# A Nomogram-Based Risk Classification System Predicting the Overall Survival of Childhood with Clear Cell Sarcoma of the Kidney Based on the SEER Database

Authors: Jingchao Ding; Huanyin Yao; Qingchun Chen
Journal: Evidence-Based Complementary and Alternative Medicine
 (2022)

---

## Abstract

Objective. Clear cell sarcoma of the kidney (CCSK) is a lethal pediatric renal malignancy with poor prognosis. A prognostic nomogram needs to be established for overall survival (OS) prediction of patients with CCSK. Methods. Eligible 2588 CCSK patients (age 0–19) diagnosed between 2000 and 2017 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomized into training and validation cohorts (7 : 3). Independent prognostic factors were identified by univariate and multifactorial Cox regression analyses and used to construct a nomogram. Receiver operating characteristics (ROC) analysis, calibration curves, and decision curve analysis (DCA) were used to validate the nomogram. Moreover, a risk classification system was established based on the risk scores of the nomogram. Results. Cox analyses revealed that age, combined stage, and origin were most significant prognostic factors. Based on these prognostic factors, a nomogram was established for predicting 3- and 5-year OS of patients with CCSK. The area under the ROC curve (AUC) of 3- and 5-year OS was 0.733 and 0.728 in the training cohort, corresponding to 0.69 and 0.674 in the validation cohort. The C-index of calibration curves in the training and validation cohorts was 0.724 and 0.686. DCAs indicated the clinical utility of this nomogram. A risk classification system stratified CCSK patients into three different risk cohorts. The OS time of low-, intermediate-, and high-risk patients was 76, 68, and 65 months in the training cohort, corresponding to 69.5, 66, and 72 months in the validation cohort. Conclusion. A nomogram-based risk classification system has high accuracy for the prognostic prediction of CCSK.


---

## Body

## 1. Introduction

Clear cell sarcoma of the kidney (CCSK) is one of the most common pediatric renal malignancies, accounting for approximately 5% of all primary childhood kidney tumors [1, 2]. The common therapeutic strategies for CCSK are surgical resection and adjuvant chemotherapy; however, the effects are still unsatisfactory due to late relapses and distant metastasis [2]. Currently, main therapy for CCSK depends on doxorubicin that is accompanied by cardiotoxic side effects [3]. Up to now, there is little information on clinical and histological features of CCSK; therefore, it is difficult for pathologic diagnosis and therapeutic development [4]. Timely detection and prognosis are of paramount importance for CCSK treatment. Thus, it is meaningful and urgent to construct a prognostic model for better treatment of CCSK.The Surveillance, Epidemiology, and End Results (SEER) database includes basically comprehensive cancer statistics, which is an authoritative source in the United States. SEER database records the demographics, diagnosis, tumor characteristics, survival records, and therapies of patients with malignant tumors [5]. Nomogram is a reliable tool to predict the overall survival (OS) of patients with cancers, which can be established using the SEER database. Nomograms have been widely applied to identify potential prognostic factors that are associated with OS in multiple cancers, such as bladder, gastric, and colorectal cancers [6–8]. Zhang et al. constructed a reliable prognostic nomogram supporting the assessment of OS in bladder cancer patients based on the SEER database [6]. Yu Zhang established the nomograms of colorectal-cancer patients that exhibit favorable clinical values in predicting OS and cancer-specific survival [8]. Therefore, a nomogram can effectively predict the OS and clinical prognosis of patients with CCSK based on the SEER database.Based on this background, we explored potential risk parameters associated with CCSK using the SEER database. Furthermore, a prognostic nomogram was constructed to predict 3- and 5-year OS time of CCSK patients. This research provides a useful model to predict survival state of CCSK patients and apply in clinical therapy.


## 2. Materials and Methods

### 2.1. Data Source

The clinical data of CCSK patients were downloaded from the SEER database (https://seer.cancer.gov/) using the SEER∗Stat program (v 8.3.8, National Cancer Institute, MD, USA) [9]. A total of 2588 CCSK patients who were diagnosed from 2000 to 2017 were enrolled. The enrollment criteria of CCSK patients were as follows: [1] patients were 0–19 years old; [2] the diagnosis of CCSK was a positive confirmation; [3] and there was complete survival information. Eligible patients were randomly allocated into the training cohort and the validation cohort (7 : 3). The prognostic factors, including age, gender, race, origin, laterality, combined stage, radiation, and chemotherapy, were selected for further analyses.


### 2.2. Nomogram Construction

The associations between clinicopathologic variables and OS were assessed using univariate and multivariate Cox regression analyses, and the results were visualized using forestplot in R package [10]. Hazard ratios (HRs) were presented with 95% confidence interval (CI). P values < 0.05 were identified as final independent risk factors to predict the OS in the multivariate Cox regression analysis. Subsequently, these independent risk factors were applied to construct a nomogram for the prediction of 3- and 5-year OS of CCSK patients. The nomogram was established using regression modeling strategies (BMS) in R package and visualized using ggplot [11, 12].


### 2.3. Nomogram Validation

The validation of the nomogram was performed by the receiver operating characteristic (ROC) analysis that was achieved using survivalROC in R package [13]. An area-under-the-ROC-curve (AUC) value more than 0.65 suggests the perfect prediction of the nomogram. Calibration curve analysis was achieved using RMS in R package [14]. A 45-degree diagonal line represented a perfect prediction. Besides, decision curve analysis (DCA) was performed using ggDCA in R package to assess the clinical value of the nomogram [15].


### 2.4. Risk Classification System  and Survival Analyses

The risk classification system was constructed based on the risk scores of each CCSK patient calculated by the nomogram. The cutoff values were determined using theX-tile program. Moreover, survival analysis was performed according to the risk classification system using the survival and survminer in R package [16, 17].


### 2.5. Statistical Analyses

All data were analyzed using the SPSS version 27.0 (IBM, NY, USA). Two-tailedP value < 0.05 was statistical difference.


## 2.1. Data Source

The clinical data of CCSK patients were downloaded from the SEER database (https://seer.cancer.gov/) using the SEER∗Stat program (v 8.3.8, National Cancer Institute, MD, USA) [9]. A total of 2588 CCSK patients who were diagnosed from 2000 to 2017 were enrolled. The enrollment criteria of CCSK patients were as follows: [1] patients were 0–19 years old; [2] the diagnosis of CCSK was a positive confirmation; [3] and there was complete survival information. Eligible patients were randomly allocated into the training cohort and the validation cohort (7 : 3). The prognostic factors, including age, gender, race, origin, laterality, combined stage, radiation, and chemotherapy, were selected for further analyses.


## 2.2. Nomogram Construction

The associations between clinicopathologic variables and OS were assessed using univariate and multivariate Cox regression analyses, and the results were visualized using forestplot in R package [10]. Hazard ratios (HRs) were presented with 95% confidence interval (CI). P values < 0.05 were identified as final independent risk factors to predict the OS in the multivariate Cox regression analysis. Subsequently, these independent risk factors were applied to construct a nomogram for the prediction of 3- and 5-year OS of CCSK patients. The nomogram was established using regression modeling strategies (BMS) in R package and visualized using ggplot [11, 12].


## 2.3. Nomogram Validation

The validation of the nomogram was performed by the receiver operating characteristic (ROC) analysis that was achieved using survivalROC in R package [13]. An area-under-the-ROC-curve (AUC) value more than 0.65 suggests the perfect prediction of the nomogram. Calibration curve analysis was achieved using RMS in R package [14]. A 45-degree diagonal line represented a perfect prediction. Besides, decision curve analysis (DCA) was performed using ggDCA in R package to assess the clinical value of the nomogram [15].


## 2.4. Risk Classification System  and Survival Analyses

The risk classification system was constructed based on the risk scores of each CCSK patient calculated by the nomogram. The cutoff values were determined using theX-tile program. Moreover, survival analysis was performed according to the risk classification system using the survival and survminer in R package [16, 17].


## 2.5. Statistical Analyses

All data were analyzed using the SPSS version 27.0 (IBM, NY, USA). Two-tailedP value < 0.05 was statistical difference.


## 3. Results

### 3.1. Baseline Characteristics of  CCSK Patients

A total of 2588 CCSK patients (0–19 years old) were identified using the SEER database from 2000 to 2017. Patients were divided into the training and validation cohorts (7 : 3). We identified eight major clinicopathologic characteristics of CCSK patients, including age, gender, race, origin, laterality, combined stage, radiation, and chemotherapy. There was no significant difference in the distribution of these clinical characteristics between the training cohort and validation cohortP>0.05 (Table 1).Table 1
Baseline characteristics of CCSK patients.
CharacteristicFrequency (n, %)Training cohort (n, %)Validation cohort (n, %)P valueAge (years)0.1694Median (IQR)3 (0–19)<3.441582 (61.1)1122 (61.9)460 (59.3)>3.441006 (38.9)690 (38.1)316 (40.7)Gender0.9718Male1222 (47.2)856 (47.2)366 (47.2)Female1366 (52.8)956 (52.8)410 (52.8)Race0.8253White1955 (75.5)1369 (75.6)586 (75.5)Black448 (17.3)312 (17.2)136 (17.5)Asian or Pacific Islander121 (4.7)87 (4.8)34 (4.4)American Indian/Alaska native34 (1.3)24 (1.3)10 (1.3)Origin0.9231Spanish-Hispanic-Latino687 (26.5)482 (26.6)205 (26.4)Non-Spanish-Hispanic-Latino1901 (73.5)1330 (73.4)571 (73.6)Laterality0.793Right1173 (45.3)823 (45.4)350 (45.1)Left1216 (47.0)852 (47.0)364 (46.9)Bilateral183 (7.1)126 (4.9)57 (7.3)Combined stage0.08337Distant459 (17.7)311 (17.2)148 (19.1)Localized816 (31.5)567 (31.3)249 (32.1)Regional605 (23.4)439 (17.0)166 (21.4)Radiation0.8683Beam1201 (46.4)840 (46.4)361 (46.5)None1361 (52.6)956 (52.8)405 (52.2)Chemotherapy0.658Yes2348 (90.7)1641 (90.6)707 (91.1)No240 (9.3)171 (9.4)69 (8.9)CCSK, clear cell sarcoma of the kidney; IQR, interquartile range.


### 3.2. Independent Prognostic Factors Correlated with OS of CCSK Patients

Hereafter, univariate and multivariate Cox regression analyses were performed to identify significant risk factors of CCSK in the training cohort. Univariate Cox regression analysis revealed that ageP=7.8e−06, origin P=0.0047, combined stage P<0.001, and radiation P=5.38e−05 were the significant risk factors related to OS of CCSK patients (Figure 1(a)). Multivariate Cox regression analysis further confirmed that age P=0.0035, origin P=0.025, and combined stage P<0.001 were independent prognostic factors of CCSK survival (Figure 1(b)).Figure 1
Univariate and multivariate Cox regression analyses of clear cell sarcoma of the kidney (CCSK) patient survival in the training cohort. (a) Univariate Cox regression analysis. (b) Multivariate Cox regression analysis.
(a)(b)


### 3.3. Nomogram Establishment and Validation

A nomogram model for predicting 3- and 5-year OS was constructed, which integrated all three independent prognostic parameters (age, origin, and combined stage). Age (nomogram score range: 0–100) was the most important independent prognostic parameter for evaluating OS, followed by combined stage (0–97.5) and origin (0–30) (Figure2). Furthermore, ROC analysis was performed for nomogram validation. The AUC values of 3- and 5-year were 0.733 and 0.728, respectively, in the training cohort, corresponding to 0.690 and 0.674 in the validation cohort (Figures 3(a)–3(d)). The C-index values of the training and validation cohorts were 0.724 and 0.686, respectively. Calibration curve exhibited satisfactory agreement between actual and prediction survival in CCSK patients (Figures 4(a)–4(d)). DCA showed that this nomogram model has great benefits for predicting 3- and 5-year OS time of patients with CCSK within all of threshold probabilities and presented more positive net benefit than the “all” or “none” strategies between the training cohort and validation cohort (Figures 5(a), 5(b)).Figure 2
A nomogram predicting 3- and 5-year overall survival (OS) of CCSK patients in the training cohort.Figure 3
Receiver operating characteristic (ROC) curves of the nomogram for predicting the 3- and 5-year OS of CCSK patients. (a, b) ROC curves in the training cohort. (c, d) ROC curves in the validation cohort.
(a)(b)(c)(d)Figure 4
Calibration curves for predicting the 3- and 5-year OS of CCSK patients. (a, b) The calibration curves in the training cohort. (c, d) The calibration curves in the validation cohort.
(a)(b)(c)(d)Figure 5
Decision curve analysis (DCA) of the nomogram predicting 3- and 5-year OS of CCSK patients. (a) DCA in the training cohort. (b) DCA in the validation cohort.
(a)(b)


### 3.4. Risk Classification System

Enrolled patients with CCSK were divided into three groups: low-, intermediate-, and high-risk groups. The corresponding cutoff points for risk groups were classified as follows: <29.28, 29.28 ≤ nomogram score <60.71, and ≥60.71. In the training cohort, the median OS time of CCSK patients in the low-, intermediate-, and high-risk groups was 76.0, 68.0, and 65.0 months, respectively. In the validation cohort, the median OS time of CCSK patients in the low-, intermediate-, and high-risk groups was 69.5 months, 66.0 months, and 72 months, respectively. Significant OS differences were exhibited among the training, validation, and entire cohortsP<0.05 (Figures 6(a)–6(c)).Figure 6
Comparison of OS in the low-, intermediate-, and high-risk patients with CCSK. (a) Comparison of OS in the training cohort. (b) Comparison of OS in the validation cohort. (c) Comparison of OS in all cohorts.
(a)(b)(c)


## 3.1. Baseline Characteristics of  CCSK Patients

A total of 2588 CCSK patients (0–19 years old) were identified using the SEER database from 2000 to 2017. Patients were divided into the training and validation cohorts (7 : 3). We identified eight major clinicopathologic characteristics of CCSK patients, including age, gender, race, origin, laterality, combined stage, radiation, and chemotherapy. There was no significant difference in the distribution of these clinical characteristics between the training cohort and validation cohortP>0.05 (Table 1).Table 1
Baseline characteristics of CCSK patients.
CharacteristicFrequency (n, %)Training cohort (n, %)Validation cohort (n, %)P valueAge (years)0.1694Median (IQR)3 (0–19)<3.441582 (61.1)1122 (61.9)460 (59.3)>3.441006 (38.9)690 (38.1)316 (40.7)Gender0.9718Male1222 (47.2)856 (47.2)366 (47.2)Female1366 (52.8)956 (52.8)410 (52.8)Race0.8253White1955 (75.5)1369 (75.6)586 (75.5)Black448 (17.3)312 (17.2)136 (17.5)Asian or Pacific Islander121 (4.7)87 (4.8)34 (4.4)American Indian/Alaska native34 (1.3)24 (1.3)10 (1.3)Origin0.9231Spanish-Hispanic-Latino687 (26.5)482 (26.6)205 (26.4)Non-Spanish-Hispanic-Latino1901 (73.5)1330 (73.4)571 (73.6)Laterality0.793Right1173 (45.3)823 (45.4)350 (45.1)Left1216 (47.0)852 (47.0)364 (46.9)Bilateral183 (7.1)126 (4.9)57 (7.3)Combined stage0.08337Distant459 (17.7)311 (17.2)148 (19.1)Localized816 (31.5)567 (31.3)249 (32.1)Regional605 (23.4)439 (17.0)166 (21.4)Radiation0.8683Beam1201 (46.4)840 (46.4)361 (46.5)None1361 (52.6)956 (52.8)405 (52.2)Chemotherapy0.658Yes2348 (90.7)1641 (90.6)707 (91.1)No240 (9.3)171 (9.4)69 (8.9)CCSK, clear cell sarcoma of the kidney; IQR, interquartile range.


## 3.2. Independent Prognostic Factors Correlated with OS of CCSK Patients

Hereafter, univariate and multivariate Cox regression analyses were performed to identify significant risk factors of CCSK in the training cohort. Univariate Cox regression analysis revealed that ageP=7.8e−06, origin P=0.0047, combined stage P<0.001, and radiation P=5.38e−05 were the significant risk factors related to OS of CCSK patients (Figure 1(a)). Multivariate Cox regression analysis further confirmed that age P=0.0035, origin P=0.025, and combined stage P<0.001 were independent prognostic factors of CCSK survival (Figure 1(b)).Figure 1
Univariate and multivariate Cox regression analyses of clear cell sarcoma of the kidney (CCSK) patient survival in the training cohort. (a) Univariate Cox regression analysis. (b) Multivariate Cox regression analysis.
(a)(b)


## 3.3. Nomogram Establishment and Validation

A nomogram model for predicting 3- and 5-year OS was constructed, which integrated all three independent prognostic parameters (age, origin, and combined stage). Age (nomogram score range: 0–100) was the most important independent prognostic parameter for evaluating OS, followed by combined stage (0–97.5) and origin (0–30) (Figure2). Furthermore, ROC analysis was performed for nomogram validation. The AUC values of 3- and 5-year were 0.733 and 0.728, respectively, in the training cohort, corresponding to 0.690 and 0.674 in the validation cohort (Figures 3(a)–3(d)). The C-index values of the training and validation cohorts were 0.724 and 0.686, respectively. Calibration curve exhibited satisfactory agreement between actual and prediction survival in CCSK patients (Figures 4(a)–4(d)). DCA showed that this nomogram model has great benefits for predicting 3- and 5-year OS time of patients with CCSK within all of threshold probabilities and presented more positive net benefit than the “all” or “none” strategies between the training cohort and validation cohort (Figures 5(a), 5(b)).Figure 2
A nomogram predicting 3- and 5-year overall survival (OS) of CCSK patients in the training cohort.Figure 3
Receiver operating characteristic (ROC) curves of the nomogram for predicting the 3- and 5-year OS of CCSK patients. (a, b) ROC curves in the training cohort. (c, d) ROC curves in the validation cohort.
(a)(b)(c)(d)Figure 4
Calibration curves for predicting the 3- and 5-year OS of CCSK patients. (a, b) The calibration curves in the training cohort. (c, d) The calibration curves in the validation cohort.
(a)(b)(c)(d)Figure 5
Decision curve analysis (DCA) of the nomogram predicting 3- and 5-year OS of CCSK patients. (a) DCA in the training cohort. (b) DCA in the validation cohort.
(a)(b)


## 3.4. Risk Classification System

Enrolled patients with CCSK were divided into three groups: low-, intermediate-, and high-risk groups. The corresponding cutoff points for risk groups were classified as follows: <29.28, 29.28 ≤ nomogram score <60.71, and ≥60.71. In the training cohort, the median OS time of CCSK patients in the low-, intermediate-, and high-risk groups was 76.0, 68.0, and 65.0 months, respectively. In the validation cohort, the median OS time of CCSK patients in the low-, intermediate-, and high-risk groups was 69.5 months, 66.0 months, and 72 months, respectively. Significant OS differences were exhibited among the training, validation, and entire cohortsP<0.05 (Figures 6(a)–6(c)).Figure 6
Comparison of OS in the low-, intermediate-, and high-risk patients with CCSK. (a) Comparison of OS in the training cohort. (b) Comparison of OS in the validation cohort. (c) Comparison of OS in all cohorts.
(a)(b)(c)


## 4. Discussion

CCSK is an aggressive renal malignancy seen in children with poor prognosis [18]. Nomogram is an effective clinical decision-making tool for the prognostic prediction of CCSK patients. In this study, three significant risk factors, including age, origin, and combined stage, were identified based on the SEER database. Nomograms were constructed and validated for the prediction of 3- and 5-year OS of CCSK patients, and we found that age was the most important prognostic factor correlated with OS. Furthermore, a risk classification system was established, and the OS time among low-, intermediate-, and high-risk CCSK patients exhibited significant differences.The prognostic model exhibited in this study was based on 2588 CCSK patients (aged 0–19) included in the SEER database from 2000–2017. We evaluated eight potential prognostic factors related to OS of CCSK patients, including age, gender, race, origin, laterality, combined stage, radiation, and chemotherapy. Through the univariate and multivariate Cox regression analyses, we demonstrated that age, origin, and combined stage were the significant independent prognostic factors closely related with the OS of CCSK patients. It has been reported that young age is a significant unfavorable prognostic factor for CCSK patient survival [19]. Seibel et al. indicated that stage is highly predictive for survival outcome of CCSK patients [20]. Combined with previous studies, we further confirmed that age, origin, and combined stage are three major prognostic factors for predicting OS of CCSK patients.Nomogram is a graphic representation widely applied to depict a statistical prognostic model for clinical events and OS in patients with cancer. Zheng et al. developed and validated a nomogram that can postoperatively evaluate OS and cancer-specific survival of patients with pediatric adrenal cancer [21]. Liu et al. established a novel nomogram with favorable discrimination ability to predict prognosis for newly diagnosed pediatric patients with atypical teratoid/rhabdoid tumors [22]. However, to our knowledge, there is no reliable nomogram for CCSK prognosis. In the present study, we constructed a promising nomogram for predicting the 3- and 5-year OS of CCSK patients based on three independent prognostic factors (age, origin, and combined stage). The nomogram illuminated that age (nomogram score 0–100) is the most significant parameter for CCSK prognosis, followed by combined stage (0–97.5), whereas origin (0–30) presents limited impact on OS outcomes. Subsequently, the prognostic nomogram was validated via ROC analysis. AUC of ROC curve indicates the discrimination ability of a prognostic nomogram [23]. The AUC values of 3- and 5-year ROC curves were, respectively, 0.733 and 0.728 in the training cohort (vs. 0.69 and 0.674 in the validation cohort). This result suggests that the nomogram we established is a stable and reliable prognostic model for CCSK patients. In addition, calibration curves are utilized to evaluate whether the nomogram-predicted survival is consistent with the actual survival of CCSK patients [24]. The C-indexes of calibration curves of the training and validation cohorts were 0.724 and 0.686, respectively, indicating that there is excellent agreement between the nomogram-predicted and the actual survival of CCSK patients. DCA further evaluated the clinical utility of the prognostic nomogram and confirmed its viability and accuracy.Furthermore, a risk classification system was established according to the nomogram risk scores from each CCSK patient. The median OS time in patients with low-risk was 76 months, whereas the OS time of high-risk patients was one year less than that of low-risk patients. This finding indicates that older age and distant stage present a poor survival outcome for high-risk CCSK patients. Indeed, CCSK has the propensity of distant metastasis to other sites, including the bone, brain, lymph nodes, lungs, and liver [25]. Therefore, age and distant stage can be as the independent prognostic factors for predicting the OS of CCSK patients, which provides new guidance for CCSK treatment.


## 5. Conclusion

In summary, a novel and reliable nomogram was established and validated for the prediction of the 3- and 5-year OS time of CCSK patients based on eight potential prognostic factors from the SEER database between 2000 and 2017. Moreover, a risk classification system was established to stratify patients with CCSK into three different risk cohorts, and older age and distant stage in the high-risk patients mean a poor survival outcome. This prognostic model provides bright prospects for survival prediction of CCSK patients and can be applied in clinical practice. The nomogram established in this study can be applied for early diagnostic and prognostic prediction for CCSK, thereby assisting clinicians in making individualized decisions. However, the present study needs to further identify other potential risk factors, such as chemotherapy, radiotherapy, and genetic characteristics. The nomogram we constructed should include more detailed clinical factors for survival prediction of CCSK patients.



---

*Source: 3784300-2022-08-30.xml*