# Establishment of a Survival Risk Prediction Model for Adolescent and Adult Osteosarcoma Patients

Authors: Zhixiang Gao; Kai Yao; Peng Cai; Nengji Long; Yang Cao; Fenglai He; Lijuan Liu; Cong Xiao
Journal: BioMed Research International
 (2022)

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## Abstract

To build a nomogram model for predicting the survival risk of teens and adults with osteosarcoma based on the TARGET database, patients with osteosarcoma were collected via the TARGET database, and the survival curves of the patients were plotted using the Kaplan-Meier method in SPSS 24.0. Least absolute shrinkage and selection operator (LASSO) univariate regression analysis was performed to identify risk factors that influence osteosarcoma survival. A model (nomogram) for predicting the survival risk of adolescent and adult patients with osteosarcoma was built or plotted using the rms26 package as implemented in R (ver. 3.5.3). The predictive accuracy and discriminating power of the nomogram were determined by the C-index and calibration curves. A total of 94 patients with osteosarcoma were included. Classification of cases based on the tumor site revealed 59 cases involving the femur (62.8%), 5 involving the fibula (5.3%), 6 humerus (6.4%), 2 radius (2.1%), 19 tibia (20.2%), and 3 ulna (3.2%). Classification of cases based on surgical method showed 81 cases involving limb sparing (86.2%), 9 cases of amputation (9.6%), and 4 without surgery (4.2%). Among the 94 cases, bone metastasis occurred in 3 cases (3.2%) and lung metastasis in 14 cases (14.9%). Among all survivors, the median rate of survival is 8.6 years (95% CI: 8.0210.92); the three-year and five-year survival rates are 64.6% and 52.6%, respectively. The LASSO regression analysis showed that metastasis site, definitive surgery, and histologic response were potential risk predictors. The C-index of the nomogram plotted was 0.729, and the C-index of the validated sample was 0.742. The nomogram used in this study allows physicians to objectively and accurately predict the prognosis and survival of osteosarcoma patients. In order to determine whether the method is applicable to other groups of patients, additional studies need to be conducted.


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## Body

## 1. Introduction

Osteosarcoma, also known as osteogenic sarcoma, is a common malignant bone tumor in children, accounting for approximately 5% of all tumors in this population [1]. Osteosarcomas are the most frequent in adolescents and children, with 75% of patients within the age range of 15-25 years [1]. Osteosarcomas are most commonly found in the distal femur or the proximal humerus, mainly in the medullary cavity. These are highly malignant, with mortality and disability rates exceeding 90% [1]. The annual global incidence of osteosarcoma is 0.005‰, while its incidence in China is relatively low, between 0.002‰ and 0.003‰ [1, 2]. Before the 1970s, prognosis was poor for these patients, with an overall survival rate below 20% [3]. Nevertheless, since the introduction of comprehensive treatment for osteosarcoma (preoperative neoadjuvant chemotherapy + limb salvage surgery + postoperative neoadjuvant chemotherapy) in the 1970s, the overall survival rate for 5 years has risen to >50% [4]. The clinical manifestations and histopathology of osteosarcoma in adolescents and adults are well described, yet reports on the factors influencing survival and prognosis are very inconsistent. Moreover, no survival risk prediction model for osteosarcoma has been developed to date.Risk prediction models yield risk values by constructing the probability of a certain outcome through the assessment of risk factors. These include a training queue, which is a sample that is analyzed in terms of risk factors and used in the construction of the model, and a validation queue, which is the sample used in the verification of the predictive effect of the model. These queues provide a more intuitive and scientific theoretical basis for the clinical assessment of disease risk. In this study, we preliminarily established an osteosarcoma survival risk prediction model map by mining the related database of osteosarcoma. The end result is a theoretical basis for estimating survival rates for osteosarcoma patients in clinical management.


## 2. Materials and Methods

### 2.1. Patients and Samples

In all, 216 cases of osteosarcoma were obtained from the TARGET database (https://ocg.cancer.gov/). Only 94 cases remained included after the application of the following exclusion criteria: (1) incomplete reports of age and survival days; (2) malignant tumors in other sites; and (3) incomplete data analysis of patient samples.


### 2.2. Research Methods and Statistical Analysis

All cases included data regarding age, gender, race, metastasis site, tumor site, definitive surgery, histologic response, and therapy, which were expressed in percentages (%) or constituent ratios. Data were analyzed with SPSS 24.0 (SPSS Corp., USA) and R software (ver. 3.5.3;https://www.R-project.org). Approximately 70% of all cases were randomly selected as training samples, and the remaining 30% were validation samples [5].The overall survival rate of all cases was analyzed using the Kaplan-Meier survival curve included in SPSS 24.0. Least absolute shrinkage and selection operator (LASSO) single-factor analysis was used to screen for the optimal risk factor affecting the survival of osteosarcoma in 70% of all patients in the training queue established by the model [5]. LASSO regression reduces some coefficients by constructing a penalty function and at the same time makes the coefficients with smaller absolute values be 0 to filter the characteristic variables and effectively reduce the complexity of the model. The estimated coefficient of the LASSO regression model is β^=argmin∑i=1nyiηβXi−ln1+expηβXi+λ∑j=1pβj, where the parameter λ represents the complexity of the LASSO regression model. The larger the value of λ, the fewer the variables included in the model. LASSO regression overcomes the limitations of the logistic regression stepwise selection method and retains the advantages of ridge regression and subset regression [5–7]. The optimal risk factor screened by LASSO regression and the factors considered to be the most influential on survival risk based on clinical experience with osteosarcoma cases were analyzed by Cox proportional risk regression to identify the high-risk factors. The nomogram was plotted with the rms26 software package in R software. The nomogram of the survival risk prediction model for osteosarcoma in adolescents and adults was constructed, and the C-index of the nomogram was calculated. Calibration curves were used to validate the internal prediction model. The closer the calibration curve to the standard line, the greater the accuracy of the model graph [8].Thirty percent of all cases were used as validation samples to validate the nomogram. With version 3.5.3 of the Hmisc28 package, we calculated the C-index of the validation samples. The nomogram model was considered accurately constructed when the C-index of the validation samples was larger than that of the model diagrams.


## 2.1. Patients and Samples

In all, 216 cases of osteosarcoma were obtained from the TARGET database (https://ocg.cancer.gov/). Only 94 cases remained included after the application of the following exclusion criteria: (1) incomplete reports of age and survival days; (2) malignant tumors in other sites; and (3) incomplete data analysis of patient samples.


## 2.2. Research Methods and Statistical Analysis

All cases included data regarding age, gender, race, metastasis site, tumor site, definitive surgery, histologic response, and therapy, which were expressed in percentages (%) or constituent ratios. Data were analyzed with SPSS 24.0 (SPSS Corp., USA) and R software (ver. 3.5.3;https://www.R-project.org). Approximately 70% of all cases were randomly selected as training samples, and the remaining 30% were validation samples [5].The overall survival rate of all cases was analyzed using the Kaplan-Meier survival curve included in SPSS 24.0. Least absolute shrinkage and selection operator (LASSO) single-factor analysis was used to screen for the optimal risk factor affecting the survival of osteosarcoma in 70% of all patients in the training queue established by the model [5]. LASSO regression reduces some coefficients by constructing a penalty function and at the same time makes the coefficients with smaller absolute values be 0 to filter the characteristic variables and effectively reduce the complexity of the model. The estimated coefficient of the LASSO regression model is β^=argmin∑i=1nyiηβXi−ln1+expηβXi+λ∑j=1pβj, where the parameter λ represents the complexity of the LASSO regression model. The larger the value of λ, the fewer the variables included in the model. LASSO regression overcomes the limitations of the logistic regression stepwise selection method and retains the advantages of ridge regression and subset regression [5–7]. The optimal risk factor screened by LASSO regression and the factors considered to be the most influential on survival risk based on clinical experience with osteosarcoma cases were analyzed by Cox proportional risk regression to identify the high-risk factors. The nomogram was plotted with the rms26 software package in R software. The nomogram of the survival risk prediction model for osteosarcoma in adolescents and adults was constructed, and the C-index of the nomogram was calculated. Calibration curves were used to validate the internal prediction model. The closer the calibration curve to the standard line, the greater the accuracy of the model graph [8].Thirty percent of all cases were used as validation samples to validate the nomogram. With version 3.5.3 of the Hmisc28 package, we calculated the C-index of the validation samples. The nomogram model was considered accurately constructed when the C-index of the validation samples was larger than that of the model diagrams.


## 3. Results

### 3.1. Clinical Features and Overall Survival Rate of Osteosarcoma Patients

In the 94 osteosarcoma patients studied, the median overall survival time was 8.6 years (95% CI: 8.02-10.92). Kaplan-Meier analysis showed the cumulative three-year and five-year survival rates which were 64.6% and 52.6%, respectively (Figure1). Assessment of tumor location showed that 59 cases were in the femur (62.8%), 5 in the fibula (5.3%), 6 in the humerus (6.4%), 2 in the radius (2.1%), 19 in the tibia (20.2%), and 3 (3.2%) in the ulna. According to the surgical methods, 81 cases spared the limb (86.2%), 9 cases underwent amputation (9.6%), and 4 cases were nonoperational (4.2%). Of the 94 cases, bone metastasis occurred in only 3 cases (3.2%), whereas lung metastasis occurred in 14 cases (14.9%). The patient data are displayed in Table 1.Figure 1
Kaplan-Meier analysis showed the cumulative three-year and five-year survival rates.Table 1
Osteosarcoma patients’ clinical characteristics.
Factorsn (%)Death (n, %)Age<1869 (74.3)28 (40.6)≥1825 (26.6)9 (36.0)GenderFemale42 (44.7)12 (28.6)Male52 (55.3)25 (48.1)RaceAmerican Indian or Alaska Native1 (1.1)1 (100.0)Asian31 (33.0)14 (45.2)Black or African American10 (10.6)2 (20.0)White52 (55.3)20 (38.5)Metastasis siteBone3 (3.2)2 (66.7)Lung14 (14.9)9 (64.3)NA77 (81.9)26 (33.8)Tumor siteFemur59 (62.8)23 (39.0)Fibula5 (5.3)2 (40.0)Humerus6 (6.4)4 (66.7)Radius2 (2.1)1 (50.0)Tibia19 (20.2)5 (26.3)Ulna3 (3.2)2 (66.7)Definitive surgeryLimb sparing81 (86.2)29 (35.8)Amputation9 (9.6)5 (55.6)NA4 (4.2)3 (75.0)Histologic responseGood34 (36.2)8 (23.5)Poor60 (63.8)29 (48.3)TherapyYes75 (79.8)29 (38.7)No19 (20.2)8 (42.1)


### 3.2. Screening of Characteristic Variables Influencing the Survival Rate of Osteosarcoma Patients

Single-factor LASSO regression analysis indicated that three potential predictive risk factors were found among eight clinical risk factors, namely, metastasis site, definitive surgery, and histologic response (3 : 1 ratio; Figures2(a) and 2(b)). In addition, our literature review showed that age, tumor site, and therapy are also risk factors influencing the survival of osteosarcoma patients. Therefore, six risk factors, metastasis site, definitive surgery, histologic response, age, tumor site, and therapy, were analyzed with Cox proportional risk regression.Figure 2
(a and b) Single-factor LASSO regression analysis indicated that three potential predictive risk factors were found among eight clinical risk factors, namely, metastasis site, definitive surgery, and histologic response.
(a)(b)


### 3.3. Multivariate Analysis of Factors Influencing the Survival Rate of Osteosarcoma Patients

Multivariate Cox proportional risk regression analysis showed that tumor site had no effect on the survival of osteosarcoma patients, whereas age, metastasis site, definitive surgery, histologic response, and therapy were independent predictors of survival of osteosarcoma patients (Table2).Table 2
An analysis of 94 patients with osteosarcoma using COX regression.
FactorORPAge2.670.045Metastasis site1.2030.004Definitive surgery3.630.003Histologic response0.990.01Therapy1.840.02


### 3.4. Prognostic Nomogram for Osteosarcoma

Using the independent predictors found in the multivariate Cox proportional risk regression analysis, a survival risk prediction map of osteosarcoma was drawn (Figure3). After extracting the corresponding score from each risk factor and calculating the total score, the three-year and five-year survival risk prediction probability of osteosarcoma patients corresponding to the total score could be obtained. The higher the total score, the lower the three-year and five-year survival rate of osteosarcoma patients. The C-index for osteosarcoma prediction was 0.729. Internal validation of the calibration curve for three-year and five-year survival risk in osteosarcoma patients showed that if the nomogram was close to the standard line, then the model map was accurate and was of predictive value (Figures 4(a) and 4(b)).Figure 3
Cox proportional risk regression analysis, a survival risk prediction map of osteosarcoma; the nomogram of the survival risk of osteosarcoma patients.Figure 4
Internal validation of the calibration curve for three-year (a) and five-year (b) survival risk in osteosarcoma patients.
(a)(b)


### 3.5. Validation of the Predictive Model Diagrams

Thirty percent of the 94 osteosarcoma patients retrieved from the TARGET database were selected as validation samples and used to validate the nomogram of the survival risk of osteosarcoma patients (Figure3). We found that the C-index (0.742) of the validated samples was larger than that for osteosarcoma prediction (0.729), indicating that the external validation of the osteosarcoma patient survival risk prediction map had certain clinical predictive value.


### 3.6. Clinical Use

Through internal and external validation of the C-index with calibration curves, we concluded that the nomogram of the survival risk of osteosarcoma patients had good predictive value. Therefore, the survival curve was plotted using the R software (ver. 3.5.3) after classifying the risk factors in the nomogram graph of survival risk. We found the difference between high-risk factors and low-risk factors significant (P=0.0119, Figure 5).Figure 5
The difference between high-risk factors and low-risk factors significant (P=0.0119).


## 3.1. Clinical Features and Overall Survival Rate of Osteosarcoma Patients

In the 94 osteosarcoma patients studied, the median overall survival time was 8.6 years (95% CI: 8.02-10.92). Kaplan-Meier analysis showed the cumulative three-year and five-year survival rates which were 64.6% and 52.6%, respectively (Figure1). Assessment of tumor location showed that 59 cases were in the femur (62.8%), 5 in the fibula (5.3%), 6 in the humerus (6.4%), 2 in the radius (2.1%), 19 in the tibia (20.2%), and 3 (3.2%) in the ulna. According to the surgical methods, 81 cases spared the limb (86.2%), 9 cases underwent amputation (9.6%), and 4 cases were nonoperational (4.2%). Of the 94 cases, bone metastasis occurred in only 3 cases (3.2%), whereas lung metastasis occurred in 14 cases (14.9%). The patient data are displayed in Table 1.Figure 1
Kaplan-Meier analysis showed the cumulative three-year and five-year survival rates.Table 1
Osteosarcoma patients’ clinical characteristics.
Factorsn (%)Death (n, %)Age<1869 (74.3)28 (40.6)≥1825 (26.6)9 (36.0)GenderFemale42 (44.7)12 (28.6)Male52 (55.3)25 (48.1)RaceAmerican Indian or Alaska Native1 (1.1)1 (100.0)Asian31 (33.0)14 (45.2)Black or African American10 (10.6)2 (20.0)White52 (55.3)20 (38.5)Metastasis siteBone3 (3.2)2 (66.7)Lung14 (14.9)9 (64.3)NA77 (81.9)26 (33.8)Tumor siteFemur59 (62.8)23 (39.0)Fibula5 (5.3)2 (40.0)Humerus6 (6.4)4 (66.7)Radius2 (2.1)1 (50.0)Tibia19 (20.2)5 (26.3)Ulna3 (3.2)2 (66.7)Definitive surgeryLimb sparing81 (86.2)29 (35.8)Amputation9 (9.6)5 (55.6)NA4 (4.2)3 (75.0)Histologic responseGood34 (36.2)8 (23.5)Poor60 (63.8)29 (48.3)TherapyYes75 (79.8)29 (38.7)No19 (20.2)8 (42.1)


## 3.2. Screening of Characteristic Variables Influencing the Survival Rate of Osteosarcoma Patients

Single-factor LASSO regression analysis indicated that three potential predictive risk factors were found among eight clinical risk factors, namely, metastasis site, definitive surgery, and histologic response (3 : 1 ratio; Figures2(a) and 2(b)). In addition, our literature review showed that age, tumor site, and therapy are also risk factors influencing the survival of osteosarcoma patients. Therefore, six risk factors, metastasis site, definitive surgery, histologic response, age, tumor site, and therapy, were analyzed with Cox proportional risk regression.Figure 2
(a and b) Single-factor LASSO regression analysis indicated that three potential predictive risk factors were found among eight clinical risk factors, namely, metastasis site, definitive surgery, and histologic response.
(a)(b)


## 3.3. Multivariate Analysis of Factors Influencing the Survival Rate of Osteosarcoma Patients

Multivariate Cox proportional risk regression analysis showed that tumor site had no effect on the survival of osteosarcoma patients, whereas age, metastasis site, definitive surgery, histologic response, and therapy were independent predictors of survival of osteosarcoma patients (Table2).Table 2
An analysis of 94 patients with osteosarcoma using COX regression.
FactorORPAge2.670.045Metastasis site1.2030.004Definitive surgery3.630.003Histologic response0.990.01Therapy1.840.02


## 3.4. Prognostic Nomogram for Osteosarcoma

Using the independent predictors found in the multivariate Cox proportional risk regression analysis, a survival risk prediction map of osteosarcoma was drawn (Figure3). After extracting the corresponding score from each risk factor and calculating the total score, the three-year and five-year survival risk prediction probability of osteosarcoma patients corresponding to the total score could be obtained. The higher the total score, the lower the three-year and five-year survival rate of osteosarcoma patients. The C-index for osteosarcoma prediction was 0.729. Internal validation of the calibration curve for three-year and five-year survival risk in osteosarcoma patients showed that if the nomogram was close to the standard line, then the model map was accurate and was of predictive value (Figures 4(a) and 4(b)).Figure 3
Cox proportional risk regression analysis, a survival risk prediction map of osteosarcoma; the nomogram of the survival risk of osteosarcoma patients.Figure 4
Internal validation of the calibration curve for three-year (a) and five-year (b) survival risk in osteosarcoma patients.
(a)(b)


## 3.5. Validation of the Predictive Model Diagrams

Thirty percent of the 94 osteosarcoma patients retrieved from the TARGET database were selected as validation samples and used to validate the nomogram of the survival risk of osteosarcoma patients (Figure3). We found that the C-index (0.742) of the validated samples was larger than that for osteosarcoma prediction (0.729), indicating that the external validation of the osteosarcoma patient survival risk prediction map had certain clinical predictive value.


## 3.6. Clinical Use

Through internal and external validation of the C-index with calibration curves, we concluded that the nomogram of the survival risk of osteosarcoma patients had good predictive value. Therefore, the survival curve was plotted using the R software (ver. 3.5.3) after classifying the risk factors in the nomogram graph of survival risk. We found the difference between high-risk factors and low-risk factors significant (P=0.0119, Figure 5).Figure 5
The difference between high-risk factors and low-risk factors significant (P=0.0119).


## 4. Discussion

### 4.1. Risk Factors for Survival of Osteosarcoma Patients

In recent years, the number of patients surviving five years after receiving surgery for osteosarcoma has increased significantly thanks to neoadjuvant chemotherapy [3, 9–13]. However, during treatment, some patients may show poor chemotherapeutic response and prognosis. Therefore, screening for high-risk patients with poor response to chemotherapy and poor prognosis has become a key clinical concern in terms of implementing targeted personalized treatment. Several studies have assessed overall survival and prognosis in osteosarcoma patients, and many clinical indicators have been correlated with survival and prognosis. Histological response appears to be the most reliable prognostic indicator in osteosarcoma patients. However, it is clinically inconvenient, as it can only be evaluated after the operation and is a single factor. We used the TARGET database to mine data on osteosarcoma patients and a Cox regression model to identify multiple risk factors affecting osteosarcoma patients’ survival. A prognostic nomogram for osteosarcoma was constructed with multiple factors to evaluate survival risk.This study enrolled 94 patients with osteosarcoma. Cox regression analysis showed that age, metastasis site, definitive surgery, histologic response, and therapy are risk factors for the survival of osteosarcoma patients. Currently, the correlation between age and prognosis of osteosarcoma patients is controversial. Research has found a positive correlation between age and prognosis, where older age is linked to worse prognosis. However, other studies have shown that adolescent osteosarcoma patients tend to have poor prognosis, with some reports indicating age of onset of osteosarcoma to be unrelated to prognosis [14–16]. Considering the differences in sample selection and age groups in current related studies, as well as the different sites of osteosarcoma incidence among various age groups, our findings remain controversial and need to be further confirmed in large, multicenter samples.Osteosarcoma often metastasizes via the bloodstream in the earlier stages. The lung is the main site of metastasis [17–19]; other extrapulmonary sites include bone, soft tissue, internal organs, brain, and lymphatic metastasis [20, 21]. Aljubran et al. [18] analyzed 85 patients with pulmonary metastasis of osteosarcoma, ascertaining a 3-year survival rate of 30%. Bone is a common site of extrapulmonary metastasis, with an incidence of 0.5% at the time of osteosarcoma diagnosis. However, with the development of neoadjuvant chemotherapy, the incidence of bone metastasis appears to have gradually increased [20] to approximately 10%-11.1%, often with very poor prognosis [20, 21]. In this study, LASSO and Cox regression analysis showed that bone metastasis and lung metastasis are the most important risk factors influencing the survival of osteosarcoma patients. By plotting the nomogram map (Figure 3), we found that the score for bone metastasis was higher than that for lung metastasis. Likewise, the three-year and five-year survival rates were lower, in harmony with previous studies.Complete resection of tumors is the key to the treatment of osteosarcoma [18]. Although amputation was the only effective treatment in the past, the development of comprehensive treatment with neoadjuvant chemotherapy has allowed limb salvage rates as high as 90% [22]. Adjuvant chemotherapy and the effect of chemotherapy are particularly important for limb salvage procedures. However, in the chemotherapy of osteosarcoma, efficacy appears to be good with higher survival and prognosis when the histologic response is ≥90% (≥90% tumor necrosis), in contrast to the poor outcomes observed when the histologic response is <90% (<90% tumor necrosis) [22]. In this study, the nomogram map (Figure 3) showed that patients with osteosarcoma who received therapy and had good histologic response had higher three-year and five-year survival rates.


### 4.2. Establishment of a Survival Risk Prediction Model for Osteosarcoma Patients

Using LASSO regression and Cox multifactor proportional regression, which are widely used in the study of prognostic risk models in oncology and medicine, the screened risk factors were constructed into a nomogram map. These synthesized data from each risk factor and yielded a numerical score. This allows the survival and prognosis of patients to be more accurately and easily understood, better assisting clinical decision-making. To date, no survival risk prediction model for osteosarcoma has been constructed. This study is the first time to use the TARGET database to mine clinical data of osteosarcoma patients for the construction of survival risk prediction models, which may be potentially used in the clinical management of osteosarcoma.The construction of a risk prediction model includes a training queue and a validation queue. Figure4 shows the calibration curve of three-year and five-year survival risk for osteosarcoma patients was validated internally. This is a good indication that the constructed model diagram is accurate, as the nomogram is close to the standard line. The C-index of 0.742 calculated in the validating queue was larger than the C-index of 0.729 calculated in the training queue. Therefore, the nomogram map of survival risk of osteosarcoma patient is of clinical predictive value.


### 4.3. Limitations

This study has several limitations. First, only 94 cases of osteosarcoma were included in the study, representing a relatively small sample size, although 216 cases were obtained from the TARGET database. Second, there were some differences in the statistical data of osteosarcoma patients from various countries during the treatment period, which may influence our results. Third, although the stability of the nomogram diagram for survival risk of osteosarcoma patients has been verified, it still requires validation using validation queues from multiethnic and multinational populations.


## 4.1. Risk Factors for Survival of Osteosarcoma Patients

In recent years, the number of patients surviving five years after receiving surgery for osteosarcoma has increased significantly thanks to neoadjuvant chemotherapy [3, 9–13]. However, during treatment, some patients may show poor chemotherapeutic response and prognosis. Therefore, screening for high-risk patients with poor response to chemotherapy and poor prognosis has become a key clinical concern in terms of implementing targeted personalized treatment. Several studies have assessed overall survival and prognosis in osteosarcoma patients, and many clinical indicators have been correlated with survival and prognosis. Histological response appears to be the most reliable prognostic indicator in osteosarcoma patients. However, it is clinically inconvenient, as it can only be evaluated after the operation and is a single factor. We used the TARGET database to mine data on osteosarcoma patients and a Cox regression model to identify multiple risk factors affecting osteosarcoma patients’ survival. A prognostic nomogram for osteosarcoma was constructed with multiple factors to evaluate survival risk.This study enrolled 94 patients with osteosarcoma. Cox regression analysis showed that age, metastasis site, definitive surgery, histologic response, and therapy are risk factors for the survival of osteosarcoma patients. Currently, the correlation between age and prognosis of osteosarcoma patients is controversial. Research has found a positive correlation between age and prognosis, where older age is linked to worse prognosis. However, other studies have shown that adolescent osteosarcoma patients tend to have poor prognosis, with some reports indicating age of onset of osteosarcoma to be unrelated to prognosis [14–16]. Considering the differences in sample selection and age groups in current related studies, as well as the different sites of osteosarcoma incidence among various age groups, our findings remain controversial and need to be further confirmed in large, multicenter samples.Osteosarcoma often metastasizes via the bloodstream in the earlier stages. The lung is the main site of metastasis [17–19]; other extrapulmonary sites include bone, soft tissue, internal organs, brain, and lymphatic metastasis [20, 21]. Aljubran et al. [18] analyzed 85 patients with pulmonary metastasis of osteosarcoma, ascertaining a 3-year survival rate of 30%. Bone is a common site of extrapulmonary metastasis, with an incidence of 0.5% at the time of osteosarcoma diagnosis. However, with the development of neoadjuvant chemotherapy, the incidence of bone metastasis appears to have gradually increased [20] to approximately 10%-11.1%, often with very poor prognosis [20, 21]. In this study, LASSO and Cox regression analysis showed that bone metastasis and lung metastasis are the most important risk factors influencing the survival of osteosarcoma patients. By plotting the nomogram map (Figure 3), we found that the score for bone metastasis was higher than that for lung metastasis. Likewise, the three-year and five-year survival rates were lower, in harmony with previous studies.Complete resection of tumors is the key to the treatment of osteosarcoma [18]. Although amputation was the only effective treatment in the past, the development of comprehensive treatment with neoadjuvant chemotherapy has allowed limb salvage rates as high as 90% [22]. Adjuvant chemotherapy and the effect of chemotherapy are particularly important for limb salvage procedures. However, in the chemotherapy of osteosarcoma, efficacy appears to be good with higher survival and prognosis when the histologic response is ≥90% (≥90% tumor necrosis), in contrast to the poor outcomes observed when the histologic response is <90% (<90% tumor necrosis) [22]. In this study, the nomogram map (Figure 3) showed that patients with osteosarcoma who received therapy and had good histologic response had higher three-year and five-year survival rates.


## 4.2. Establishment of a Survival Risk Prediction Model for Osteosarcoma Patients

Using LASSO regression and Cox multifactor proportional regression, which are widely used in the study of prognostic risk models in oncology and medicine, the screened risk factors were constructed into a nomogram map. These synthesized data from each risk factor and yielded a numerical score. This allows the survival and prognosis of patients to be more accurately and easily understood, better assisting clinical decision-making. To date, no survival risk prediction model for osteosarcoma has been constructed. This study is the first time to use the TARGET database to mine clinical data of osteosarcoma patients for the construction of survival risk prediction models, which may be potentially used in the clinical management of osteosarcoma.The construction of a risk prediction model includes a training queue and a validation queue. Figure4 shows the calibration curve of three-year and five-year survival risk for osteosarcoma patients was validated internally. This is a good indication that the constructed model diagram is accurate, as the nomogram is close to the standard line. The C-index of 0.742 calculated in the validating queue was larger than the C-index of 0.729 calculated in the training queue. Therefore, the nomogram map of survival risk of osteosarcoma patient is of clinical predictive value.


## 4.3. Limitations

This study has several limitations. First, only 94 cases of osteosarcoma were included in the study, representing a relatively small sample size, although 216 cases were obtained from the TARGET database. Second, there were some differences in the statistical data of osteosarcoma patients from various countries during the treatment period, which may influence our results. Third, although the stability of the nomogram diagram for survival risk of osteosarcoma patients has been verified, it still requires validation using validation queues from multiethnic and multinational populations.


## 5. Conclusions

The nomogram diagram established in this study can objectively and accurately predict the survival and prognosis risk of osteosarcoma patients. Nonetheless, further studies are needed to determine whether it can be applied to other patient groups.



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*Source: 9559496-2022-08-23.xml*