Incidence trends and a nomogram for predicting overall survival in children with hepatoblastoma: A population-based analysis

doi:10.21203/rs.3.rs-1648131/v1

Background

In recent years, the incidence of pediatric hepatoblastoma has increased significantly. The goals of our study were to analyze incidence trends, identify independent risk factors influencing prognosis, and develop a nomogram based on these risk factors to guide clinical treatment.

Methods

Clinicopathological data from children diagnosed with hepatoblastoma between 2000 and 2018 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database to analyze incidence trends. The annual percentage change (APC) was assessed using joinpoint regression analysis. After multiple interpolations, the data selected from 810 children who met the study criteria were randomized into data from the training cohort (n = 568) and validation cohort (n = 242). COX, LASSO, and BSR univariate analyses were used to screen potential risk factors affecting prognosis. Independent risk factors were identified by a multivariate COX regression analysis, and a line-graph model was developed to predict overall survival. The risk score for each child was obtained according to the model. The optimal cutoff value was determined using X-tile software, and the high-risk and low-risk groups were determined. Survival curves were plotted for subgroups using the Kaplan–Meier method. The nomogram was evaluated and verified by the consistency index (C-index) and other analyses: area under the curve (AUC) for receiver operating characteristic (ROC) curve, calibration diagram, and decision curve analysis (DCA).

Results

A total of 810 children with hepatoblastoma were included in the study. The APC was 1.63% (95% confidence interval [CI] -0.6–3.9%, P < 0.05). In particular, boys had a higher incidence than girls; however, the incidence of girls has risen in recent years. The incidence in African-American children has risen. The incidence in children under 2 years old is higher and is on the rise compared to other age groups. Race, age, tumor size, surgical type, and chemotherapy were independent risk factors and thus were selected to develop the nomogram. The time-dependent AUC (> 0.7) and the time-dependent C-index (> 0.7) indicated the satisfactory discriminative ability of the nomogram. The calibration plots showed favorable consistency between the prediction of the nomogram and actual observations in both the training and validation cohorts. Furthermore, DCA showed that the nomogram was clinically useful and had a discriminative ability. We developed a web-based calculator that implements a dynamic nomogram that automatically generates a survival curve (with a 95% confidence interval [CI]) after inputting clinical data for the patient. Using a smartphone, clinicians can access the calculator to predict the prognosis of an individual patient and guide treatment decisions.

Conclusions

The incidence of pediatric hepatoblastoma has increased in recent years, according to data from the SEER database. We developed a nomogram to predict prognosis and guide treatment. A combined treatment approach of surgery and chemotherapy is highly likely to prolong survival and improve patient outcomes.

SEER

Hepatoblastoma

Nomogram

Incidence

Overall survival

Hepatoblastoma is the most common malignant primary liver tumor in children, accounting for approximately 80% of all pediatric liver tumors. While in essence, it is a malignant embryoblastoma, the specific etiology of hepatoblastoma is still unclear. It is mostly seen in children under 2 years old [1]. Hepatoblastoma has a high degree of malignancy, metastasizes extensively through the blood and lymph nodes, and has a short survival period. At present, effective treatment for hepatoblastoma includes chemotherapy, hepatectomy, and liver transplantation [2]. The survival rate has increased from 30–70% in the past 30 years, mainly due to improvements in imaging technology, surgical methods, the timing of intervention, and chemotherapy regimens [3–5].

Although some studies have reported on the prognosis and related influencing factors for pediatric hepatoblastoma, previous studies were mostly limited to single-center retrospective analyses with small sample sizes due to the relative rarity of this disease [6]; few studies considered the effects of chemotherapy. Therefore, our study chose a relatively large sample size from the Surveillance, Epidemiology, and End Results (SEER, National Cancer Institute, Bethesda, MD, USA) database for analysis. The SEER database is the most authoritative cancer statistics database in the United States, recording the morbidity, mortality, and morbidity of millions of malignant tumor patients, accounting for approximately 35% of the U.S. population. We retrospectively analyzed clinicopathological data from the SEER database for children diagnosed with hepatoblastoma from 2000 to 2018 to explore incidence trends, identify independent risk factors affecting prognosis, and develop a nomogram based on these risk factors. The nomogram is a reliable statistical model that can be used for predicting long-term survival and guiding treatment decisions. As a convenient tool for clinicians, we developed a web-based calculator that implements the nomogram and can be accessed with a smartphone to predict prognosis and help select effective treatment for children with hepatoblastoma [7, 8].

Patient Data

In our retrospective study, clinicopathological data from children diagnosed with hepatoblastoma from 2000 to 2018 were collected from the SEER database using SEER* Stat 8.3.9 software. The study inclusion criteria were as follows: ① First-time diagnosis of hepatoblastoma; ② Histological type ICD-O-3 Hist/Behave = "8970: Hepatoblastoma"; ③ Age ≤ 18 years old; and ④ Survival time with a clear cause of death. We excluded cases with survival times of less than 30 days. A total of 810 children met the above criteria and were included in the study. The R software MICE program package was used for multiple interpolations of missing data. The R software“CreateDatPartition” function was used to randomize patients into a training cohort (n = 568) and a validation cohort (n = 242) at a ratio of 7:3. A descriptive analysis of trends in the incidence of hepatoblastoma from 2000 to 2018 was carried out. Overall survival was measured in months from the date of diagnosis to the date of last follow-up or death.

Nine variables were selected from the SEER database: ① Sex: female or male; ② Race: white, black, or other; ③ Age: ≤2 years old or >2 years old; ④ Diagnosis Year: 2000–2009 or 2010–2018; ⑤ Tumor stage: local, regional, or distant; ⑥ Alpha-fetoprotein (AFP): positive or negative; ⑦ Tumor size: ≤ 5cm or >5cm; ⑧ Surgery type: no surgery, liver resection, or liver transplantation; and ⑨ Chemotherapy: chemotherapy or no chemotherapy.

Statistical analysis

Standardized incidence and clinical data were extracted from the SEER database. The annual percentage change (APC) in incidence was calculated. The Joinpoint regression analysis (V.4.6.0, National Cancer Institute, Bethesda, MD, USA) was used to analyze incidence trends. The log-rank test was used to determine whether the survival comparison was statistically significant, and the results were expressed by the Kaplan–Meier method. Using the” survminer” package in R software, three regression models (COX, LASSO, and BSR univariate analyses) were used to screen potential risk factors affecting prognosis. The results of the three univariate analyses were included in the COX regression multifactor analysis to determine the independent risk factors. Regression selection variables were based on the minimum value of the Akaike information criterion (AIC). The RMS and other program packages in R4.1.1 software were used to construct and verify the line-graph model [9].

A risk score was obtained for each patient according to the model. X-tile software was used to select the best truncation value of the score and determine the high-risk and low-risk subgroups. The Kaplan–Meier method was used to plot survival curves for subgroups. The value of the area under the curve (AUC) was calculated by drawing the receiver operating characteristic (ROC) curve, and the regression model was constructed. The consistency index (C-index) value was calculated by the bootstrapping method. If the C-index and AUC values were greater than 0.7, the model had good differentiation. We plotted the calibration curve and it could be seen from the graph that a higher degree of fitting between the prediction model curve and the 45° diagonals meant a better consistency between the predicted probability and actual probability. The decision curve analysis (DCA) showed the clinical application value of the model. A web-based calculator was developed to realize the dynamic line-graph, and the network server would automatically generate the survival probability curve and 95% confidence interval (CI) of the patient after inputting the clinical data of the patient. Test level α = 0.05.

Characteristics of patients and disease

A total of 810 children diagnosed with hepatoblastoma between 2000 and 2018 were identified from the SEER database. Boys accounted for 61.6% (n=499) of the patients and girls for 38.4% (n=311), with a male-to-female ratio of 1.6 (Table 1). There were 641 children ≤ 2 years old and 169 children ＞ 2 years old. We found that the overall incidence of hepatoblastoma increased from 2000 to 2018, and the APC was 1.63% (95% CI: -0.6% to 3.9%, P<0.05). Boys had a higher incidence than girls. The incidence of girls has increased in recent years, and the APC was 3.01% (95% CI: -1.0% to 7.2%, P<0.05). The incidence rate of African American children had increased, and the APC was 55.64% (95% CI: 1.0% to 139.9%, P<0.05). The incidence rate of children aged under 2 years old was higher than that of other age groups, and the APC was 5.39% (95% CI: 0.5% to 10.5%, P<0.05) (Figure 1).

Table 1: Characteristics of children with hepatoblastoma who were included in the study

Characteristic	Whole population [cases (%)]	Training cohort [cases (%)]	Validation cohort [cases (%)]	P value
Total	n=810	n=568	n=242
Sex 0.3781
Female	311 (38.40)	212 (37.32)	99 (40.91)
Male	499 (61.60)	356 (62.68)	143 (59.09)
Race 0.6667
White	616 (76.05)	436 (76.76)	180 (74.38)
Black	70 (8.64)	46 (8.10)	24 (9.92)
Other	124 (15.31)	86 (15.14)	38 (15.70)
Age 0.8489
0-2 years old	641 (79.14)	451 (79.40)	190 (78.51)
＞2 years old	169 (20.86)	117 (20.60)	52 (21.49)
Diagnosis year 0.6199
2000-2009	384 (47.41)	273 (48.06)	111 (45.87)
2010-2018	426 (52.59)	295 (51.94)	131 (54.13)
Tumor_stage 0.4695
Local	377 (46.54)	257 (45.25)	120 (49.59)
Reginal	244 (30.12)	173 (30.46)	71 (29.34)
Distant	189 (23.33)	138 (24.30)	51 (21.07)
AFP 1.0
Positive	786 (97.04)	551 (97.01)	235 (97.11)
Negative	24 (2.96)	17 (2.99)	7 (2.89)
Tumor_size 1.0
≤5cm	172 (21.23)	121 (21.30)	51 (21.07)
＞5cm	638 (78.77)	447 (78.70)	191 (78.93)
Surgery_type 0.9933
No surgery	152 (18.77)	106 (18.66)	46 (19.01)
Liver resection	527 (65.06)	370 (65.14)	157 (64.88)
Liver transplantation	131 (16.17)	92 (16.20)	39 (16.12)
Chemotherapy 1.0
Yes	740 (91.36)	519 (91.37)	221 (91.32)
No	70 (8.64)	49 (8.63)	21 (8.68)
Status 0.7172
Alive	644 (79.51)	454 (79.93)	190 (78.51)
Dead	166 (20.49)	114 (20.07)	52 (21.49)
Survival_time	61.5[18.0, 120.75]	65.0[19.0, 123.25]	56.0[17.0, 110.0]	0.229

Nomogram variable screening

The COX univariate regression analysis showed that risk factors for pediatric hepatoblastoma were race, age, tumor stage, tumor size, surgery type, and chemotherapy (P < 0.05) (Table 2). The LASSO univariate regression analysis indicated that the risk factors were sex, age, diagnosis year, tumor stage, tumor size, surgery type, and chemotherapy (Figures 2a, 2b). The BSR univariate regression analysis showed that the risk factors were sex, age, diagnosis year, tumor size, surgery type, and chemotherapy (Figure 2c). The results of the three univariate analyses were included in COX multivariate regression, and the independent risk factors for hepatoblastoma were determined. The AUC of COX (AIC=1264, Figure 3a) and BSR (AIC=1260.87, Figure 3b) was 80.2% (Figure 3d). In addition, 80.2% of AUC was superior to LASSO (AIC=1267.96, Figure 3c), and COX was consistent with the independent risk factors identified by the BSR analysis, which included race, age, tumor size, surgery type, and chemotherapy. Therefore, the above variables were used for further analysis and modeling.

Table 2: COX univariate regression analysis for predicting overall survival in children with hepatoblastoma

Variable	HR （95% CI）	P value
Sex
Female	1.00
male	1.2 (0.82-1.77)	0.351
Race
White	1.00
Black	2.22 (1.3-3.79)	0.003
Other	0.87 (0.49-1.53)	0.619
Age
0-2 years old	1.00
＞2 years old	1.69 (1.13-2.51)	0.01
Diagnosis_year
2000-2009	1.00
2010-2018	0.76 (0.52-1.1)	0.146
Tumor_stage
Local	1.00
Reginal	1.44 (0.91-2.26)	0.12
Distant	2 (1.28-3.13)	0.002
AFP
Positive	1.00
Negative	0.95 (0.3-2.99)	0.932
Tumor_size
≤5cm ＞5cm	1.00
＞5cm ＞5cm	1.83 (1.08-3.11)	0.024
Surgery_type
No surgery	1.00
Liver resection	0.12 (0.08-0.18)	0
Liver transplantation	0.15 (0.08-0.27)	0
Chemotherapy
Yes	1.00
No	4.09 (2.58-6.47)	0

Nomogram construction

R4.1.1 software (the "RMS" package) was used to identify independent risk factors affecting prognosis. The nomogram was constructed and verified by ranking each variable in importance according to the standard deviation of the nomogram scale. Each variable reflects the corresponding point on the relevant variable axis of the column chart, and the point is used as the vertical line of the variable axis to correspond to the upper score scale to obtain the score of the variable. The total score was obtained by summation of the scores for each variable. The total score corresponded to the points on the axis of the 1-, 3-, and 5-year overall survival (OS) rates, which represent the children's predicted survival rate (Figure 4).

The "DynNom" program package in R4.1.1 software was used to create a web-based calculator to realize a dynamic line-graph, allowing for the input of specific variables to calculate the possibility of events and increase the practicability of the line-graph prediction model. Direct access to the application https://yixuexiaodaotong.shinyapps.io/DynNomapp/ can be formulated into our web-based calculator to realize the dynamic model of the nomogram. As shown in Figure 5a, the abscissa represents follow-up time (month) and the ordinate represents survival probability. The network server will then automatically generate the patient's survival probability curve and 95% CI after inputting the patient's clinical data. The OS of the patient can be easily predicted (Figure 5).

Risk stratification based on the nomogram

Finally, we stratified risk based on total points calculated using the nomogram and X-tile software to calculate the optimal truncation value. Children with hepatoblastoma were divided into two risk groups: low risk (total points < 119) and high risk (total points ≥ 119). Kaplan–Meier curves showed significant differences between the two groups (P ＜ 0.0001) (Figure 6).

Nomogram evaluation and validation

The C-index of the training cohort (Figure 7a) and validation cohort (Figure 7b) was plotted over time. A value greater than 0.7 indicated that the line-graph prediction model had a good degree of discriminative ability. The time-dependent AUC of OS at 10 years was calculated, and the OS probability of both the predicted training cohort (Figure 7c) and the validation cohort (Figure 7d) at 10 years was > 0.7, indicating that the line-graph prediction model had a good degree of discriminative ability. The calibration curve was drawn, and the OS curve that was obtained from the prediction model had a high fit degree with the 45° diagonal line in the calibration graph, suggesting a good consistency between the predicted survival rate and the actual survival rate. The training cohort calibration curve for the 3-year survival rate is shown in Figure 7e and for the 5-year survival rate in Figure 7f. The calibration curve for the 3-year survival rate in the validation cohort is shown in Figure 7g and for the 5-year survival rate in Figure 7h. Decision curve analysis was used to evaluate the clinical effectiveness of the model by calculating the net benefit. The farther the decision curve is from the X and Y axes, the more practical the model will be. Decision curves of 3-year and 5-year survival benefits of the training cohort are shown in Figure 7i. Calibration curves to verify the 3-year and 5-year survival benefits of the cohort are shown in Figure 7j.

Survival

The difference in the survival of 0–2 years old children with chemotherapy versus without chemotherapy was statistically significant throughout the survival curve (P < 0.0001). Children aged 0 to 2 years old with chemotherapy had a much longer survival time than children who did not receive chemotherapy. However, for children more than 2 years old, there was no statistically significant difference in survival whether they received chemotherapy or not (P > 0.05) (Figure 8a). Children who received chemotherapy had a significantly longer survival time as the disease progressed than those who did not (P < 0.05) (Figure 8b). The survival times of children with chemotherapy with tumor sizes larger or smaller than 5cm were significantly longer than those of children without chemotherapy (P < 0.05) (Figure 8c). The survival time for children treated nonsurgically with chemotherapy was significantly longer than the survival time for children treated without chemotherapy (P < 0.05). There was no statistically significant difference in survival times between chemotherapy and no chemotherapy for children who underwent hepatectomy or liver transplantation (P ＞ 0.05) (Figure 8d). Children with chemotherapy treated with hepatectomy and without surgery had significantly longer survival times for tumors ≥ 5cm, compared to nonchemotherapy patients (P < 0.05). The survival time for patients treated with liver transplantation with and without chemotherapy was not significantly different (P > 0.05). For tumors < 5cm, the survival time without surgical treatment was significantly longer with chemotherapy than without chemotherapy (P < 0.05). The survival times for hepatectomy and liver transplantation were not significantly different with or without chemotherapy (P > 0.05) (Figure 8e).

As of 40 years ago, the prognosis of hepatoblastoma was very poor. The survival rate was less than 30% and children usually died within 6 months after diagnosis. Children with multiple distant metastases could not be treated with radical surgery. After 2000, chemotherapy was applied clinically and the survival rate began to improve [10–12].

The SEER database is the authoritative cancer statistics database in the United States, which records the morbidity, mortality, and morbidity of millions of malignant tumor patients in approximately 35% of the U.S. population. It can provide powerful data support for clinical research and has high clinical application value. We included 810 children with hepatoblastoma in our study, which is the largest number of cases included in a research study to date. The findings are highly reliable and representative, with a high clinical guiding value. The findings revealed that the overall incidence of hepatoblastoma in children has increased in recent years, with the race being one of the independent risk factors, which is a worth noting finding.

In this study, a nomogram was constructed to predict the prognosis of hepatoblastoma. Nomograms have been developed for some cancers and are accepted as reliable tools for the prediction of risk for individuals [13–15]. Both internal and external validation results showed good differentiation and calibration of the model. In order to develop a useful tool for clinical work, we developed a web-based calculator so that clinicians can calculate the predicted survival rate by using smartphones. In the future, more small applications that are easy to use in clinical work should be developed.

The results of this study determined that race, age, tumor size, surgical type, and chemotherapy were independent risk factors affecting the prognosis of children with hepatoblastoma. The results of this study were basically consistent with previous studies, but not completely identical. Most children have abnormally elevated AFP levels, which is one of the most important diagnostic criteria for hepatoblastoma. Our results showed that neither AFP nor distant metastasis was an independent risk factor affecting the prognosis of children with hepatoblastoma, which was consistent with the results of Feng et al. [16] based on the SEER database. However, the results of Zhi et al. [17] showed that AFP and distant metastasis were independent risk factors affecting the prognosis of children with hepatoblastoma. This study was a single-center study of children with hepatoblastoma under 6 years old in China. It may also be related to chemotherapy factors, and more studies are needed to confirm our hypothesis. Studies on different age groups, different regions, different treatment methods, and larger sample sizes should become the focus of our future research.

Surgery and chemotherapy are the main treatment methods for hepatoblastoma. However, due to the occult onset of the disease, most children have lost the opportunity to undergo tumor resection by the time they are diagnosed. Neoadjuvant chemotherapy can reduce tumor volume and inhibit metastasis, and the combined application of surgery and chemotherapy improves the survival rate of children with hepatoblastoma [18]. This study showed that children with hepatoblastoma who received nonsurgical treatment and who were given chemotherapy had a significantly longer survival time than that of children without chemotherapy (P < 0.05). However, there was no statistically significant difference between children who underwent liver resection and liver transplantation, regardless of whether or not they had chemotherapy (P > 0.05); however, the sample size of children with hepatoblastoma who underwent liver transplantation and chemotherapy was small that may have influenced the results to some extent. Only a small number of children with hepatoblastoma registered in the SEER database have undergone liver transplantation, and all of them are children without chemotherapy, for which there are currently some limitations. However, it does not mean that chemotherapy is not needed after liver transplantation, and this data needs to be further improved by the database over time. This finding is consistent with the results of McAteer et al. [19] At present, there is no high-quality comparative study on the efficacy of liver transplantation and traditional surgery for children with advanced hepatoblastoma, and there is a lack of long-term follow-up data. In addition, there is controversy over whether liver transplantation can be performed for hepatoblastoma with lung metastasis [20].

According to the tumor tissue morphology defined by the International Collaboration of Children's Hepatic Tumors (HB, CHIC) [21], pathological tissues were classified into epithelial and mixed types. Among them, the epithelial type is divided into fetal type, embryonic type, giant beam type, and small cell undifferentiated type. In previous studies, the prognosis of different pathological types of hepatoblastoma was inconsistent. We decided to focus on analyzing and verifying the correlation in future studies.

There are some limitations to our study: ① The study was retrospective so the included data may be biased, which requires further verification by more prospective studies; ② The SEER database lacks information about other factors that may affect the prognosis of children with hepatoblastoma. Many other studies have found that the PRETEXT stage, tumor rupture, incomplete tumor resection, and other factors have important prognostic value [22–26]. ③ The data in this study were from American children. The applicability of the results to children in other countries remains to be confirmed. ④ Although the data in the SEER database included 18 cancer registries, the sample size was still only 810, and future studies with larger sample sizes are needed.

We demonstrated recent trends in the incidence of hepatoblastoma in children, as well as a nomogram designed to accurately predict the prognosis of hepatoblastoma in children. We found that chemotherapy has benefits for the treatment of hepatoblastoma; further research into chemotherapy should be conducted in the future.

Acknowledgments

This work was supported by the Natural Science Foundation of the Jilin Province Science and Technology Department（20190201054JC）

The authors would like to thank all the reviewers who participated in the review and MJEditor (www.mjeditor.com) for its linguistic assistance during the preparation of this manuscript.

Compliance with ethical standards

Disclosures Shan-shan Dong, Kun Wang, Fang Xie, Jun-chao Wang, Xing-hui Wang, Wei Zhang, Yuan-yuan Ni, and Yue-wei Li have no conflicts of interest or financial ties to disclose.

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No competing interests reported.

Incidence trends and a nomogram for predicting overall survival in children with hepatoblastoma: A population-based analysis

Status:

Version 1

Abstract

Background

Methods

Results

Conclusions

Figures

Background

Methods

Patient Data

Statistical analysis

Results

Discussion

Conclusions

Declarations

References

Additional Declarations

Status:

Version 1