. Harding J.F. Paulsell D. English B. Hijjawi G.R. Ng’andu J. Economic well-being and health: the role of income support programs in promoting health and advancing health equity Health Aff. 41 12 2022 1700 1706
42 Fizazi K. Dohollou N. Blay J.Y. Guérin S. Le Cesne A. André F. Pouillart P. Tursz T. Nguyen B.B. Ewing's family of tumors in adults: multivariate analysis of survival and long-term results of multimodality therapy in 182 patients J. Clin. Orthod. 16 12 1998 3736 3743
43 Lee J. Hoang B.H. Ziogas A. Zell J.A. Analysis of prognostic factors in Ewing sarcoma using a population‐based cancer registry Cancer 116 8 2010 1964 1973 20151425
44 Stacchiotti S. Sommer J. Building a global consensus approach to chordoma: a position paper from the medical and patient community Lancet Oncol. 16 2 2015 e71 e83 25638683
45 Gulluoglu S. Turksoy O. Kuskucu A. Ture U. Bayrak O.F. The molecular aspects of chordoma Neurosurg. Rev. 39 2 2015 185 196 26363792
46 Denaro L. Berton A. Ciuffreda M. Loppini M. Candela V. Brandi M.L. Longo U.G. Surgical management of chordoma: a systematic review The Journal of Spinal Cord Medicine 43 6 2018 797 812 30048230
47 Shi J. Yang J. Ma X. Wang X. Risk factors for metastasis and poor prognosis of Ewing sarcoma: a population based study J. Orthop. Surg. Res. 15 1 2020
48 Meshram G.G. Kaur N. Hura K.S. Ewing's sarcoma with distant metastasis: a brief note on management and emerging therapies Clin. Pract. 9 3 2019 1111 31579490
