Impact of Postoperative Radiotherapy on Survival in Primary Osteosarcoma: A population-based study

Impact of Postoperative Radiotherapy on Survival in Primary Osteosarcoma: A population-based study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Impact of Postoperative Radiotherapy on Survival in Primary Osteosarcoma: A population-based study HongXiang Gao, YaZheng Dang, XiaoChao Liu, JieXin Chen, HongLiang Zhao, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4433658/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract PURPOSE To evaluate the impact of postoperative radiotherapy on survival in osteosarcoma patients. MATERIALS AND METHODS Total of 3218 participants aged 3–85 years with primary bone and joint osteosarcoma, primary site resection, and/or postoperative radiotherapy were enrolled from the Surveillance, Epidemiology, and End Results (SEER) database. Multiple imputations were utilized to fill in missing data, a directed acyclic graph was constructed to identify causal pathways, and propensity score matching at a ratio of 1:1 was employed to balance covariate characteristics. The Kaplan-Meier method was utilized to estimate survival rates, which were compared the rates using the Log-rank test, and univariate and multivariate analyses were performed using the Cox proportional hazards regression model. Subsequently, sensitivity analyses were conducted on the conclusions using subgroup analysis, competitive risk analysis, and complete dataset analysis. RESULTS A total of 430 patients in the analysis, with 215 in the Radiotherapy and Non-Radiotherapy groups. The 5-year overall survival rates (OS) were 39.1% and 47.1% in the two groups, and the 5-year cancer-specific survival rates (CSS) were 45.5% and 51.8%, respectively. Comparison of the survival rate between the two groups using the Log-rank test yielded non-significant differences (OS, χ 2 = 2.029, p = 0.154; CSS, χ 2 = 0.826, p = 0.363). Both univariate and multivariate analyses revealed no significant differencse in survival associated with radiotherapy. Moreover, the sensitivity analysis findings were consistent with these conclusions. CONCLUSION Postoperative radiotherapy for primary bone and joint osteosarcoma has not shown survival benefits, and its value should be reassessed in multidisciplinary management. Biological sciences/Cancer/Bone cancer Biological sciences/Cancer Biological sciences/Cancer/Cancer therapy/Radiotherapy Osteosarcoma Postoperative Radiotherapy Survival Propensity Score Matching SEER Figures Figure 1 Figure 2 Figure 3 Introduction Osteosarcoma mainly occurs in adolescents and adults over 60 years old 1 , constituting only 0.2% of all newly diagnosed tumors 2 . Recent studies employing molecular spectrum analysis techniques have elucidated the genomic complexity and heterogeneity of osteosarcoma, proposing that it could be more accurately conceptualized as a spectrum of distinct diseases 3 . These rarity and complexity introduce uncertainty in treatment decision-making. Although the standard treatment methods including surgery and chemotherapy developed since the 1980s have enabled 60% of patients with localized osteosarcoma to achieve long-term survival, research indicates that the survival rates have plateaued over the past three decades 4, 5, 6, 7 . Therefore, it is crucial to assess the impact of various treatment options on patient the survival. Postoperative radiotherapy is commonly used to enhance local control in cases of incomplete resection and tumors located in the axial 8, 9, 10, 11, 12, 13, 14 . Nevertheless, there is a debate regarding its efficacy in enhancing survival 11, 15, 16, 17 . This retrospective cohort study, based on a robust sample from the Surveillance, Epidemiology, and End Results (SEER) database, aimed to investigate the influence of postoperative radiotherapy on the survival of primary bone and joint osteosarcoma. Material and methods Participant Participants for this study were identified from the SEER Research Data, 17 Registries, Nov 2022 Sub (2000–2020). We enrolled patients with primary osteosarcoma who met the criteria for pathological confirmation based on the International Classification of Diseases for Oncology, Third Edition (ICD-O-3) codes related to bones and joints. The inclusion criteria were age between 3 and 85 years and having undergone primary tumor surgery followed by postoperative external beam radiotherapy. In total 3816 patients were initially included in this study. Patients who were still alive at the end of the follow-up period were treated as censored value. To avoid bias in the survival analysis, we excluded the dataset from 2018 to 2020 when the ratio of aliving patients exceeded 70%, reducing the study cases to 3218 (Fig. 1). Figure 1. Selected criteria process flowchart Variables definition The study included ten covariates: Age, Sex, Race, Primary Site (PS), primary tumor Size, pathological Grade, Stage, Multiple Primary tumors (MP), Resection margin, and the administration of Chemotherapy. Stage followed the SEER Combined Summary Stage rules. Resection margin were categorized into two groups based on the surgical approach: the Incomplete resection group, comprising local destruction, local excision, and partial resection, and the Complete resection group, consisting of radical excision and amputation. The primary endpoint was overall survival (OS), defined as the duration from diagnosis to the patient’s death or follow-up conclusion; while the secondary endpoint was cancer-specific survival (CSS), defined as the duration from diagnosis to the patient’s death from cancer or follow-up conclusion. Abnormal and missing value Abnormal value in quantitative data were determined as values below Q1–1.5*IQR and above Q3 + 1.5*IQR, where IQR = Q3 - Q1. For tumor Size, values exceeding 217.5 are considered abnormal, leading to 121 cases being treated as missing values. An assessment of the missing value showed that 2062 had complete information. Of the variables examined, Race had 13 (0.40%), PS had 14 (0.44%), Grade had 667 (20.73%), Size had 657 (20.42%), Stage had 65 (2.02%) and Resection had 70 (2.18%) missing information, respectively. To address the missing value, we utilized the multiple imputation (MI) methodology. The R version 4.3.2 mice package was employed to perform 20 imputations with the random forest method. The validity of each variable was assessed using multiple linear regression models across the 20 imputed dataset. Subsequently, the dataset exhibiting the most favorable statistical parameters was selected as the final analysis dataset. The variables incorporated in the MI model include: Age, Sex, Race, PS, Grade, Stage, Size, MP, Resection, Radiation, Chemotherapy, Status, Cause specific death, and Overall survial. Identification of Confounding variables This study employed an entry survey to ensure that all reported factors were accounted for as covariates in order to address potential confounding factors. Subsequently, a Directed Acyclic Graph (DAG) was constructed based on the survey findings, indicating that Age, Size, PS, Grade, Stage, Resection, and Chemotherapy were confounding variables, while Race, Sex, and MP were associated variables (Fig. 2). Figure 2. Directed Acyclic Graph Propensity Score Match To address the heterogeneity in baseline characteristics between the Radiotherapy and Non-Radiotherapy groups, propensity score matching (PSM) (R version 4.3.2 package MatchIt v4.2.1, cobalt v4.5.0, and EValue v4.1.3) was performed. A logistic regression model was used to match the patients from both groups at a 1:1 ratio. The variables considered in the model included Age, Sex, Race, PS, Grade, Stage, Size, MP, Resection, and Chemotherapy. Standardized mean differences (SMD) were utilized to assess matching balance, with an SMD below 0.2 signifying balanced matching (Table 1 , eTable 3, eFigure 1, 2). Table 1 Comparisons of baseline characteristics of patients in Radiotherapy and Non-Radiotherapy groups before and after PSM Factor Before PSM After PSM RT (n = 215) NRT (n = 3003) SMD RT (n = 215) NRT (n = 215) SMD Age 44( 37 ) a 17( 20 ) a 0.906 44( 37 ) a 46( 43 ) a 0.085 Sex 0.023 0.075 Male 117(54.42%) 1669(55.58%) 117(54.42%) 125(58.14%) Female 98(45.58%) 1334(44.42%) 98(45.58%) 90(41.86%) Race 0.094 0.059 White 170(79.07%) 2256(75.12%) 170(79.07%) 173(80.47%) Black 27(12.56%) 450(14.99%) 27(12.56%) 23(10.70%) Others 18(8.37%) 297(9.89%) 18(8.37%) 19(8.84%) PS 1.359 0.010 Extremity 58(26.98%) 2490(82.92%) 58(26.98%) 59(27.44%) Axial 157(73.02%) 513(17.08%) 157(73.02%) 156(72.56%) Grade 0.181 0.066 I/II 17(7.91%) 405(13.49%) 17(7.91%) 21(9.77%) III/IV 198(92.09%) 2598(86.51%) 198(92.09%) 194(90.23%) Size 63( 45 ) a 85(60) a 0.484 63( 45 ) a 65(60) a 0.065 Stage 0.258 0.096 Localized 60(27.91%) 1173(39.06%) 60(27.91%) 57(26.51%) Reginal 104(48.37%) 1331(44.32%) 104(48.37%) 98(45.58%) Distance 51(23.72%) 499(16.62%) 51(23.72%) 60(27.91%) MP 0.163 0.059 No 176(81.86%) 2634(87.71%) 176(81.86%) 171(79.53%) Yes 39(18.14%) 369(12.29%) 39(18.14%) 44(20.47%) Resection 0.637 0.076 Incomplete 90(41.86%) 437(14.55%) 90(41.86%) 82(38.14%) Complete 125(58.14%) 2566(85.45%) 125(58.14%) 133(61.86%) Chemotherapy 0.440 0.058 No 75(34.88%) 485(16.15%) 75(34.88%) 81(37.67%) Yes 140(65.12%) 2518(83.85%) 140(65.12%) 134(62.33%) RT: Radiotherapy; NRT: Non-Radiotherapy; PS: Primary Site; MP: Multiple Primary tumor; a: Abnormal distribution Quantitative data, M (IQR) Statistical analysis The Kaplan-Meier method was employed to estimate the survival rate and median survival time (MST), and the Log-rank test was used to compare the long-term survival difference. Univariate and Multivariate analyses were conducted using the Cox proportional hazard regression model. Covariates with p < 0.1 in the univariate analysis and identified as confounding variables based on the DAG were included in the multivariate analysis. Diagnostic tests for the Cox model included testing the proportional hazards assumption, examining multicollinearity among the independent variables, and scrutinizing the linear relationship between the quantitative independent variables and the outcome. A variance inflation factor (VIF) below 10 demonstrates the absence of multicollinearity. A scatter plot of Age and Size against Martingale residuals showed a linear trend. The proportional hazards assumption was tested by assessing the statistical significance of the interactions between follow-up time and the covariables. Given the presence of time-dependent variables in the covariates, the Time-Dependent Cox Regression Model was employed. Sensitivity analysis was conducted on the conclusions using subgroup analysis, competitive risk analysis (cmprsk v2.2-11 package, R version 4.3.2), and complete dataset analysis. Statistical significance was determined at P < 0.05 using a two-tailed test. Statistical analyses were performed using the SPSS software (version 26, IBM Corp) and R version 4.3.2. All experiments and methods detailed in this study were conducted in compliance with the relevant guidelines and regulations as set forth by the SEER database. Results Demographics Of the 5216 patients, 3218 were selected for analysis. In the unmatched cohort, patients who underwent radiotherapy tended to be older, had tumors located in the axial, smaller tumor size, regional invasion, incomplete resection, and no chemotherapy. Following PSM, 430 patients were included in the analysis, with 215 in both the Radiotherapy and Non-Radiotherapy groups. The covariates were effectively balanced, indicating no substantial differences in demographic or tumor-related variables between the groups (Table 1 ). Table 1 Comparisons of baseline characteristics of patients in Radiotherapy and Non-Radiotherapy groups before and after PSM Comparison of survival The MST for all patients was 38 months (95% CI, 28.9–47.1), with a 5-year OS of 43.5%. In the Radiotherapy group, the MST was 34 months (95% CI, 24.4–43.6), while in the Non-Radiotherapy group, it was 42 months (95% CI, 16.7–67.3). The 5-year OS in the two groups were 39.1% and 47.5%, respectively. The Log-Rank test revealed no significant difference between the two groups (χ 2 = 2.029, p = 0.154) (eTable 1, Fig. 3a). The cancer-specific MST for all patients was 52 months (95% CI, 33.7–70.3), with a 5-year CSS of 48.5%. In the Radiotherapy group, the cancer-specific MST was 43 months (95% CI, 25.7–60.3 months), while in the Non-Radiotherapy group, it was 65 months (95% CI, 33.0–97.0 months). The 5-year CSS in the two groups were 44.9% and 51.9%, respectively. The Log-Rank test revealed no significant difference between the two groups (χ 2 = 0.826, p = 0.363) (eTable 1, Fig. 3b). Figure 3. Comparison of Kaplan-Meier survival curves between the Radiotherapy group and the Non-Radiotherapy group. a, For Overall survival; b, For Cancer-specific survival Univariate and Multivariate analyses Cox proportional hazards regression analyses for OS indicated that Age, Stage, Resection, Chemotherapy, and Radiation were time-dependent variables. Univariate and Multivariate analyses were conducted using the Time-Dependent Cox Regression Model. The results showed a lower hazard of death in females compared to males (HR, 0.758; 95% CI, 0.592 to 0.969). Hazards of death increased with Age (HR, 1.024; 95% CI, 1.018 to 1.029), Grade (HR, 1.949; 95% CI, 1.105 to 3.437), and Size (HR, 1.005; 95% CI, 1.002 to 1.008). The hazard of death significantly increased with the Distance Stage (HR, 3.684; 95% CI, 2.532 to 5.358). Complete tumor Resection was associated with a reduced hazard of death (HR, 0.290; 95%CI, 0.143 to 0.587), with the reduction increasing over time (HR, 1.345; 95%CI, 1.071 to 1.689). The hazard of death increased over time in patients undergoing Radiation (HR, 1.102; 95% CI, 1.021 to 1.190), although no statistical significance was found (Table 2 ). An HR calculation based on the time-dependent variable Radiation revealed HR < 1 before 9 months of OS, hinting a benefit of receiving radiotherapy during this period (eFigure 3). Table 2 Univariate and Multivariate analyses for overall survival of the osteosarcoma patients after PSM Factor Univariate Multivariate HR(95%CI) p HR(95%CI) p Age 1.036(1.019–1.054) 0.000 1.024(1.018–1.029) 0.000 Age*Ln(T) 0.994(0.989–0.999) 0.026 NA 0.242 Sex 0.748(0.588–0.952) 0.018 0.758(0.592–0.969) 0.027 Race 0.747 Not selected White Ref Black 0.868(0.590–1.278) 0.474 Others 1.033(0.678–1.574) 0.881 PS 1.011(0.776–1.316) 0.936 NA 0.124 Grade 2.693(1.542–4.702) 0.000 1.949(1.105–3.437) 0.021 Size 1.006(1.003–1.008) 0.000 1.005(1.002–1.008) 0.003 Stage 0.000 0.000 Localized Ref Ref Reginal 2.695(1.448–5.016) 0.002 1.339(0.967–1.855) 0.079 Distance 8.638(3.094–24.116) 0.000 3.684(2.532–5.358) 0.000 Stage*Ln(T) 0.835(0.710–0.983) 0.030 NA 0.071 MP 1.535(1.166–2.021) 0.002 NA 0.423 Resection 0.338(0.169–0.674) 0.002 0.290(0.143–0.587) 0.001 Resection*Ln(T) 1.415(1.129–1.772) 0.003 1.345(1.071–1.689) 0.011 Chemotherapy 0.355(0.177–0.711) 0.003 NA 0.767 Chemotherapy*Ln(T) 1.339(1.064–1.684) 0.013 NA 0.758 Radiation 0.552(0.280–1.090) 0.087 NA 0.143 Radiation*Ln(T) 1.301(1.045–1.620) 0.019 1.102(1.021–1.190) 0.013 PS: Primary Site; MP: Multiple Primary tumor; NA: In Univariate analysis, α = 0.1, while in Multivariate analysis, α = 0.05. For CSS, Age, Stage, Resection, and Chemotherapy were identified as time-dependent variables. The results demonstrated a lower hazard of cancer-specific death in females than in males (HR, 0.731; 95% CI, 0.557 to 0.958). The hazard of cancer-specific death increased with Age (HR, 1.045; 95% CI, 1.026 to 1.065) but decreased with follow-up time (HR, 0.992; 95% CI, 0.985 to 0.998). Hazards increased with Grade (HR, 1.921; 95% CI, 1.037 to 3.558) and Size (HR, 1.004; 95% CI, 1.000 to 1.007). Advanced staging correlated with higher hazards: Regional (HR, 3.103; 95% CI, 1.434 to 6.717), Distance (HR, 16.377; 95% CI, 4.538 to 59.104), but decreased over time (HR, 0.774; 95% CI, 0.630 to 0.951). No statistically significant difference was found in the hazard of death associated with radiotherapy (Table 3 ). Table 3 Univariate and Multivariate analyses for cancer-specific survival of the osteosarcoma patients after PSM Factor Univariate Multivariate HR(95%CI) p HR(95%CI) p Age 1.040(1.020–1.061) 0.000 1.045(1.026–1.065) 0.000 Age*Ln(T) 0.991(0.984–0.997) 0.005 0.992(0.985–0.998) 0.007 Sex 0.693(0.530–0.905) 0.007 0.731(0.557–0.958) 0.023 Race 0.539 Not selected White Ref Black 0.796(0.512–1.238) 0.311 Others 1.085(0.691–1.703) 0.724 PS 0.880(0.664–1.167) 0.374 0.220 Grade 2.613(1.425–4.790) 0.002 1.921(1.037–3.558) 0.038 Size 1.006(1.004–1.009) 0.000 1.004(1.000-1.007) 0.025 Stage 0.000 0.000 Localized Ref Ref Reginal 3.501(1.659–7.389) 0.001 3.103(1.434–6.717) 0.004 Distance 15.018(4.321–52.199) 0.000 16.377(4.538–59.104) 0.000 Stage*Ln(T) 0.781(0.639–0.954) 0.015 0.774(0.630–0.951) 0.015 MP 0.914(0.656–1.273) 0.594 Not selected Resection 0.314(0.140–0.703) 0.005 NA 0.075 Resection*Ln(T) 1.533(1.169–2.010) 0.002 NA 0.492 Chemotherapy 0.183(0.078–0.428) 0.000 NA 0.855 Chemotherapy*Ln(T) 1.810(1.351–2.426) 0.000 NA 0.272 Radiation 1.126(0.870–1.459) 0.367 NA 0.184 PS: Primary Site; MP: Multiple Primary tumor; NA: In Univariate analysis, α = 0.1, while in Multivariate analysis, α = 0.05. Table 2 Univariate and Multivariate analyses for overall survival of the osteosarcoma patients after PSM Table 3 Univariate and Multivariate analyses for cancer-specific survival of the osteosarcoma patients after PSM Subgroup Analysis The subgroup analysis conducted in this study was a post-hoc analysis aimed at evaluating the robustness of the conclusions. The optimal cutoff values for Age and Size were determined using the ROC curve. The area under the curve (AUC) was 0.657 (95%CI: 0.606–0.709) for Age, with the identified critical age point of 50.5 years based on the maximal Youden index, corresponding to a sensitivity of approximately 53.2% and specificity of about 76.3%. Regarding Size, the AUC was 0.616 (95% CI: 0.560–0.672), with the optimal critical point of 54.5 mm, resulting in a sensitivity of around 67.3% and specificity of approximately 53.3%. The p-value for the interaction was calculated using a likelihood ratio test, which compared the main regression model with the interaction model. The findings demonstrated a consistent hazard of OS and CSS from radiotherapy across all six subgroups, with no significant difference (eFigure 4, 5). Competitive risk analysis Applying Fine and Gray’ s method to build the regression model with all variables, the results indicated that Age (HR, 1.790; 95%CI, 1.322 to 2.423), Sex (HR, 0.703; 95%CI, 0.532 to 0.930), Size (HR, 1.432; 95%CI, 1.030 to 1.992), and Stage (HR, 1.907; 95%CI, 1.493 to 2.436) are significant independent predictors of CSS. Notably, Radiation (HR, 1.204; 95%CI, 0.922 to 1.573) did not seem to influence CSS, which is consistent with the conclusions of the Cox regression analysis (eTable 2). Complete dataset analyses A total of 2062 cases with complete information were analyzed. In the unmatched cohort, results revealed that patients who received radiotherapy were more likely to be older, have tumors in the axial region, smaller tumor size, regional invasion, multiple primary tumors, incomplete resection, and no chemotherapy. PSM was then performed, resulting in 272 patients being 1:1 matched. After matching, the covariates were well balanced, showing no significant differences between the two groups (eTable 3). The comparison of OS (χ² = 4.845, p = 0.028) and CSS (χ² = 3.538, p = 0.060) by Log-rank test indicated no significant difference (eFigure 6). Subsequently, both univariate and multivariate analyses, using the Time-Dependent Cox Regression Model, showed that the hazard of overall mortality (HR, 1.122; 95% CI, 1.016 to 1.239) and cancer-specific mortality (HR, 1.124; 95% CI, 1.007 to 1.256) from radiation increases over time. However, there were no significant differences (eTable 4, 5). Discussion Patients with positive surgical margins have a higher rate of local recurrence, which is an crucial adverse prognostic factor 18, 19, 20, 21, 22, 23 , hinting potential benefits of postoperative radiotherapy for incomplete resection 8, 9, 10, 11 . Additionally, studies suggests that radiotherapy may be more efficacious in patients with smaller tumor burden, favorable response to chemotherapy, or those suitable for complete resection 9, 12, 24, 25 . However, these findings show local benefits, while the benefits of survival remains contentious. This study aimed to investigate the potential correlation between postoperative radiotherapy and survival by analyzing a large sample of patients with primary osteosarcoma. Since the establishement of standard treatment for osteosarcoma involving surgery and chemotherapy in the 1980s, subsequent research on modifying these protocols has not significantly improved overall survival 26, 27, 28 . The 5-year survival rate for localized osteosarcoma patients ranges from 50–70%, whereas for those with metastatic tumors, it drops to 20–30% 3, 5, 6, 29, 30, 31 . Our findings showed that the 5-year OS was 50.7% for localized osteosarcoma and 22.2% for metastatic osteosarcoma. This underscores the limitations of traditional comprehensive treatment on survival. There was an uncorrelated impact of postperative radiotherapy on survival in both OS (χ 2 = 2.029, p = 0.154) and CSS (χ 2 = 0.826, p = 0.363). Further univariate and multivariate analyses demonstrated that Age, Sex, Size, Grade, Resection, and Stage serve were independent prognostic factors for OS, while Age, Sex, Size, Grade, and Stage for CSS, consistent with previous findings 6, 7, 15, 18, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41 . Sensitivity analyses ensures the robustness of the conclusion. A study conducted by Guadagnolo compared the effect of adjuvant radiotherapy in 119 patients with positive surgical margins for head and neck osteosarcoma 11 . Of these patients, 23% received postoperative radiotherapy. The results indicated that combined radiotherapy improved overall survival. However, the patients enrolled in this study spanned from 1960 to 2007, and only 47% administrated chemotherapy. Significant advancements in osteosarcoma chemotherapy in recent decades warrant caution when assessing whether the conclusion accurately represents the clinical reality. Conversely, a meta-analysis evaluating the role of adjuvant radiotherapy/chemotherapy in the management of head and neck osteosarcoma demonstrated that the addition of adjuvant treatment led to lower survival compared to surgery alone 16 . However, it is noteworthy that most reported cases did not provide information about the surgical margins, which is a crucial factor influencing survival. Studies by the Cooperative Osteosarcoma Study Group revealed that patients with unresectable and incompletely resected lesions had a more favorable prognosis when they received radiotherapy 8, 42 . The development of radiotherapy technology has rendered proton and heavy-ion therapy a feasible choice for these patients, demonstrating encouraging outcomes 10, 43, 44 . Nevertheless, the conclusions regarding the prognostic value of radiotherapy have not been fully substantiated due to the potential confounding variables in these studies. Intriguingly, despite the rigorous control of all confounding variables, a puzzling observation arises from the Kaplan-Meier survival curve, indicating that patients who received radiotherapy exhibit lower survival rates. Further analysis demonstrated a short-term survival benefit at 9 months follow-up, this advantage did not persist beyond that period. The temporary survival benefits can be attributed to enhanced local control resulted in prolonged overall survival 45 . Nevertheless, the long-term survival disadvantage remains unexplained, even though subgroup analysis indicated potential survival benefits in lower-grade cases. Possible reasons for this discrepancy may lie in unrecorded factors in the SEER database impacting survival time, such as the number of metastases, surgical resectability of metastases, and response to chemotherapy 46, 47, 48, 49, 50 . This emphasizes the need for a more thorough and meticulous study in the future. This report presents the largest study on the impact of postoperative radiotherapy on the survival of patients with primary osteosarcoma. We utilized the SEER database, a population-based database maintained by the National Cancer Institute, which collects cancer patient information from 18 states representing 28% of the U.S. population. Due to its targeted sampling method, this database includes a higher proportion of minority ethnic groups and to some extent represents the entire U.S. population. Our findings suggest that race does not impact survival, supporting the generalization of our conclusions to all osteosarcoma patients in the United States. Meanwhile, the study used DAG for causal pathways and PSM to balance covariates between groups, thus enhancing the study’s validity and accuracy. Furthermore, the robustness of the conclusions was confirmed through subgroup analysis, competing risk analysis, and analysis of the complete dataset. Regretfully, this study faces several challenging limitations. Firstly, this was a retrospective cohort study in which researchers were unable to allocated exposure to individuals in the population prior to the study. Despite balancing known confounding variables through PSM, potential factors that could influence the results were not accounted for, such as the degree of tumor necrosis after chemotherapy, which is a significant prognostic factor for survival 7, 46 , and patients with a lower degree of necrosis were more likely to be chosen for postoperative radiotherapy. This makes it challenging to determine the causal relationship between radiation and survival, and demonstrates only a correlation. Addressing selection and confounding biases effectively to clarify the causal relationships can only be achieved through prospective randomized controlled trials. Additionally, the 17-year time span of patient enrollment in this retrospective study may have caused an uncontrollable time deviation. This is reflected in the development of radiotherapy techniques like proton and heavy-ion radiation, causing patients to receive different types of techniques at different time points of enrollment. Moreover, other potential risk factors, treatment strategies, and observation methods may chang over time. Although this was corroborated by the time-dependent variable analysis in this study, the lack of detailed variable records unavoidably affected the comparison and interpretation of results. Ultimately, osteosarcoma is a common bone malignancy that affects the long-term survival of patients. Despite tremendous efforts over the past few decades, the survival have not improved significantly. Enhancing survival rates has been a long-standing challenge. Although our findings indicate that postoperative radiotherapy did not result in a survival benefit, we further confirmed that the traditional multimodal treatment approach primarily based on surgery, radiotherapy, and chemotherapy, cannot improve survival. We recommend the management of all osteosarcoma patients by a multidisciplinary team and, based on careful selection of patients who may benefit from postoperative radiotherapy, expand research to consider new multimodal treatment approaches involving targeted therapy and immunotherapy to explore their potential benefits on survival. Declarations Author Contribution HXG designs the framework, analyzes data, interprets findings, and drafts; YZD conducts a detailed literature review, elucidating the research background; XCL, JXC, HLZ, JL and KJZ collect and organize data. All authors reviewed the manuscript. Funding stastement This research did not receive any specific grants from funding agencies in the public, commercial, or not-for-profit sectors. Ethical Considerations The Institutional Review Committee waived the need for ethical approval, citing the global accessibility of the SEER database for researchers. 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Journal of clinical oncology: official journal of the American Society of Clinical Oncology 2013, 31(18): 2303–2312. Davis AM, Bell RS, Goodwin PJ. Prognostic factors in osteosarcoma: a critical review. Journal of clinical oncology: official journal of the American Society of Clinical Oncology 1994, 12(2): 423–431. Ferrari S, Bertoni F, Mercuri M, Picci P, Giacomini S, Longhi A, et al. Predictive factors of disease-free survival for non-metastatic osteosarcoma of the extremity: an analysis of 300 patients treated at the Rizzoli Institute. Annals of oncology: official journal of the European Society for Medical Oncology 2001, 12(8): 1145–1150. Bacci G, Longhi A, Versari M, Mercuri M, Briccoli A, Picci P. Prognostic factors for osteosarcoma of the extremity treated with neoadjuvant chemotherapy: 15-year experience in 789 patients treated at a single institution. Cancer 2006, 106(5): 1154–1161. Whelan JS, Jinks RC, McTiernan A, Sydes MR, Hook JM, Trani L, et al. Survival from high-grade localised extremity osteosarcoma: combined results and prognostic factors from three European Osteosarcoma Intergroup randomised controlled trials. Annals of oncology: official journal of the European Society for Medical Oncology 2012, 23(6): 1607–1616. Ogura K, Fujiwara T, Yasunaga H, Matsui H, Jeon DG, Cho WH, et al. Development and external validation of nomograms predicting distant metastases and overall survival after neoadjuvant chemotherapy and surgery for patients with nonmetastatic osteosarcoma: A multi-institutional study. Cancer 2015, 121(21): 3844–3852. Bertrand TE, Cruz A, Binitie O, Cheong D, Letson GD. Do Surgical Margins Affect Local Recurrence and Survival in Extremity, Nonmetastatic, High-grade Osteosarcoma? Clinical orthopaedics and related research 2016, 474(3): 677–683. Bacci G, Longhi A, Ferrari S, Briccoli A, Donati D, De Paolis M, et al. Prognostic significance of serum lactate dehydrogenase in osteosarcoma of the extremity: experience at Rizzoli on 1421 patients treated over the last 30 years. Tumori 2004, 90(5): 478–484. Ozaki T, Flege S, Liljenqvist U, Hillmann A, Delling G, Salzer-Kuntschik M, et al. Osteosarcoma of the spine: experience of the Cooperative Osteosarcoma Study Group. Cancer 2002, 94(4): 1069–1077. Dong M, Liu R, Zhang Q, Luo H, Wang D, Wang Y, et al. Efficacy and safety of carbon ion radiotherapy for bone sarcomas: a systematic review and meta-analysis. Radiation oncology (London, England) 2022, 17(1): 172. Matsunobu A, Imai R, Kamada T, Imaizumi T, Tsuji H, Tsujii H, et al. Impact of carbon ion radiotherapy for unresectable osteosarcoma of the trunk. Cancer 2012, 118(18): 4555–4563. Halalsheh H, Ismael T, Boheisi M, Shehadeh A, Sultan I. Impact of delay of local control in nonmetastatic extremity primary osteosarcoma. Pediatric blood & cancer 2024, 71(1): e30752. Bishop MW, Chang YC, Krailo MD, Meyers PA, Provisor AJ, Schwartz CL, et al. Assessing the Prognostic Significance of Histologic Response in Osteosarcoma: A Comparison of Outcomes on CCG-782 and INT0133-A Report From the Children's Oncology Group Bone Tumor Committee. Pediatric blood & cancer 2016, 63(10): 1737–1743. Harris MB, Gieser P, Goorin AM, Ayala A, Shochat SJ, Ferguson WS, et al. Treatment of metastatic osteosarcoma at diagnosis: a Pediatric Oncology Group Study. Journal of clinical oncology: official journal of the American Society of Clinical Oncology 1998, 16(11): 3641–3648. Bacci G, Rocca M, Salone M, Balladelli A, Ferrari S, Palmerini E, et al. High grade osteosarcoma of the extremities with lung metastases at presentation: treatment with neoadjuvant chemotherapy and simultaneous resection of primary and metastatic lesions. Journal of surgical oncology 2008, 98(6): 415–420. Kager L, Zoubek A, Pötschger U, Kastner U, Flege S, Kempf-Bielack B, et al. Primary metastatic osteosarcoma: presentation and outcome of patients treated on neoadjuvant Cooperative Osteosarcoma Study Group protocols. Journal of clinical oncology: official journal of the American Society of Clinical Oncology 2003, 21(10): 2011–2018. Daw NC, Billups CA, Rodriguez-Galindo C, McCarville MB, Rao BN, Cain AM, et al. Metastatic osteosarcoma. Cancer 2006, 106(2): 403–412. Additional Declarations No competing interests reported. Supplementary Files supplementaryfiguresandtables.docx supplementaryprotocol.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4433658","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":307759130,"identity":"f48deb73-211e-4861-8efa-8b44f064e85f","order_by":0,"name":"HongXiang Gao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABD0lEQVRIie2RMUvEMBiGUwpxyXlrDqH1J+QIBIfs/giXFCEu1VFuqGdBiJuZb/IvCC6OhQ/qUvUvxEUcOlw3R691uCn1RsE8w5vlffLxJQgFAn8TvE21kMk0jsHtrrhG89kt1mwnpSd6N5A9vJFDOtZPj6F23dNVdrf3Ap+Z0dEjEMRQIU98yrzRZ/NV88wxudBH6lXGAiaVQ7U+L31KmYuDiakTjHLB1KXGAvYVi0rwK7YdFIKn7UbBQPgNYXRMSekwpUgwzblTBiiLf1EY/dCzlak4pq1AqtGMwuaR1cguqT2taWeW2b3Nefe1kNfWArh1If1TquGAPvD2O5SnPkz5uWvZR7weKQYCgcA/5ht0R1tjB5S3eAAAAABJRU5ErkJggg==","orcid":"","institution":"Radiotherapy Department, Honghui Hospital, Xi'an Jiaotong University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"HongXiang","middleName":"","lastName":"Gao","suffix":""},{"id":307759131,"identity":"6ecc0513-3c22-47be-8910-0e1f9fe8fef9","order_by":1,"name":"YaZheng Dang","email":"","orcid":"","institution":"Radiotherapy Department, 986 Hospital of the People's Liberation Army","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"YaZheng","middleName":"","lastName":"Dang","suffix":""},{"id":307759132,"identity":"22b55b26-392f-4d9e-9e76-38319e391d6c","order_by":2,"name":"XiaoChao Liu","email":"","orcid":"","institution":"Radiotherapy Department, Honghui Hospital, Xi'an Jiaotong University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"XiaoChao","middleName":"","lastName":"Liu","suffix":""},{"id":307759133,"identity":"7dca5286-3905-4a75-87ac-167e69d01a86","order_by":3,"name":"JieXin Chen","email":"","orcid":"","institution":"Radiotherapy Department, Honghui Hospital, Xi'an Jiaotong University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"JieXin","middleName":"","lastName":"Chen","suffix":""},{"id":307759134,"identity":"d54beea5-aa61-47c4-83ab-96beb92c4c76","order_by":4,"name":"HongLiang Zhao","email":"","orcid":"","institution":"Radiotherapy Department, Honghui Hospital, Xi'an Jiaotong University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"HongLiang","middleName":"","lastName":"Zhao","suffix":""},{"id":307759135,"identity":"96d083cc-51da-44e4-9cec-603f381c040a","order_by":5,"name":"Jia Li","email":"","orcid":"","institution":"Radiotherapy Department, Honghui Hospital, Xi'an Jiaotong University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jia","middleName":"","lastName":"Li","suffix":""},{"id":307759136,"identity":"a2212276-b93f-4e55-8a67-25ded9bf3b3b","order_by":6,"name":"KeJia Zhang","email":"","orcid":"","institution":"Radiotherapy Department, Honghui Hospital, Xi'an Jiaotong University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"KeJia","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-05-17 02:06:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4433658/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4433658/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57723025,"identity":"e641af03-c042-4f9e-b854-4079dfa0cf0b","added_by":"auto","created_at":"2024-06-04 19:13:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":27009,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSelected criteria process flowchart\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4433658/v1/3f6ef3c73d9b880c1b299213.png"},{"id":57723024,"identity":"267c6b17-dfbb-4233-b599-1b7985dc10c8","added_by":"auto","created_at":"2024-06-04 19:13:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":35476,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of Kaplan-Meier survival curves between the Radiotherapy group and the Non-Radiotherapy group. a, For Overall survival; b, For Cancer-specific survival\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4433658/v1/f6b63da64049ac06fb5df354.png"},{"id":57723023,"identity":"1d358df9-0cb7-4b84-9cdd-6206ea3ed78d","added_by":"auto","created_at":"2024-06-04 19:13:10","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":230283,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDirected Acyclic Graph\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4433658/v1/30d248e99e3efbf62f667026.jpeg"},{"id":74208879,"identity":"43770e5e-1115-4aa7-afa7-7998708cffd3","added_by":"auto","created_at":"2025-01-20 04:31:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1666856,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4433658/v1/dac5c646-f331-446b-ac3c-97ea887deadc.pdf"},{"id":57723027,"identity":"6fec6607-d182-4ee3-9489-b7c54f45a691","added_by":"auto","created_at":"2024-06-04 19:13:13","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":805531,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryfiguresandtables.docx","url":"https://assets-eu.researchsquare.com/files/rs-4433658/v1/754107977265da77da421525.docx"},{"id":57723026,"identity":"70040521-96c5-44aa-af73-d59edd7ed503","added_by":"auto","created_at":"2024-06-04 19:13:12","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":22076,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryprotocol.docx","url":"https://assets-eu.researchsquare.com/files/rs-4433658/v1/a3c94990b1bd02348b9e5597.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of Postoperative Radiotherapy on Survival in Primary Osteosarcoma: A population-based study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOsteosarcoma mainly occurs in adolescents and adults over 60 years old\u003csup\u003e1\u003c/sup\u003e, constituting only 0.2% of all newly diagnosed tumors\u003csup\u003e2\u003c/sup\u003e. Recent studies employing molecular spectrum analysis techniques have elucidated the genomic complexity and heterogeneity of osteosarcoma, proposing that it could be more accurately conceptualized as a spectrum of distinct diseases\u003csup\u003e3\u003c/sup\u003e. These rarity and complexity introduce uncertainty in treatment decision-making. Although the standard treatment methods including surgery and chemotherapy developed since the 1980s have enabled 60% of patients with localized osteosarcoma to achieve long-term survival, research indicates that the survival rates have plateaued over the past three decades\u003csup\u003e4, 5, 6, 7\u003c/sup\u003e. Therefore, it is crucial to assess the impact of various treatment options on patient the survival. Postoperative radiotherapy is commonly used to enhance local control in cases of incomplete resection and tumors located in the axial\u003csup\u003e8, 9, 10, 11, 12, 13, 14\u003c/sup\u003e. Nevertheless, there is a debate regarding its efficacy in enhancing survival\u003csup\u003e11, 15, 16, 17\u003c/sup\u003e. This retrospective cohort study, based on a robust sample from the Surveillance, Epidemiology, and End Results (SEER) database, aimed to investigate the influence of postoperative radiotherapy on the survival of primary bone and joint osteosarcoma.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipant\u003c/h2\u003e \u003cp\u003eParticipants for this study were identified from the SEER Research Data, 17 Registries, Nov 2022 Sub (2000\u0026ndash;2020). We enrolled patients with primary osteosarcoma who met the criteria for pathological confirmation based on the International Classification of Diseases for Oncology, Third Edition (ICD-O-3) codes related to bones and joints. The inclusion criteria were age between 3 and 85 years and having undergone primary tumor surgery followed by postoperative external beam radiotherapy. In total 3816 patients were initially included in this study. Patients who were still alive at the end of the follow-up period were treated as censored value. To avoid bias in the survival analysis, we excluded the dataset from 2018 to 2020 when the ratio of aliving patients exceeded 70%, reducing the study cases to 3218 (Fig.\u0026nbsp;1).\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 1. Selected criteria process flowchart\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eVariables definition\u003c/h2\u003e \u003cp\u003eThe study included ten covariates: Age, Sex, Race, Primary Site (PS), primary tumor Size, pathological Grade, Stage, Multiple Primary tumors (MP), Resection margin, and the administration of Chemotherapy. Stage followed the SEER Combined Summary Stage rules. Resection margin were categorized into two groups based on the surgical approach: the Incomplete resection group, comprising local destruction, local excision, and partial resection, and the Complete resection group, consisting of radical excision and amputation. The primary endpoint was overall survival (OS), defined as the duration from diagnosis to the patient\u0026rsquo;s death or follow-up conclusion; while the secondary endpoint was cancer-specific survival (CSS), defined as the duration from diagnosis to the patient\u0026rsquo;s death from cancer or follow-up conclusion.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eAbnormal and missing value\u003c/h2\u003e \u003cp\u003eAbnormal value in quantitative data were determined as values below Q1\u0026ndash;1.5*IQR and above Q3\u0026thinsp;+\u0026thinsp;1.5*IQR, where IQR\u0026thinsp;=\u0026thinsp;Q3 - Q1. For tumor Size, values exceeding 217.5 are considered abnormal, leading to 121 cases being treated as missing values.\u003c/p\u003e \u003cp\u003eAn assessment of the missing value showed that 2062 had complete information. Of the variables examined, Race had 13 (0.40%), PS had 14 (0.44%), Grade had 667 (20.73%), Size had 657 (20.42%), Stage had 65 (2.02%) and Resection had 70 (2.18%) missing information, respectively. To address the missing value, we utilized the multiple imputation (MI) methodology. The R version 4.3.2 mice package was employed to perform 20 imputations with the random forest method. The validity of each variable was assessed using multiple linear regression models across the 20 imputed dataset. Subsequently, the dataset exhibiting the most favorable statistical parameters was selected as the final analysis dataset. The variables incorporated in the MI model include: Age, Sex, Race, PS, Grade, Stage, Size, MP, Resection, Radiation, Chemotherapy, Status, Cause specific death, and Overall survial.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of Confounding variables\u003c/h2\u003e \u003cp\u003eThis study employed an entry survey to ensure that all reported factors were accounted for as covariates in order to address potential confounding factors. Subsequently, a Directed Acyclic Graph (DAG) was constructed based on the survey findings, indicating that Age, Size, PS, Grade, Stage, Resection, and Chemotherapy were confounding variables, while Race, Sex, and MP were associated variables (Fig.\u0026nbsp;2).\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 2. Directed Acyclic Graph\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003ePropensity Score Match\u003c/h2\u003e \u003cp\u003eTo address the heterogeneity in baseline characteristics between the Radiotherapy and Non-Radiotherapy groups, propensity score matching (PSM) (R version 4.3.2 package MatchIt v4.2.1, cobalt v4.5.0, and EValue v4.1.3) was performed. A logistic regression model was used to match the patients from both groups at a 1:1 ratio. The variables considered in the model included Age, Sex, Race, PS, Grade, Stage, Size, MP, Resection, and Chemotherapy. Standardized mean differences (SMD) were utilized to assess matching balance, with an SMD below 0.2 signifying balanced matching (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, eTable 3, eFigure 1, 2).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparisons of baseline characteristics of patients in Radiotherapy and Non-Radiotherapy groups before and after PSM\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eBefore PSM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eAfter PSM\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRT (n\u0026thinsp;=\u0026thinsp;215)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNRT (n\u0026thinsp;=\u0026thinsp;3003)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSMD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRT (n\u0026thinsp;=\u0026thinsp;215)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNRT (n\u0026thinsp;=\u0026thinsp;215)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSMD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.906\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e46(\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e117(54.42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1669(55.58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e117(54.42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e125(58.14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98(45.58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1334(44.42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e98(45.58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e90(41.86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e170(79.07%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2256(75.12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e170(79.07%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e173(80.47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27(12.56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e450(14.99%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27(12.56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23(10.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18(8.37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e297(9.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18(8.37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19(8.84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtremity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58(26.98%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2490(82.92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58(26.98%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59(27.44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAxial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e157(73.02%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e513(17.08%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e157(73.02%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e156(72.56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGrade\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI/II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17(7.91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e405(13.49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17(7.91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21(9.77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII/IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e198(92.09%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2598(86.51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e198(92.09%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e194(90.23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSize\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63(\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85(60)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63(\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e65(60)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocalized\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60(27.91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1173(39.06%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60(27.91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57(26.51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReginal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104(48.37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1331(44.32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e104(48.37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e98(45.58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51(23.72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e499(16.62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51(23.72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e60(27.91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e176(81.86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2634(87.71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e176(81.86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e171(79.53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39(18.14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e369(12.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39(18.14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e44(20.47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResection\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncomplete\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90(41.86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e437(14.55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90(41.86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e82(38.14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComplete\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e125(58.14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2566(85.45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e125(58.14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e133(61.86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChemotherapy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75(34.88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e485(16.15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75(34.88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e81(37.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e140(65.12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2518(83.85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e140(65.12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e134(62.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eRT: Radiotherapy; NRT: Non-Radiotherapy; PS: Primary Site; MP: Multiple Primary tumor; a: Abnormal distribution Quantitative data, M (IQR)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe Kaplan-Meier method was employed to estimate the survival rate and median survival time (MST), and the Log-rank test was used to compare the long-term survival difference. Univariate and Multivariate analyses were conducted using the Cox proportional hazard regression model. Covariates with p\u0026thinsp;\u0026lt;\u0026thinsp;0.1 in the univariate analysis and identified as confounding variables based on the DAG were included in the multivariate analysis. Diagnostic tests for the Cox model included testing the proportional hazards assumption, examining multicollinearity among the independent variables, and scrutinizing the linear relationship between the quantitative independent variables and the outcome. A variance inflation factor (VIF) below 10 demonstrates the absence of multicollinearity. A scatter plot of Age and Size against Martingale residuals showed a linear trend. The proportional hazards assumption was tested by assessing the statistical significance of the interactions between follow-up time and the covariables. Given the presence of time-dependent variables in the covariates, the Time-Dependent Cox Regression Model was employed. Sensitivity analysis was conducted on the conclusions using subgroup analysis, competitive risk analysis (cmprsk v2.2-11 package, R version 4.3.2), and complete dataset analysis. Statistical significance was determined at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 using a two-tailed test. Statistical analyses were performed using the SPSS software (version 26, IBM Corp) and R version 4.3.2.\u003c/p\u003e \u003cp\u003e All experiments and methods detailed in this study were conducted in compliance with the relevant guidelines and regulations as set forth by the SEER database.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eDemographics\u003c/h2\u003e \u003cp\u003eOf the 5216 patients, 3218 were selected for analysis. In the unmatched cohort, patients who underwent radiotherapy tended to be older, had tumors located in the axial, smaller tumor size, regional invasion, incomplete resection, and no chemotherapy. Following PSM, 430 patients were included in the analysis, with 215 in both the Radiotherapy and Non-Radiotherapy groups. The covariates were effectively balanced, indicating no substantial differences in demographic or tumor-related variables between the groups (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cb\u003eComparisons of baseline characteristics of patients in Radiotherapy and Non-Radiotherapy groups before and after PSM\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eComparison of survival\u003c/h2\u003e \u003cp\u003eThe MST for all patients was 38 months (95% CI, 28.9\u0026ndash;47.1), with a 5-year OS of 43.5%. In the Radiotherapy group, the MST was 34 months (95% CI, 24.4\u0026ndash;43.6), while in the Non-Radiotherapy group, it was 42 months (95% CI, 16.7\u0026ndash;67.3). The 5-year OS in the two groups were 39.1% and 47.5%, respectively. The Log-Rank test revealed no significant difference between the two groups (χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;2.029, p\u0026thinsp;=\u0026thinsp;0.154) (eTable 1, Fig.\u0026nbsp;3a).\u003c/p\u003e \u003cp\u003eThe cancer-specific MST for all patients was 52 months (95% CI, 33.7\u0026ndash;70.3), with a 5-year CSS of 48.5%. In the Radiotherapy group, the cancer-specific MST was 43 months (95% CI, 25.7\u0026ndash;60.3 months), while in the Non-Radiotherapy group, it was 65 months (95% CI, 33.0\u0026ndash;97.0 months). The 5-year CSS in the two groups were 44.9% and 51.9%, respectively. The Log-Rank test revealed no significant difference between the two groups (χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.826, p\u0026thinsp;=\u0026thinsp;0.363) (eTable 1, Fig.\u0026nbsp;3b).\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eFigure 3. Comparison of Kaplan-Meier survival curves between the Radiotherapy group and the Non-Radiotherapy group. a, For Overall survival; b, For Cancer-specific survival\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eUnivariate and Multivariate analyses\u003c/h2\u003e \u003cp\u003eCox proportional hazards regression analyses for OS indicated that Age, Stage, Resection, Chemotherapy, and Radiation were time-dependent variables. Univariate and Multivariate analyses were conducted using the Time-Dependent Cox Regression Model. The results showed a lower hazard of death in females compared to males (HR, 0.758; 95% CI, 0.592 to 0.969). Hazards of death increased with Age (HR, 1.024; 95% CI, 1.018 to 1.029), Grade (HR, 1.949; 95% CI, 1.105 to 3.437), and Size (HR, 1.005; 95% CI, 1.002 to 1.008). The hazard of death significantly increased with the Distance Stage (HR, 3.684; 95% CI, 2.532 to 5.358). Complete tumor Resection was associated with a reduced hazard of death (HR, 0.290; 95%CI, 0.143 to 0.587), with the reduction increasing over time (HR, 1.345; 95%CI, 1.071 to 1.689). The hazard of death increased over time in patients undergoing Radiation (HR, 1.102; 95% CI, 1.021 to 1.190), although no statistical significance was found (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). An HR calculation based on the time-dependent variable Radiation revealed HR\u0026thinsp;\u0026lt;\u0026thinsp;1 before 9 months of OS, hinting a benefit of receiving radiotherapy during this period (eFigure 3).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and Multivariate analyses for overall survival of the osteosarcoma patients after PSM\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMultivariate\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.036(1.019\u0026ndash;1.054)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.024(1.018\u0026ndash;1.029)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge*Ln(T)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.994(0.989\u0026ndash;0.999)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.242\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.748(0.588\u0026ndash;0.952)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.758(0.592\u0026ndash;0.969)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eNot selected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.868(0.590\u0026ndash;1.278)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.033(0.678\u0026ndash;1.574)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.011(0.776\u0026ndash;1.316)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGrade\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.693(1.542\u0026ndash;4.702)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.949(1.105\u0026ndash;3.437)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSize\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.006(1.003\u0026ndash;1.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.005(1.002\u0026ndash;1.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocalized\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReginal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.695(1.448\u0026ndash;5.016)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.339(0.967\u0026ndash;1.855)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.638(3.094\u0026ndash;24.116)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.684(2.532\u0026ndash;5.358)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStage*Ln(T)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.835(0.710\u0026ndash;0.983)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.535(1.166\u0026ndash;2.021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.423\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResection\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.338(0.169\u0026ndash;0.674)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.290(0.143\u0026ndash;0.587)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResection*Ln(T)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.415(1.129\u0026ndash;1.772)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.345(1.071\u0026ndash;1.689)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChemotherapy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.355(0.177\u0026ndash;0.711)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.767\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChemotherapy*Ln(T)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.339(1.064\u0026ndash;1.684)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.758\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRadiation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.552(0.280\u0026ndash;1.090)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRadiation*Ln(T)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.301(1.045\u0026ndash;1.620)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.102(1.021\u0026ndash;1.190)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003ePS: Primary Site; MP: Multiple Primary tumor; NA: In Univariate analysis, α\u0026thinsp;=\u0026thinsp;0.1, while in Multivariate analysis, α\u0026thinsp;=\u0026thinsp;0.05.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFor CSS, Age, Stage, Resection, and Chemotherapy were identified as time-dependent variables. The results demonstrated a lower hazard of cancer-specific death in females than in males (HR, 0.731; 95% CI, 0.557 to 0.958). The hazard of cancer-specific death increased with Age (HR, 1.045; 95% CI, 1.026 to 1.065) but decreased with follow-up time (HR, 0.992; 95% CI, 0.985 to 0.998). Hazards increased with Grade (HR, 1.921; 95% CI, 1.037 to 3.558) and Size (HR, 1.004; 95% CI, 1.000 to 1.007). Advanced staging correlated with higher hazards: Regional (HR, 3.103; 95% CI, 1.434 to 6.717), Distance (HR, 16.377; 95% CI, 4.538 to 59.104), but decreased over time (HR, 0.774; 95% CI, 0.630 to 0.951). No statistically significant difference was found in the hazard of death associated with radiotherapy (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and Multivariate analyses for cancer-specific survival of the osteosarcoma patients after PSM\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMultivariate\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.040(1.020\u0026ndash;1.061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.045(1.026\u0026ndash;1.065)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge*Ln(T)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.991(0.984\u0026ndash;0.997)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.992(0.985\u0026ndash;0.998)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.693(0.530\u0026ndash;0.905)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.731(0.557\u0026ndash;0.958)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eNot selected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.796(0.512\u0026ndash;1.238)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.085(0.691\u0026ndash;1.703)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.880(0.664\u0026ndash;1.167)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.220\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGrade\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.613(1.425\u0026ndash;4.790)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.921(1.037\u0026ndash;3.558)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSize\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.006(1.004\u0026ndash;1.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.004(1.000-1.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocalized\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReginal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.501(1.659\u0026ndash;7.389)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.103(1.434\u0026ndash;6.717)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.018(4.321\u0026ndash;52.199)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.377(4.538\u0026ndash;59.104)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStage*Ln(T)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.781(0.639\u0026ndash;0.954)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.774(0.630\u0026ndash;0.951)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.914(0.656\u0026ndash;1.273)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eNot selected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResection\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.314(0.140\u0026ndash;0.703)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResection*Ln(T)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.533(1.169\u0026ndash;2.010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.492\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChemotherapy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.183(0.078\u0026ndash;0.428)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.855\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChemotherapy*Ln(T)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.810(1.351\u0026ndash;2.426)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.272\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRadiation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.126(0.870\u0026ndash;1.459)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.184\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003ePS: Primary Site; MP: Multiple Primary tumor; NA: In Univariate analysis, α\u0026thinsp;=\u0026thinsp;0.1, while in Multivariate analysis, α\u0026thinsp;=\u0026thinsp;0.05.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cb\u003eUnivariate and Multivariate analyses for overall survival of the osteosarcoma patients after PSM\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u003cb\u003eUnivariate and Multivariate analyses for cancer-specific survival of the osteosarcoma patients after PSM\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup Analysis\u003c/h2\u003e \u003cp\u003eThe subgroup analysis conducted in this study was a post-hoc analysis aimed at evaluating the robustness of the conclusions. The optimal cutoff values for Age and Size were determined using the ROC curve. The area under the curve (AUC) was 0.657 (95%CI: 0.606\u0026ndash;0.709) for Age, with the identified critical age point of 50.5 years based on the maximal Youden index, corresponding to a sensitivity of approximately 53.2% and specificity of about 76.3%. Regarding Size, the AUC was 0.616 (95% CI: 0.560\u0026ndash;0.672), with the optimal critical point of 54.5 mm, resulting in a sensitivity of around 67.3% and specificity of approximately 53.3%.\u003c/p\u003e \u003cp\u003eThe p-value for the interaction was calculated using a likelihood ratio test, which compared the main regression model with the interaction model. The findings demonstrated a consistent hazard of OS and CSS from radiotherapy across all six subgroups, with no significant difference (eFigure 4, 5).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCompetitive risk analysis\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eApplying Fine and Gray\u0026rsquo; s method to build the regression model with all variables, the results indicated that Age (HR, 1.790; 95%CI, 1.322 to 2.423), Sex (HR, 0.703; 95%CI, 0.532 to 0.930), Size (HR, 1.432; 95%CI, 1.030 to 1.992), and Stage (HR, 1.907; 95%CI, 1.493 to 2.436) are significant independent predictors of CSS. Notably, Radiation (HR, 1.204; 95%CI, 0.922 to 1.573) did not seem to influence CSS, which is consistent with the conclusions of the Cox regression analysis (eTable 2).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eComplete dataset analyses\u003c/h2\u003e \u003cp\u003eA total of 2062 cases with complete information were analyzed. In the unmatched cohort, results revealed that patients who received radiotherapy were more likely to be older, have tumors in the axial region, smaller tumor size, regional invasion, multiple primary tumors, incomplete resection, and no chemotherapy. PSM was then performed, resulting in 272 patients being 1:1 matched. After matching, the covariates were well balanced, showing no significant differences between the two groups (eTable 3). The comparison of OS (χ\u0026sup2; = 4.845, p\u0026thinsp;=\u0026thinsp;0.028) and CSS (χ\u0026sup2; = 3.538, p\u0026thinsp;=\u0026thinsp;0.060) by Log-rank test indicated no significant difference (eFigure 6). Subsequently, both univariate and multivariate analyses, using the Time-Dependent Cox Regression Model, showed that the hazard of overall mortality (HR, 1.122; 95% CI, 1.016 to 1.239) and cancer-specific mortality (HR, 1.124; 95% CI, 1.007 to 1.256) from radiation increases over time. However, there were no significant differences (eTable 4, 5).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003ePatients with positive surgical margins have a higher rate of local recurrence, which is an crucial adverse prognostic factor\u003csup\u003e18, 19, 20, 21, 22, 23\u003c/sup\u003e, hinting potential benefits of postoperative radiotherapy for incomplete resection\u003csup\u003e8, 9, 10, 11\u003c/sup\u003e. Additionally, studies suggests that radiotherapy may be more efficacious in patients with smaller tumor burden, favorable response to chemotherapy, or those suitable for complete resection\u003csup\u003e9, 12, 24, 25\u003c/sup\u003e. However, these findings show local benefits, while the benefits of survival remains contentious. This study aimed to investigate the potential correlation between postoperative radiotherapy and survival by analyzing a large sample of patients with primary osteosarcoma.\u003c/p\u003e \u003cp\u003eSince the establishement of standard treatment for osteosarcoma involving surgery and chemotherapy in the 1980s, subsequent research on modifying these protocols has not significantly improved overall survival\u003csup\u003e26, 27, 28\u003c/sup\u003e. The 5-year survival rate for localized osteosarcoma patients ranges from 50\u0026ndash;70%, whereas for those with metastatic tumors, it drops to 20\u0026ndash;30%\u003csup\u003e3, 5, 6, 29, 30, 31\u003c/sup\u003e. Our findings showed that the 5-year OS was 50.7% for localized osteosarcoma and 22.2% for metastatic osteosarcoma. This underscores the limitations of traditional comprehensive treatment on survival. There was an uncorrelated impact of postperative radiotherapy on survival in both OS (χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;2.029, p\u0026thinsp;=\u0026thinsp;0.154) and CSS (χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.826, p\u0026thinsp;=\u0026thinsp;0.363). Further univariate and multivariate analyses demonstrated that Age, Sex, Size, Grade, Resection, and Stage serve were independent prognostic factors for OS, while Age, Sex, Size, Grade, and Stage for CSS, consistent with previous findings\u003csup\u003e6, 7, 15, 18, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41\u003c/sup\u003e. Sensitivity analyses ensures the robustness of the conclusion.\u003c/p\u003e \u003cp\u003eA study conducted by Guadagnolo compared the effect of adjuvant radiotherapy in 119 patients with positive surgical margins for head and neck osteosarcoma\u003csup\u003e11\u003c/sup\u003e. Of these patients, 23% received postoperative radiotherapy. The results indicated that combined radiotherapy improved overall survival. However, the patients enrolled in this study spanned from 1960 to 2007, and only 47% administrated chemotherapy. Significant advancements in osteosarcoma chemotherapy in recent decades warrant caution when assessing whether the conclusion accurately represents the clinical reality. Conversely, a meta-analysis evaluating the role of adjuvant radiotherapy/chemotherapy in the management of head and neck osteosarcoma demonstrated that the addition of adjuvant treatment led to lower survival compared to surgery alone\u003csup\u003e16\u003c/sup\u003e. However, it is noteworthy that most reported cases did not provide information about the surgical margins, which is a crucial factor influencing survival. Studies by the Cooperative Osteosarcoma Study Group revealed that patients with unresectable and incompletely resected lesions had a more favorable prognosis when they received radiotherapy\u003csup\u003e8, 42\u003c/sup\u003e. The development of radiotherapy technology has rendered proton and heavy-ion therapy a feasible choice for these patients, demonstrating encouraging outcomes\u003csup\u003e10, 43, 44\u003c/sup\u003e. Nevertheless, the conclusions regarding the prognostic value of radiotherapy have not been fully substantiated due to the potential confounding variables in these studies.\u003c/p\u003e \u003cp\u003eIntriguingly, despite the rigorous control of all confounding variables, a puzzling observation arises from the Kaplan-Meier survival curve, indicating that patients who received radiotherapy exhibit lower survival rates. Further analysis demonstrated a short-term survival benefit at 9 months follow-up, this advantage did not persist beyond that period. The temporary survival benefits can be attributed to enhanced local control resulted in prolonged overall survival\u003csup\u003e45\u003c/sup\u003e. Nevertheless, the long-term survival disadvantage remains unexplained, even though subgroup analysis indicated potential survival benefits in lower-grade cases. Possible reasons for this discrepancy may lie in unrecorded factors in the SEER database impacting survival time, such as the number of metastases, surgical resectability of metastases, and response to chemotherapy\u003csup\u003e46, 47, 48, 49, 50\u003c/sup\u003e. This emphasizes the need for a more thorough and meticulous study in the future.\u003c/p\u003e \u003cp\u003eThis report presents the largest study on the impact of postoperative radiotherapy on the survival of patients with primary osteosarcoma. We utilized the SEER database, a population-based database maintained by the National Cancer Institute, which collects cancer patient information from 18 states representing 28% of the U.S. population. Due to its targeted sampling method, this database includes a higher proportion of minority ethnic groups and to some extent represents the entire U.S. population. Our findings suggest that race does not impact survival, supporting the generalization of our conclusions to all osteosarcoma patients in the United States. Meanwhile, the study used DAG for causal pathways and PSM to balance covariates between groups, thus enhancing the study\u0026rsquo;s validity and accuracy. Furthermore, the robustness of the conclusions was confirmed through subgroup analysis, competing risk analysis, and analysis of the complete dataset.\u003c/p\u003e \u003cp\u003eRegretfully, this study faces several challenging limitations. Firstly, this was a retrospective cohort study in which researchers were unable to allocated exposure to individuals in the population prior to the study. Despite balancing known confounding variables through PSM, potential factors that could influence the results were not accounted for, such as the degree of tumor necrosis after chemotherapy, which is a significant prognostic factor for survival\u003csup\u003e7, 46\u003c/sup\u003e, and patients with a lower degree of necrosis were more likely to be chosen for postoperative radiotherapy. This makes it challenging to determine the causal relationship between radiation and survival, and demonstrates only a correlation. Addressing selection and confounding biases effectively to clarify the causal relationships can only be achieved through prospective randomized controlled trials. Additionally, the 17-year time span of patient enrollment in this retrospective study may have caused an uncontrollable time deviation. This is reflected in the development of radiotherapy techniques like proton and heavy-ion radiation, causing patients to receive different types of techniques at different time points of enrollment. Moreover, other potential risk factors, treatment strategies, and observation methods may chang over time. Although this was corroborated by the time-dependent variable analysis in this study, the lack of detailed variable records unavoidably affected the comparison and interpretation of results.\u003c/p\u003e \u003cp\u003eUltimately, osteosarcoma is a common bone malignancy that affects the long-term survival of patients. Despite tremendous efforts over the past few decades, the survival have not improved significantly. Enhancing survival rates has been a long-standing challenge. Although our findings indicate that postoperative radiotherapy did not result in a survival benefit, we further confirmed that the traditional multimodal treatment approach primarily based on surgery, radiotherapy, and chemotherapy, cannot improve survival. We recommend the management of all osteosarcoma patients by a multidisciplinary team and, based on careful selection of patients who may benefit from postoperative radiotherapy, expand research to consider new multimodal treatment approaches involving targeted therapy and immunotherapy to explore their potential benefits on survival.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eHXG designs\u0026nbsp;the\u0026nbsp;framework, analyzes data, interprets findings, and drafts; YZD conducts\u0026nbsp;a\u0026nbsp;detailed literature review,\u0026nbsp;elucidating\u0026nbsp;the research background; XCL, JXC, HLZ, JL and KJZ collect and organize data. All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding stastement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grants from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Considerations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Institutional Review Committee waived the need for ethical approval, citing the global accessibility of the SEER database for researchers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest disclosures\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no potential conflicts of interest to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the data used in this study can be freely accessed from the SEER program (https://seer.cancer.gov/).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMirabello L, Troisi RJ, Savage SA. 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Cancer 2006, 106(2): 403\u0026ndash;412.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Osteosarcoma, Postoperative Radiotherapy, Survival, Propensity Score Matching, SEER","lastPublishedDoi":"10.21203/rs.3.rs-4433658/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4433658/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePURPOSE\u003c/h2\u003e \u003cp\u003eTo evaluate the impact of postoperative radiotherapy on survival in osteosarcoma patients.\u003c/p\u003e\u003ch2\u003eMATERIALS AND METHODS\u003c/h2\u003e \u003cp\u003eTotal of 3218 participants aged 3\u0026ndash;85 years with primary bone and joint osteosarcoma, primary site resection, and/or postoperative radiotherapy were enrolled from the Surveillance, Epidemiology, and End Results (SEER) database. Multiple imputations were utilized to fill in missing data, a directed acyclic graph was constructed to identify causal pathways, and propensity score matching at a ratio of 1:1 was employed to balance covariate characteristics. The Kaplan-Meier method was utilized to estimate survival rates, which were compared the rates using the Log-rank test, and univariate and multivariate analyses were performed using the Cox proportional hazards regression model. Subsequently, sensitivity analyses were conducted on the conclusions using subgroup analysis, competitive risk analysis, and complete dataset analysis.\u003c/p\u003e\u003ch2\u003eRESULTS\u003c/h2\u003e \u003cp\u003eA total of 430 patients in the analysis, with 215 in the Radiotherapy and Non-Radiotherapy groups. The 5-year overall survival rates (OS) were 39.1% and 47.1% in the two groups, and the 5-year cancer-specific survival rates (CSS) were 45.5% and 51.8%, respectively. Comparison of the survival rate between the two groups using the Log-rank test yielded non-significant differences (OS, χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;2.029, p\u0026thinsp;=\u0026thinsp;0.154; CSS, χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.826, p\u0026thinsp;=\u0026thinsp;0.363). Both univariate and multivariate analyses revealed no significant differencse in survival associated with radiotherapy. Moreover, the sensitivity analysis findings were consistent with these conclusions.\u003c/p\u003e\u003ch2\u003eCONCLUSION\u003c/h2\u003e \u003cp\u003ePostoperative radiotherapy for primary bone and joint osteosarcoma has not shown survival benefits, and its value should be reassessed in multidisciplinary management.\u003c/p\u003e","manuscriptTitle":"Impact of Postoperative Radiotherapy on Survival in Primary Osteosarcoma: A population-based study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-04 19:13:01","doi":"10.21203/rs.3.rs-4433658/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0d768e07-752b-4b07-a51f-fc0aa670ed86","owner":[],"postedDate":"June 4th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":32506374,"name":"Biological sciences/Cancer/Bone cancer"},{"id":32506375,"name":"Biological sciences/Cancer"},{"id":32506376,"name":"Biological sciences/Cancer/Cancer therapy/Radiotherapy"}],"tags":[],"updatedAt":"2025-01-20T04:23:35+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-04 19:13:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4433658","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4433658","identity":"rs-4433658","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}