Development and Validation of a Nomogram for Predicting Cancer-specific Survival in Patients with Primary Osteosarcoma of Long Bones: A SEER- Based Study

Development and Validation of a Nomogram for Predicting Cancer-specific Survival in Patients with Primary Osteosarcoma of Long Bones: A SEER- 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 Research Article Development and Validation of a Nomogram for Predicting Cancer-specific Survival in Patients with Primary Osteosarcoma of Long Bones: A SEER- Based Study Shilong Wang, Long Jia This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9410266/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Background Existing prognostic models for primary osteosarcoma of long bones often lack temporal external validation, making them susceptible to calibration drift. This study aimed to develop, temporally validate, and recalibrate a nomogram to predict cancer-specific survival (CSS) for these patients utilizing the Surveillance, Epidemiology, and End Results (SEER) database. Methods Patients diagnosed between 2010 and 2017 were identified and divided into a training cohort (2010–2015, n = 631) and a temporal external validation cohort (2016–2017, n = 210). Feature selection was performed by comparing LASSO penalized regression and traditional stepwise Cox regression. Model performance was evaluated using the concordance index (C-index), time-dependent receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA). Statistical recalibration was applied to address calibration drift in the validation cohort. Results Seven independent prognostic factors—age, sex, tumor grade, T stage, N stage, M stage, and surgery—were identified. Based on Occam's razor principle and clinical utility, the traditional 7-variable Cox model (C-index = 0.751) was selected over the LASSO model (C-index = 0.753) to construct the final nomogram. In the temporal external validation cohort, the nomogram maintained robust discrimination with an external C-index of 0.706. The initial calibration slope of 0.724 indicated statistical overfitting. However, after adjusting the baseline risk and applying a shrinkage factor, the recalibrated curve aligned perfectly with actual modern clinical outcomes. Conclusion The developed 7-variable nomogram serves as an accurate and reliable tool for predicting CSS in patients with primary osteosarcoma of the long bones. Rigorous temporal external validation and recalibration ensure its adaptability and utility in formulating individualized treatment regimens for contemporary patients. Osteosarcoma Long bones Nomogram Temporal validation Recalibration SEER Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Osteosarcoma is the most common primary malignant bone tumor, predominantly affecting children, adolescents and young adults[ 1 ]. Despite advancements in multimodal treatments as incorporating neoadjuvant chemotherapy and surgical resection[ 2 ], the long-term survival rates for osteosarcoma patients have largely plateaued over the past few decades[ 3 ]. Furthermore, due to the high biological heterogeneity of the disease, clinical outcomes vary significantly even among patients with similar clinicopathological features[ 4 ]. Traditional staging systems as the American Joint Committee on Cancer (AJCC) staging and the Musculoskeletal Tumor Society (MSTS) staging primarily rely on tumor extent and metastasis to stratify patients[ 5 ], Although these systems provide baseline prognostic value, previous studies have shown that their predictive accuracy is limited and similar across different systems. Crucially, researchers have highlighted that the prognostic performance of these traditional systems could be significantly improved by incorporating nonanatomic prognostic variables into their algorithms[ 5 ]. Consequently, relying solely on anatomic staging often fails to provide precise, individualized survival predictions. Nomograms have emerged as intuitive and effective statistical models that integrate multiple prognostic variables to quantify individualized risk[ 6 ]. In recent years, several nomograms have been developed for osteosarcoma[ 7 , 8 ], predictive models specifically tailored to primary osteosarcoma of long bones remain relatively scarce. Furthermore, although existing models provide valuable prognostic insights, there is an opportunity to further enhance their generalizability. Many current models rely primarily on internal validation or random split-sample validation within the same time frame. While these approaches are undeniably effective, the addition of temporal external validation is increasingly recognized as crucial. Without it, models may naturally encounter subtle calibration drift when applied to modern patient cohorts, a phenomenon often presenting as "regression towards the mean" due to temporal shifts in baseline survival and the inherent challenges of statistical overfitting[ 9 ]. Therefore, this study aimed to develop and validate a prognostic nomogram for predicting survival outcomes in patients with primary osteosarcoma of long bones, utilizing a large population-based cohort from the Surveillance, Epidemiology, and End Results (SEER) database, which provide cancer data to the public for clinical studies[ 10 ]. To ensure methodological rigor, we compared a machine learning approach (LASSO penalized regression) with traditional Cox stepwise regression for optimal feature selection. More importantly, we employed an independent temporal cohort (2016–2017) for external validation and applied a statistical recalibration framework to explicitly correct calibration drift. By doing so, we sought to provide clinicians with a highly accurate, contemporary, and pragmatic tool to assist in personalized treatment decision-making and patient counseling. 2. Methods and materials 2.1 Study Population Data for this retrospective study were extracted from the Surveillance, Epidemiology, and End Results (SEER) database using SEER*Stat software (version 9.0.42). We utilized the "Incidence - SEER Research Data, 17 Registries, Nov 2022 Sub (2000–2020)" dataset, which covers approximately 48.0% of the U.S. population. Patient selection followed predefined inclusion criteria to ensure consistency, reproducibility, and methodological rigor. Eligible patients met all of the following criteria: (1) Patients diagnosed with osteosarcoma were identified according to the International Classification of Diseases for Oncology, Third Edition morphology codes, restricted to malignant behavior (/3). The following histological subtypes were included:9180–9187/3, 9192–9194/3; (2) Patients with primary tumors located in long bones were included, defined using SEER primary site codes:C40.0 (long bones of upper limb, scapula, and associated joints) and C40.2 (long bones of lower limb and associated joints); (3) Cases were limited to those with microscopic confirmation, ensuring pathological verification of diagnosis and minimizing misclassification bias; (4) Patients diagnosed between 2010 and 2017 were included; (5) Only patients with one primary tumor. Patients were excluded if they met any of the following conditions: (1) Patients with a recorded survival time of less than 1 month or coded as 0 months were excluded to reduce bias from peri-diagnostic mortality. (2) Patients with missing essential clinicopathological information were excluded, including race, tumor grade, stage, or surgical treatment status. A detailed flowchart of the patient selection process is presented in Fig. 1 . 2.2 Data Collection The following variables were identified from the dataset: age at diagnosis (0–9, 10–24, or ≥ 24 years), sex (male or female), race (white, black, or others), marital status (married or unmarried), laterality (left, right, or others), tumor grade (I/II, III, or IV), T stage, N stage, M stage, surgery (limb salvage surgery, amputation, local excision, or no surgery), radiation therapy (yes or no/unknown), and chemotherapy (yes or no). To perform a rigorous temporal external validation, the eligible patients were divided into two independent cohorts based on the year of diagnosis: patients diagnosed between 2010 and 2015 were allocated to the training cohort (n = 631), while those diagnosed between 2016 and 2017 were assigned to the validation cohort (n = 210). 2.3 Statistical Analysis and Feature Selection To ensure the robustness of the prognostic feature selection, mitigate multicollinearity, and avoid overfitting, two parallel variable selection strategies were employed and compared in the training cohort: Machine Learning Approach: A least absolute shrinkage and selection operator (LASSO) Cox regression was performed using the ‘glmnet’ R package. The optimal penalty parameter (λ) was determined via 10-fold cross-validation to select the most predictive feature set (lambda.min). Traditional Stepwise Approach: Univariate Cox proportional hazards regression was first used to screen potential prognostic factors (P < 0.05). Significant variables were then incorporated into a multivariate Cox regression model. A backward stepwise elimination based on the likelihood ratio test (drop1 function) was utilized to retain only the independent core prognostic factors (P < 0.05). Following Occam's razor principle, the final model was selected by comparing the Harrell's Concordance index (C-index) and Area Under the Curve (AUC) of both strategies, favoring the more parsimonious model without significantly compromising predictive accuracy. 2.4 Model Validation, Recalibration, and Clinical Utility Model discrimination and calibration were evaluated using the rms and timeROC packages. Internal validation was performed using bootstrap resampling (1,000 iterations). Temporal external validation was conducted to assess the model's generalizability. To identify potential calibration drift or regression to the mean, the calibration slope was calculated in the validation cohort. If the slope deviated from 1.0, a statistical recalibration was performed by using the original linear predictor as a single covariate to fit a new Cox model in the validation cohort, thereby generating a recalibrated curve. Finally, Decision Curve Analysis (DCA) was performed to quantify the net clinical benefit across a range of threshold probabilities. All statistical analyses were performed using R software (version 4.2.1). A two-sided P-value < 0.05 was considered statistically significant. 3. Results 3.1 Baseline Characteristics of the Study Population Among 841 patients, 210 (25.0%) were assigned to the testing cohort and 631 (75.0%) to the training cohort. The mean (SD) survival was 37.7 (15.1) months in the testing cohort and 66.3 (37.3) months in the training cohort (p < 0.001). The distribution of age categories was similar between cohorts (p = 0.96). The testing cohort had a higher proportion of male patients (62.4% vs 55.0%; p = 0.06) and a different racial distribution (p = 0.005), with a lower proportion of Black patients (9.0% vs 15.7%) and a higher proportion classified as “Other” (17.1% vs 10.6%) compared with the training cohort. The distribution of T stage differed between cohorts (p = 0.02), with a higher proportion of T3 tumors in the testing cohort (7.6% vs 3.2%). No significant between-cohort differences were observed for primary tumor site, tumor grade, N stage, M stage, type of surgery, use of radiotherapy, use of chemotherapy, marital status, tumor laterality, cancer-specific survival status, or overall survival status (all p > 0.05). Table 1 Variables Total (n = 841) Testing (n = 210) Training (n = 631) Statistic P Survival months , Mean ± SD 59.18 ± 35.40 37.71 ± 15.06 66.32 ± 37.29 t=-15.79 < .001 Age , n(%) χ²=0.08 0.963 ≥ 25 223 (26.52) 55 (26.19) 168 (26.62) 0 ~ 9 88 (10.46) 23 (10.95) 65 (10.30) 10 ~ 24 530 (63.02) 132 (62.86) 398 (63.07) Sex , n(%) χ²=3.51 0.061 Female 363 (43.16) 79 (37.62) 284 (45.01) Male 478 (56.84) 131 (62.38) 347 (54.99) Race , n(%) χ²=10.43 0.005 Black 118 (14.03) 19 (9.05) 99 (15.69) Others 103 (12.25) 36 (17.14) 67 (10.62) White 620 (73.72) 155 (73.81) 465 (73.69) Primary Site , n(%) χ²=0.05 0.820 Lower limb 709 (84.30) 176 (83.81) 533 (84.47) Upper limb 132 (15.70) 34 (16.19) 98 (15.53) Grade , n(%) χ²=0.01 0.997 Ⅰ/Ⅱ 95 (11.30) 24 (11.43) 71 (11.25) Ⅲ 265 (31.51) 66 (31.43) 199 (31.54) Ⅳ 481 (57.19) 120 (57.14) 361 (57.21) T Stage , n(%) χ²=8.31 0.016 T1 269 (31.99) 60 (28.57) 209 (33.12) T2 536 (63.73) 134 (63.81) 402 (63.71) T3 36 (4.28) 16 (7.62) 20 (3.17) N Stage , n(%) χ²=1.72 0.190 N0 821 (97.62) 202 (96.19) 619 (98.10) N1 20 (2.38) 8 (3.81) 12 (1.90) M Stage , n(%) χ²=1.54 0.215 M0 685 (81.45) 165 (78.57) 520 (82.41) M1 156 (18.55) 45 (21.43) 111 (17.59) Surgery , n(%) χ²=0.43 0.934 Amputation 168 (19.98) 42 (20.00) 126 (19.97) Limb Salvage Surgery 552 (65.64) 140 (66.67) 412 (65.29) Local Excision 69 (8.20) 15 (7.14) 54 (8.56) No surgery 52 (6.18) 13 (6.19) 39 (6.18) Radiotherapy , n(%) χ²=0.05 0.815 No/Unknown 815 (96.91) 203 (96.67) 612 (96.99) Yes 26 (3.09) 7 (3.33) 19 (3.01) Chemotherapy , n(%) χ²=2.24 0.135 No 96 (11.41) 18 (8.57) 78 (12.36) Yes 745 (88.59) 192 (91.43) 553 (87.64) Marital Status , n(%) χ²=0.97 0.325 Married 113 (13.44) 24 (11.43) 89 (14.10) Unmarried 728 (86.56) 186 (88.57) 542 (85.90) Laterality , n(%) χ²=0.55 0.458 Left 458 (54.46) 119 (56.67) 339 (53.72) Right 383 (45.54) 91 (43.33) 292 (46.28) CSS , n(%) χ²=1.46 0.226 Alive 593 (70.51) 155 (73.81) 438 (69.41) Dead 248 (29.49) 55 (26.19) 193 (30.59) OS , n(%) χ²=2.33 0.127 Alive 544 (64.68) 145 (69.05) 399 (63.23) Dead 297 (35.32) 65 (30.95) 232 (36.77) t: t-test, χ²: Chi-square test SD: standard deviation 3.2 Variable Selection and Model Comparison To ensure the robustness of feature selection, we evaluated both Cox regression and LASSO penalized regression in parallel. Initially, the univariate Cox regression analysis revealed that nine variables—age, sex, race, tumor grade, T stage, N stage, M stage, surgery, and marital status—were significantly associated with cancer-specific survival (CSS) ( P < 0.05). Subsequently, these variables were incorporated into a multivariate Cox regression model. As detailed in Table 2 , 7 variables were ultimately identified as independent prognostic factors for CSS: age, sex, grade, T stage, N stage, M stage, and Surgery. The traditional prognostic model built on these 7 core variables achieved a C-index of 0.751. Simultaneously, we applied LASSO regression for high-dimensional feature reduction (Fig. 2 ). At the lambda.min node, the LASSO model retained a more complex combination of features (including race and marital status), yielding a C-index of 0.753. Model comparison analysis demonstrated that although the LASSO model incorporated more candidate variables, its predictive discrimination (C-index) improved by merely 0.002 compared to the 7-variable model derived from the traditional screening. Furthermore, the time-dependent ROC curves of both models were highly overlapping (Fig. 3 ). Based on Occam's razor principle and clinical utility considerations, and without significantly compromising predictive accuracy, this study ultimately discarded redundant variables. We selected the 7-variable model to construct the final nomogram, aiming to provide a more convenient and efficient tool for clinical decision-making. Table 2 Variables Univariable Cox Multivariable Cox β S.E Z P HR (95%CI) β S.E Z P HR (95%CI) Age ≥25 1.000 (Reference) 1.000 (Reference) 0 ~ 9 -0.695 0.298 -2.329 0.020 0.499 (0.278 ~ 0.896) -1.003 0.341 -2.944 0.003 0.367 (0.188 ~ 0.715) 10 ~ 24 -0.302 0.161 -1.882 0.060 0.739 (0.540 ~ 1.013) -0.396 0.227 -1.745 0.081 0.673 (0.432 ~ 1.050) Sex Female 1.000 (Reference) 1.000 (Reference) Male 0.441 0.150 2.941 0.003 1.554 (1.158 ~ 2.084) 0.348 0.158 2.206 0.027 1.416 (1.039 ~ 1.928) Race Black 1.000 (Reference) Others -0.368 0.297 -1.238 0.216 0.692 (0.387 ~ 1.239) White -0.170 0.191 -0.890 0.374 0.844 (0.580 ~ 1.227) Primary Site Lower limb 1.000 (Reference) Upper limb -0.074 0.208 -0.356 0.722 0.929 (0.618 ~ 1.395) Grade Ⅰ/Ⅱ 1.000 (Reference) 1.000 (Reference) Ⅲ 1.971 0.515 3.828 < .001 7.176 (2.616 ~ 19.683) 2.128 0.567 3.755 < .001 8.401 (2.766 ~ 25.511) Ⅳ 1.954 0.508 3.846 < .001 7.059 (2.607 ~ 19.111) 1.913 0.564 3.389 < .001 6.773 (2.240 ~ 20.476) T Stage T1 1.000 (Reference) 1.000 (Reference) T2 0.437 0.169 2.585 0.010 1.548 (1.111 ~ 2.157) 0.263 0.173 1.521 0.128 1.300 (0.927 ~ 1.824) T3 1.731 0.290 5.972 < .001 5.644 (3.198 ~ 9.959) 1.232 0.310 3.977 < .001 3.428 (1.868 ~ 6.290) N Stage N0 1.000 (Reference) 1.000 (Reference) N1 1.753 0.326 5.375 < .001 5.775 (3.047 ~ 10.944) 0.825 0.371 2.224 0.026 2.283 (1.103 ~ 4.725) M Stage M0 1.000 (Reference) 1.000 (Reference) M1 1.596 0.151 10.595 < .001 4.934 (3.673 ~ 6.629) 1.436 0.165 8.675 < .001 4.202 (3.038 ~ 5.812) Surgery Amputation 1.000 (Reference) 1.000 (Reference) Limb Salvage Surgery -0.675 0.171 -3.945 < .001 0.509 (0.364 ~ 0.712) -0.508 0.176 -2.886 0.004 0.602 (0.426 ~ 0.849) Local Excision -0.240 0.268 -0.894 0.371 0.787 (0.465 ~ 1.331) -0.071 0.274 -0.261 0.794 0.931 (0.544 ~ 1.593) No surgery 1.105 0.256 4.324 < .001 3.019 (1.829 ~ 4.981) 0.890 0.312 2.855 0.004 2.436 (1.322 ~ 4.490) Radiotherapy No/Unknown 1.000 (Reference) 1.000 (Reference) Yes 1.382 0.288 4.797 < .001 3.984 (2.265 ~ 7.008) -0.108 0.350 -0.310 0.757 0.897 (0.452 ~ 1.781) Chemotherapy No 1.000 (Reference) 1.000 (Reference) Yes 0.557 0.278 2.005 0.045 1.745 (1.013 ~ 3.006) -0.343 0.341 -1.007 0.314 0.710 (0.364 ~ 1.384) Marital Status Married 1.000 (Reference) 1.000 (Reference) Unmarried -0.553 0.183 -3.023 0.003 0.575 (0.402 ~ 0.823) -0.258 0.252 -1.020 0.308 0.773 (0.471 ~ 1.268) Laterality Left 1.000 (Reference) Right 0.071 0.144 0.491 0.623 1.073 (0.809 ~ 1.424) HR: Hazard Ratio, CI: Confidence Interval 3.3 Construction and Internal Validation of the Nomogram Based on the seven independent prognostic variables identified (age, sex, tumor grade, T stage, N stage, M stage, and surgery), a prognostic nomogram was constructed to provide individualized predictions of 3-year and 5-year cancer-specific survival (CSS) for patients with primary osteosarcoma of the long bones (Fig. 4 ). In the internal validation, the model demonstrated robust predictive performance with a C-index of 0.751 (SE = 0.016). The 5-year time-dependent ROC curve for the training cohort further confirmed the excellent discriminatory power of the model. Additionally, the 3- and 5-year internal calibration curves generated via bootstrap resampling (Fig. 5 ) revealed a high degree of concordance between the nomogram-predicted survival probabilities and the actual Kaplan-Meier observations, indicating the great accuracy of the model within the development cohort. 3.4 Temporal External Validation and Model Recalibration In the temporal external validation cohort, the nomogram maintained robust discrimination, achieving an external C-index of 0.706. The time-dependent ROC curves (Fig. 6 A) demonstrated that the model preserved its great predictive efficacy in this recent independent cohort. When evaluating the calibration performance in the external validation set (Fig. 6 B), we observed a calibration slope of 0.724. This slope of less than 1.0 indicates a typical degree of statistical overfitting within the training cohort, which translates to slightly pessimistic survival predictions for high-risk patients and overly optimistic predictions for low-risk patients (regression to the mean). To optimize the absolute predictive accuracy for contemporary clinical cohorts, a statistical recalibration was performed. After adjusting the baseline risk and applying a shrinkage factor, the recalibrated curve (Fig. 6 C) aligned perfectly with the 45-degree ideal line, successfully correcting the calibration drift introduced by model translation. 4. Discussion Prognostic assessment is of paramount importance for the treatment, monitoring, and follow-up of cancer patients. In this study, we developed and rigorously validated a prognostic nomogram incorporating seven clinicopathological features to predict the CSS of patients with primary osteosarcoma of the long bones, utilizing a large-scale cohort from the SEER database. Our multivariate analysis results corroborate previous findings. Age is a well-established prognostic factor for numerous malignancies[ 11 , 12 ]. Similarly, our research demonstrated a significant survival advantage in younger patients, particularly those aged 0–9 years. Tumor stage and metastatic status (M stage) remain the most critical risk factors for osteosarcoma[ 13 ]. Prior studies have reported significantly poorer survival outcomes in patients presenting with distant metastasis, which is highly consistent with the HR of 4.2 for M1 stage observed in our model. Regarding treatment, the surgical modality exerts a profound and immediate impact on the survival of osteosarcoma patients. Our data provide robust evidence supporting the prognostic superiority of limb salvage surgery (LSS), which demonstrated a significantly lower mortality risk compared to both amputation and the absence of surgical intervention. While this favorable outcome may be partially attributed to selection bias—as patients with smaller tumor volumes and positive responses to neoadjuvant chemotherapy are more likely to be candidates for LSS—the holistic benefits of the procedure cannot be overlooked. Furthermore, on the premise of ensuring long-term survival, LSS preserves physical function and enhances quality of life while mitigating the psychological and social barriers associated with amputation. Such holistic benefits significantly facilitate patient reintegration into society and overall recovery, ultimately translating into improved prognostic outcomes. Although traditional perspectives favored amputation for its perceived radicality in tumor clearance, advancements in medical technology and disease understanding have demonstrated that the survival rates of LSS are now comparable to those of amputation[ 14 , 15 ]. Notably, the direct correlation between surgical type and survival remains a subject of academic debate, with several studies indicating no significant difference in survival outcomes between the two modalities. Beyond absolute survival, functional recovery is a critical metric of surgical success. A prospective study evaluating functional outcomes via TESS and MSTS scores found that despite a high complication rate in LSS patients, there were no significant functional differences between LSS and amputation groups during a 2-to-7-year follow-up period[ 16 ]. This suggests that the selection of surgical modality should be a multifaceted decision incorporating tumor size, location, and chemotherapy response, rather than relying solely on the surgical type itself. The primary strength and novelty of this study lie in the implementation of temporal external validation and a detailed exploration of model calibration drift. In clinical prediction models, "regression to the mean" and overfitting are common phenomena encountered during external validation. Our validation cohort yielded a calibration slope of 0.724, which objectively reflects the improvement in baseline survival between the 2010–2015 and 2016–2017 periods, as well as slight polarization of model weights. Departing from previous studies that often overlook this issue, we innovatively introduced a recalibration framework. Through a straightforward mathematical shrinkage adjustment, the nomogram’s predicted probabilities were perfectly aligned with the actual outcomes of the modern clinical cohort. This process not only validates that the seven selected variables are core drivers of osteosarcoma prognosis but also highlights the strong adaptive potential of the model for future clinical implementation. This study also has certain limitations. First, its retrospective design inherently leads to potential missing data. Second, the SEER database lacks granular information regarding specific chemotherapy regimens, targeted therapy usage, tumor necrosis rates (histological response), and genetic mutations (e.g., TP53), all of which may significantly influence prognosis. Future prospective, multicenter studies are required to further validate and refine this model. 5. Conclusion In conclusion, age, sex, pathological grade, T-stage, N-stage, M-stage, and surgical approach serve as independent prognostic factors for patients with primary osteosarcoma of the long bones. The nomogram developed in this study demonstrates great discriminative power and clinical utility. Following rigorous external temporal validation and recalibration, this model accurately predicts survival probabilities for contemporary osteosarcoma patients, providing clinicians with an invaluable tool for formulating individualized treatment regimens and follow-up plans. Declarations ACKNOWLEDGMENTS Conflicts of interest: Authors declare no conflicts of interest. Funding: Authors received no specific funding for this work. Data availability: The data that support the findings of this study are publicly available from the Surveillance, Epidemiology, and End Results (SEER) database (https://seer.cancer.gov/). Researchers can obtain full access to the SEER data by submitting a data access request and signing a data-use agreement. The derived data supporting the findings of this study are available from the corresponding author upon reasonable request and can be provided for peer review if required. Ethical statement: This study utilized publicly available, de-identified data from the Surveillance, Epidemiology, and End Results (SEER) database. As the data are fully anonymized and do not contain any personally identifiable information, the requirement for ethical approval and informed consent from an institutional review board or ethics committee was waived, in accordance with local legislation and institutional requirements. This study was conducted in accordance with the principles of the Declaration of Helsinki. Author Contributions Shilong Wang: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, and Writing – original draft. Long Jia: Conceptualization, Project administration, Resources, Supervision, Validation, and Writing – review & editing References Beird HC, Bielack SS, Flanagan AM, Gill J, Heymann D, Janeway KA, Livingston JA, Roberts RD, Strauss SJ, Gorlick R: Osteosarcoma. Nature reviews. Disease primers 2022, 8: 77. Wang X, Zhu K, Hu J, Zhang C: Advances and challenges in the treatment of osteosarcoma. Progress in biophysics and molecular biology 2025, 197: 60-74. Anderson ME: Update on Survival in Osteosarcoma. The Orthopedic clinics of North America 2016, 47: 283-292. Rossi F, Rydzyk MM, Barba L, Malucelli E, Palamà MEF, Gentili C, Mastrogiacomo M, Cedola A, Mancini L, Salomé M, et al: Insights into the osteosarcoma microenvironment: Multiscale analysis of structural and mineral heterogeneity. 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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-9410266","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":636255175,"identity":"c9ff0205-cc39-4559-9617-0a1ab5e8eb6f","order_by":0,"name":"Shilong Wang","email":"","orcid":"","institution":"The First Clinical Medical College of Binzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shilong","middleName":"","lastName":"Wang","suffix":""},{"id":636255176,"identity":"7bfbc837-c14f-4a7e-80f1-dbb0a36fc105","order_by":1,"name":"Long Jia","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYBACxmYGxgNgFjMQfzCwkSNGCwNcC+OMgjRjomw6AGMw83w4nEhQOXM784MDH3fU2puz8x58bGPAnMDAfvjoBvwOYzM4OPPM8cSdzXzJxjkGbHkMPGlpN/BrYTA4zNt2LMHgMI+ZdI4BTzGDBI8ZAS3sH0Ba7IFazH9bGEgkNhDWwgOypYZxA9AWZgYDA6K0FByc2XYA6BceY8kegwRjNkJ+Mew/vvHBx7Y6e3P+M4Yffvz5L8fPfvgYfi0NYOowgwFMhA2fchCQh1B1CC2jYBSMglEwCtABAMPJSQPSWfJpAAAAAElFTkSuQmCC","orcid":"","institution":"The Affiliated Hospital of Binzhou Medical University","correspondingAuthor":true,"prefix":"","firstName":"Long","middleName":"","lastName":"Jia","suffix":""}],"badges":[],"createdAt":"2026-04-14 04:08:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9410266/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9410266/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108969092,"identity":"1752b5ed-0fda-4eeb-8e44-48033ea51eb3","added_by":"auto","created_at":"2026-05-11 10:08:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":154180,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of patient selection from the SEER database.\u003c/strong\u003e The diagram details the specific inclusion and exclusion criteria utilized in this study. Eligible patients with primary osteosarcoma of the long bones were divided into two independent cohorts based on the year of diagnosis: the training cohort (2010–2015, n = 631) and the temporal testing cohort (2016–2017, n = 210).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9410266/v1/7310281a9afedfaded0a95b3.png"},{"id":108969091,"identity":"564b2f21-44f0-490d-930a-15b564484141","added_by":"auto","created_at":"2026-05-11 10:08:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":109877,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFeature selection using the Least Absolute Shrinkage and Selection Operator (LASSO) penalized Cox regression model.\u003c/strong\u003e (A) Selection of the optimal tuning parameter (λ) via 10-fold cross-validation. The partial likelihood deviance curve is plotted against -log(λ). Vertical dotted lines are drawn at the optimal values based on the minimum criteria (lambda.min, left) and 1 standard error of the minimum criteria (lambda.1se, right). (B) LASSO coefficient profiles of the candidate clinicopathological variables. The coefficients of the features are plotted against -log(λ), illustrating the shrinkage of variable weights as the penalization increases.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9410266/v1/49d7b258b55614c39e9b93f9.png"},{"id":108969093,"identity":"06ea9e05-4ea1-40b9-b067-0420e97d9afc","added_by":"auto","created_at":"2026-05-11 10:08:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":65094,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTime-dependent receiver operating characteristic (ROC) curves comparing the predictive performance of the LASSO model and the traditional Cox model in the training cohort.\u003c/strong\u003e The ROC curves and corresponding area under the curve (AUC) values for 3-year (A) and 5-year (B) cancer-specific survival are presented. The trajectories of the two models are highly overlapping, indicating that the parsimonious 7-variable traditional model (blue line) achieves comparable discrimination to the more complex LASSO model (red line) without significantly compromising predictive accuracy.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9410266/v1/c73fcef65a9d228c4a3bae07.png"},{"id":108969100,"identity":"35ebae3a-ac56-43bf-bf54-060290e5871e","added_by":"auto","created_at":"2026-05-11 10:08:09","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":105429,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNomogram for predicting the 3-year and 5-year cancer-specific survival (CSS) of patients with primary osteosarcoma of the long bones.\u003c/strong\u003e The prognostic nomogram incorporates seven independent clinicopathological factors: age, sex, tumor grade, T stage, N stage, M stage, and surgical modality. \u003cstrong\u003eInstructions for use:\u003c/strong\u003e To determine a patient's survival probability, locate their specific value on each variable axis and draw a vertical line upwards to the \"Points\" scale to assign a score. Sum the scores from all seven variables to obtain the \"Total Points\". Finally, draw a vertical line downwards from the \"Total Points\" scale to the bottom axes to estimate the individualized 3-year and 5-year survival probabilities.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9410266/v1/d0d1451634b55eb9460768ad.png"},{"id":108969106,"identity":"19218a55-2c49-456d-8c54-c0fd0ac5e62e","added_by":"auto","created_at":"2026-05-11 10:08:09","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":87279,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInternal calibration curves of the nomogram in the training cohort.\u003c/strong\u003e The calibration plots display the agreement between the nomogram-predicted probabilities and the actual Kaplan-Meier observed outcomes for 3-year (A) and 5-year (B) cancer-specific survival. The diagonal dashed line acts as the ideal reference, representing perfect calibration. The solid red line (with blue error bars indicating 95% confidence intervals) represents the actual apparent performance of the model generated via bootstrap resampling (1,000 iterations). The close alignment between the predicted performance and the ideal reference line demonstrates the excellent internal calibration accuracy of the developed nomogram.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9410266/v1/741e8c750be68a48b3d89854.png"},{"id":108969097,"identity":"ac9b5b5d-1891-4e44-8fa4-97ddab7fc66d","added_by":"auto","created_at":"2026-05-11 10:08:09","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":73307,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTemporal external validation and statistical recalibration of the prognostic nomogram.\u003c/strong\u003e(A) Time-dependent receiver operating characteristic (ROC) curves demonstrating the robust discriminatory performance for 1-year (AUC = 0.847) and 3-year (AUC = 0.763) cancer-specific survival in the external validation cohort. (B) The apparent calibration curve for 3-year survival prior to recalibration. The deviation from the ideal 45-degree reference line (dashed) illustrates typical calibration drift and the \"regression to the mean\" phenomenon encountered during model translation. (C) The recalibrated 3-year calibration curve after adjusting the baseline risk and applying a mathematical shrinkage factor. The recalibrated predicted probabilities (solid red line with blue 95% confidence interval bars) align perfectly with the ideal reference line, indicating excellent absolute predictive accuracy in the contemporary cohort.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-9410266/v1/55d759eeb31d4dcfbc9a8c70.png"},{"id":108969182,"identity":"497cc21a-f06c-46c1-80f9-02642c7ff365","added_by":"auto","created_at":"2026-05-11 10:08:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1160182,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9410266/v1/2e83504f-ac7f-4716-9c9a-d91bff510ea1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and Validation of a Nomogram for Predicting Cancer-specific Survival in Patients with Primary Osteosarcoma of Long Bones: A SEER- Based Study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eOsteosarcoma is the most common primary malignant bone tumor, predominantly affecting children, adolescents and young adults[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Despite advancements in multimodal treatments as incorporating neoadjuvant chemotherapy and surgical resection[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], the long-term survival rates for osteosarcoma patients have largely plateaued over the past few decades[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Furthermore, due to the high biological heterogeneity of the disease, clinical outcomes vary significantly even among patients with similar clinicopathological features[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Traditional staging systems as the American Joint Committee on Cancer (AJCC) staging and the Musculoskeletal Tumor Society (MSTS) staging primarily rely on tumor extent and metastasis to stratify patients[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], Although these systems provide baseline prognostic value, previous studies have shown that their predictive accuracy is limited and similar across different systems. Crucially, researchers have highlighted that the prognostic performance of these traditional systems could be significantly improved by incorporating nonanatomic prognostic variables into their algorithms[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Consequently, relying solely on anatomic staging often fails to provide precise, individualized survival predictions.\u003c/p\u003e \u003cp\u003eNomograms have emerged as intuitive and effective statistical models that integrate multiple prognostic variables to quantify individualized risk[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In recent years, several nomograms have been developed for osteosarcoma[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], predictive models specifically tailored to primary osteosarcoma of long bones remain relatively scarce. Furthermore, although existing models provide valuable prognostic insights, there is an opportunity to further enhance their generalizability. Many current models rely primarily on internal validation or random split-sample validation within the same time frame. While these approaches are undeniably effective, the addition of temporal external validation is increasingly recognized as crucial. Without it, models may naturally encounter subtle calibration drift when applied to modern patient cohorts, a phenomenon often presenting as \"regression towards the mean\" due to temporal shifts in baseline survival and the inherent challenges of statistical overfitting[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTherefore, this study aimed to develop and validate a prognostic nomogram for predicting survival outcomes in patients with primary osteosarcoma of long bones, utilizing a large population-based cohort from the Surveillance, Epidemiology, and End Results (SEER) database, which provide cancer data to the public for clinical studies[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. To ensure methodological rigor, we compared a machine learning approach (LASSO penalized regression) with traditional Cox stepwise regression for optimal feature selection. More importantly, we employed an independent temporal cohort (2016\u0026ndash;2017) for external validation and applied a statistical recalibration framework to explicitly correct calibration drift. By doing so, we sought to provide clinicians with a highly accurate, contemporary, and pragmatic tool to assist in personalized treatment decision-making and patient counseling.\u003c/p\u003e"},{"header":"2. Methods and materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Population\u003c/h2\u003e \u003cp\u003eData for this retrospective study were extracted from the Surveillance, Epidemiology, and End Results (SEER) database using SEER*Stat software (version 9.0.42). We utilized the \"Incidence - SEER Research Data, 17 Registries, Nov 2022 Sub (2000\u0026ndash;2020)\" dataset, which covers approximately 48.0% of the U.S. population. Patient selection followed predefined inclusion criteria to ensure consistency, reproducibility, and methodological rigor.\u003c/p\u003e \u003cp\u003eEligible patients met all of the following criteria: (1) Patients diagnosed with osteosarcoma were identified according to the International Classification of Diseases for Oncology, Third Edition morphology codes, restricted to malignant behavior (/3). The following histological subtypes were included:9180\u0026ndash;9187/3, 9192\u0026ndash;9194/3; (2) Patients with primary tumors located in long bones were included, defined using SEER primary site codes:C40.0 (long bones of upper limb, scapula, and associated joints) and C40.2 (long bones of lower limb and associated joints); (3) Cases were limited to those with microscopic confirmation, ensuring pathological verification of diagnosis and minimizing misclassification bias; (4) Patients diagnosed between 2010 and 2017 were included; (5) Only patients with one primary tumor.\u003c/p\u003e \u003cp\u003ePatients were excluded if they met any of the following conditions: (1) Patients with a recorded survival time of less than 1 month or coded as 0 months were excluded to reduce bias from peri-diagnostic mortality. (2) Patients with missing essential clinicopathological information were excluded, including race, tumor grade, stage, or surgical treatment status. A detailed flowchart of the patient selection process is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data Collection\u003c/h2\u003e \u003cp\u003eThe following variables were identified from the dataset: age at diagnosis (0\u0026ndash;9, 10\u0026ndash;24, or \u0026ge;\u0026thinsp;24 years), sex (male or female), race (white, black, or others), marital status (married or unmarried), laterality (left, right, or others), tumor grade (I/II, III, or IV), T stage, N stage, M stage, surgery (limb salvage surgery, amputation, local excision, or no surgery), radiation therapy (yes or no/unknown), and chemotherapy (yes or no). To perform a rigorous temporal external validation, the eligible patients were divided into two independent cohorts based on the year of diagnosis: patients diagnosed between 2010 and 2015 were allocated to the training cohort (n\u0026thinsp;=\u0026thinsp;631), while those diagnosed between 2016 and 2017 were assigned to the validation cohort (n\u0026thinsp;=\u0026thinsp;210).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Statistical Analysis and Feature Selection\u003c/h2\u003e \u003cp\u003eTo ensure the robustness of the prognostic feature selection, mitigate multicollinearity, and avoid overfitting, two parallel variable selection strategies were employed and compared in the training cohort:\u003c/p\u003e \u003cp\u003eMachine Learning Approach: A least absolute shrinkage and selection operator (LASSO) Cox regression was performed using the \u0026lsquo;glmnet\u0026rsquo; R package. The optimal penalty parameter (λ) was determined via 10-fold cross-validation to select the most predictive feature set (lambda.min).\u003c/p\u003e \u003cp\u003eTraditional Stepwise Approach: Univariate Cox proportional hazards regression was first used to screen potential prognostic factors (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Significant variables were then incorporated into a multivariate Cox regression model. A backward stepwise elimination based on the likelihood ratio test (drop1 function) was utilized to retain only the independent core prognostic factors (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eFollowing Occam's razor principle, the final model was selected by comparing the Harrell's Concordance index (C-index) and Area Under the Curve (AUC) of both strategies, favoring the more parsimonious model without significantly compromising predictive accuracy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Model Validation, Recalibration, and Clinical Utility\u003c/h2\u003e \u003cp\u003eModel discrimination and calibration were evaluated using the rms and timeROC packages. Internal validation was performed using bootstrap resampling (1,000 iterations). Temporal external validation was conducted to assess the model's generalizability. To identify potential calibration drift or regression to the mean, the calibration slope was calculated in the validation cohort. If the slope deviated from 1.0, a statistical recalibration was performed by using the original linear predictor as a single covariate to fit a new Cox model in the validation cohort, thereby generating a recalibrated curve. Finally, Decision Curve Analysis (DCA) was performed to quantify the net clinical benefit across a range of threshold probabilities.\u003c/p\u003e \u003cp\u003eAll statistical analyses were performed using R software (version 4.2.1). A two-sided P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Baseline Characteristics of the Study Population\u003c/h2\u003e \u003cp\u003eAmong 841 patients, 210 (25.0%) were assigned to the testing cohort and 631 (75.0%) to the training cohort. The mean (SD) survival was 37.7 (15.1) months in the testing cohort and 66.3 (37.3) months in the training cohort (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The distribution of age categories was similar between cohorts (p\u0026thinsp;=\u0026thinsp;0.96). The testing cohort had a higher proportion of male patients (62.4% vs 55.0%; p\u0026thinsp;=\u0026thinsp;0.06) and a different racial distribution (p\u0026thinsp;=\u0026thinsp;0.005), with a lower proportion of Black patients (9.0% vs 15.7%) and a higher proportion classified as \u0026ldquo;Other\u0026rdquo; (17.1% vs 10.6%) compared with the training cohort. The distribution of T stage differed between cohorts (p\u0026thinsp;=\u0026thinsp;0.02), with a higher proportion of T3 tumors in the testing cohort (7.6% vs 3.2%). No significant between-cohort differences were observed for primary tumor site, tumor grade, N stage, M stage, type of surgery, use of radiotherapy, use of chemotherapy, marital status, tumor laterality, cancer-specific survival status, or overall survival status (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\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\u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;841)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTesting (n\u0026thinsp;=\u0026thinsp;210)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTraining (n\u0026thinsp;=\u0026thinsp;631)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStatistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\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\u003eSurvival months\u003c/b\u003e, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59.18\u0026thinsp;\u0026plusmn;\u0026thinsp;35.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.71\u0026thinsp;\u0026plusmn;\u0026thinsp;15.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66.32\u0026thinsp;\u0026plusmn;\u0026thinsp;37.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et=-15.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e, n(%)\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.963\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e223 (26.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55 (26.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e168 (26.62)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026thinsp;~\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88 (10.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (10.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65 (10.30)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u0026thinsp;~\u0026thinsp;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e530 (63.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e132 (62.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e398 (63.07)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e, n(%)\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=3.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.061\u003c/p\u003e \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\u003e363 (43.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79 (37.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e284 (45.01)\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 \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\u003e478 (56.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e131 (62.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e347 (54.99)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace\u003c/b\u003e, n(%)\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=10.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \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\u003e118 (14.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (9.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99 (15.69)\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 \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\u003e103 (12.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (17.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67 (10.62)\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 \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\u003e620 (73.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e155 (73.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e465 (73.69)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrimary Site\u003c/b\u003e, n(%)\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.820\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLower limb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e709 (84.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e176 (83.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e533 (84.47)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpper limb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e132 (15.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (16.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98 (15.53)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGrade\u003c/b\u003e, n(%)\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.997\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\u003e95 (11.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (11.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71 (11.25)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅢ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e265 (31.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66 (31.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e199 (31.54)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅣ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e481 (57.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120 (57.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e361 (57.21)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eT Stage\u003c/b\u003e, n(%)\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=8.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e269 (31.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60 (28.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e209 (33.12)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e536 (63.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e134 (63.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e402 (63.71)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36 (4.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (7.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (3.17)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eN Stage\u003c/b\u003e, n(%)\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.190\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e821 (97.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e202 (96.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e619 (98.10)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (2.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (3.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (1.90)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eM Stage\u003c/b\u003e, n(%)\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.215\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e685 (81.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e165 (78.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e520 (82.41)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e156 (18.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45 (21.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e111 (17.59)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSurgery\u003c/b\u003e, n(%)\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.934\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmputation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e168 (19.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (20.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e126 (19.97)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLimb Salvage Surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e552 (65.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e140 (66.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e412 (65.29)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocal Excision\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69 (8.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (7.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54 (8.56)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52 (6.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (6.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 (6.18)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRadiotherapy\u003c/b\u003e, n(%)\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.815\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo/Unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e815 (96.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e203 (96.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e612 (96.99)\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 \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\u003e26 (3.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (3.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (3.01)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChemotherapy\u003c/b\u003e, n(%)\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=2.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.135\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\u003e96 (11.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (8.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78 (12.36)\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 \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\u003e745 (88.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e192 (91.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e553 (87.64)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital Status\u003c/b\u003e, n(%)\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.325\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e113 (13.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (11.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89 (14.10)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnmarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e728 (86.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e186 (88.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e542 (85.90)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaterality\u003c/b\u003e, n(%)\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.458\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e458 (54.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119 (56.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e339 (53.72)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e383 (45.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91 (43.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e292 (46.28)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCSS\u003c/b\u003e, n(%)\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.226\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e593 (70.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e155 (73.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e438 (69.41)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248 (29.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55 (26.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e193 (30.59)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOS\u003c/b\u003e, n(%)\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=2.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e544 (64.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e145 (69.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e399 (63.23)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e297 (35.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65 (30.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e232 (36.77)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003et: t-test, χ\u0026sup2;: Chi-square test\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eSD: standard deviation\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Variable Selection and Model Comparison\u003c/h2\u003e \u003cp\u003eTo ensure the robustness of feature selection, we evaluated both Cox regression and LASSO penalized regression in parallel. Initially, the univariate Cox regression analysis revealed that nine variables\u0026mdash;age, sex, race, tumor grade, T stage, N stage, M stage, surgery, and marital status\u0026mdash;were significantly associated with cancer-specific survival (CSS) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Subsequently, these variables were incorporated into a multivariate Cox regression model. As detailed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, 7 variables were ultimately identified as independent prognostic factors for CSS: age, sex, grade, T stage, N stage, M stage, and Surgery. The traditional prognostic model built on these 7 core variables achieved a C-index of 0.751.\u003c/p\u003e \u003cp\u003eSimultaneously, we applied LASSO regression for high-dimensional feature reduction (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). At the lambda.min node, the LASSO model retained a more complex combination of features (including race and marital status), yielding a C-index of 0.753.\u003c/p\u003e \u003cp\u003eModel comparison analysis demonstrated that although the LASSO model incorporated more candidate variables, its predictive discrimination (C-index) improved by merely 0.002 compared to the 7-variable model derived from the traditional screening. Furthermore, the time-dependent ROC curves of both models were highly overlapping (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Based on Occam's razor principle and clinical utility considerations, and without significantly compromising predictive accuracy, this study ultimately discarded redundant variables. We selected the 7-variable model to construct the final nomogram, aiming to provide a more convenient and efficient tool for clinical decision-making.\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\u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eUnivariable Cox\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c12\" namest=\"c8\"\u003e \u003cp\u003eMultivariable Cox\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS.E\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ\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 \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eS.E\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eZ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;25\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.000 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.000 (Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026thinsp;~\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.020\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.499 (0.278\u0026thinsp;~\u0026thinsp;0.896)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-1.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-2.944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.367 (0.188\u0026thinsp;~\u0026thinsp;0.715)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u0026thinsp;~\u0026thinsp;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.739 (0.540\u0026thinsp;~\u0026thinsp;1.013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-1.745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.673 (0.432\u0026thinsp;~\u0026thinsp;1.050)\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.000 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.000 (Reference)\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\u003e0.441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.554 (1.158\u0026thinsp;~\u0026thinsp;2.084)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0.027\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.416 (1.039\u0026thinsp;~\u0026thinsp;1.928)\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.000 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\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\u003e-0.368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.692 (0.387\u0026thinsp;~\u0026thinsp;1.239)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\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\u003e-0.170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.890\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.844 (0.580\u0026thinsp;~\u0026thinsp;1.227)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrimary Site\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLower limb\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.000 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpper limb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.929 (0.618\u0026thinsp;~\u0026thinsp;1.395)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.000 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.000 (Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅢ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.176 (2.616\u0026thinsp;~\u0026thinsp;19.683)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e8.401 (2.766\u0026thinsp;~\u0026thinsp;25.511)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅣ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.059 (2.607\u0026thinsp;~\u0026thinsp;19.111)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e6.773 (2.240\u0026thinsp;~\u0026thinsp;20.476)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eT Stage\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.000 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.000 (Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.010\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.548 (1.111\u0026thinsp;~\u0026thinsp;2.157)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.300 (0.927\u0026thinsp;~\u0026thinsp;1.824)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.644 (3.198\u0026thinsp;~\u0026thinsp;9.959)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3.428 (1.868\u0026thinsp;~\u0026thinsp;6.290)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eN Stage\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN0\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.000 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.000 (Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.775 (3.047\u0026thinsp;~\u0026thinsp;10.944)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0.026\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.283 (1.103\u0026thinsp;~\u0026thinsp;4.725)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eM Stage\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM0\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.000 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.000 (Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.934 (3.673\u0026thinsp;~\u0026thinsp;6.629)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8.675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e4.202 (3.038\u0026thinsp;~\u0026thinsp;5.812)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSurgery\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmputation\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.000 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.000 (Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLimb Salvage Surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.509 (0.364\u0026thinsp;~\u0026thinsp;0.712)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-2.886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.602 (0.426\u0026thinsp;~\u0026thinsp;0.849)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocal Excision\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.787 (0.465\u0026thinsp;~\u0026thinsp;1.331)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.931 (0.544\u0026thinsp;~\u0026thinsp;1.593)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.019 (1.829\u0026thinsp;~\u0026thinsp;4.981)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.890\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.436 (1.322\u0026thinsp;~\u0026thinsp;4.490)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRadiotherapy\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo/Unknown\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.000 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.000 (Reference)\u003c/p\u003e \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\u003e1.382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.984 (2.265\u0026thinsp;~\u0026thinsp;7.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.897 (0.452\u0026thinsp;~\u0026thinsp;1.781)\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.000 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.000 (Reference)\u003c/p\u003e \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\u003e0.557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.045\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.745 (1.013\u0026thinsp;~\u0026thinsp;3.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-1.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.710 (0.364\u0026thinsp;~\u0026thinsp;1.384)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital Status\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.000 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.000 (Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnmarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.575 (0.402\u0026thinsp;~\u0026thinsp;0.823)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-1.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.773 (0.471\u0026thinsp;~\u0026thinsp;1.268)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaterality\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.000 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.073 (0.809\u0026thinsp;~\u0026thinsp;1.424)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003eHR: Hazard Ratio, CI: Confidence Interval\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Construction and Internal Validation of the Nomogram\u003c/h2\u003e \u003cp\u003eBased on the seven independent prognostic variables identified (age, sex, tumor grade, T stage, N stage, M stage, and surgery), a prognostic nomogram was constructed to provide individualized predictions of 3-year and 5-year cancer-specific survival (CSS) for patients with primary osteosarcoma of the long bones (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In the internal validation, the model demonstrated robust predictive performance with a C-index of 0.751 (SE\u0026thinsp;=\u0026thinsp;0.016). The 5-year time-dependent ROC curve for the training cohort further confirmed the excellent discriminatory power of the model. Additionally, the 3- and 5-year internal calibration curves generated via bootstrap resampling (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) revealed a high degree of concordance between the nomogram-predicted survival probabilities and the actual Kaplan-Meier observations, indicating the great accuracy of the model within the development cohort.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Temporal External Validation and Model Recalibration\u003c/h2\u003e \u003cp\u003eIn the temporal external validation cohort, the nomogram maintained robust discrimination, achieving an external C-index of 0.706. The time-dependent ROC curves (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA) demonstrated that the model preserved its great predictive efficacy in this recent independent cohort. When evaluating the calibration performance in the external validation set (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB), we observed a calibration slope of 0.724. This slope of less than 1.0 indicates a typical degree of statistical overfitting within the training cohort, which translates to slightly pessimistic survival predictions for high-risk patients and overly optimistic predictions for low-risk patients (regression to the mean). To optimize the absolute predictive accuracy for contemporary clinical cohorts, a statistical recalibration was performed. After adjusting the baseline risk and applying a shrinkage factor, the recalibrated curve (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC) aligned perfectly with the 45-degree ideal line, successfully correcting the calibration drift introduced by model translation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003ePrognostic assessment is of paramount importance for the treatment, monitoring, and follow-up of cancer patients. In this study, we developed and rigorously validated a prognostic nomogram incorporating seven clinicopathological features to predict the CSS of patients with primary osteosarcoma of the long bones, utilizing a large-scale cohort from the SEER database.\u003c/p\u003e \u003cp\u003eOur multivariate analysis results corroborate previous findings. Age is a well-established prognostic factor for numerous malignancies[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Similarly, our research demonstrated a significant survival advantage in younger patients, particularly those aged 0\u0026ndash;9 years. Tumor stage and metastatic status (M stage) remain the most critical risk factors for osteosarcoma[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Prior studies have reported significantly poorer survival outcomes in patients presenting with distant metastasis, which is highly consistent with the HR of 4.2 for M1 stage observed in our model. Regarding treatment, the surgical modality exerts a profound and immediate impact on the survival of osteosarcoma patients. Our data provide robust evidence supporting the prognostic superiority of limb salvage surgery (LSS), which demonstrated a significantly lower mortality risk compared to both amputation and the absence of surgical intervention. While this favorable outcome may be partially attributed to selection bias\u0026mdash;as patients with smaller tumor volumes and positive responses to neoadjuvant chemotherapy are more likely to be candidates for LSS\u0026mdash;the holistic benefits of the procedure cannot be overlooked. Furthermore, on the premise of ensuring long-term survival, LSS preserves physical function and enhances quality of life while mitigating the psychological and social barriers associated with amputation. Such holistic benefits significantly facilitate patient reintegration into society and overall recovery, ultimately translating into improved prognostic outcomes. Although traditional perspectives favored amputation for its perceived radicality in tumor clearance, advancements in medical technology and disease understanding have demonstrated that the survival rates of LSS are now comparable to those of amputation[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Notably, the direct correlation between surgical type and survival remains a subject of academic debate, with several studies indicating no significant difference in survival outcomes between the two modalities. Beyond absolute survival, functional recovery is a critical metric of surgical success. A prospective study evaluating functional outcomes via TESS and MSTS scores found that despite a high complication rate in LSS patients, there were no significant functional differences between LSS and amputation groups during a 2-to-7-year follow-up period[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This suggests that the selection of surgical modality should be a multifaceted decision incorporating tumor size, location, and chemotherapy response, rather than relying solely on the surgical type itself.\u003c/p\u003e \u003cp\u003eThe primary strength and novelty of this study lie in the implementation of temporal external validation and a detailed exploration of model calibration drift. In clinical prediction models, \"regression to the mean\" and overfitting are common phenomena encountered during external validation. Our validation cohort yielded a calibration slope of 0.724, which objectively reflects the improvement in baseline survival between the 2010\u0026ndash;2015 and 2016\u0026ndash;2017 periods, as well as slight polarization of model weights. Departing from previous studies that often overlook this issue, we innovatively introduced a recalibration framework. Through a straightforward mathematical shrinkage adjustment, the nomogram\u0026rsquo;s predicted probabilities were perfectly aligned with the actual outcomes of the modern clinical cohort. This process not only validates that the seven selected variables are core drivers of osteosarcoma prognosis but also highlights the strong adaptive potential of the model for future clinical implementation.\u003c/p\u003e \u003cp\u003eThis study also has certain limitations. First, its retrospective design inherently leads to potential missing data. Second, the SEER database lacks granular information regarding specific chemotherapy regimens, targeted therapy usage, tumor necrosis rates (histological response), and genetic mutations (e.g., TP53), all of which may significantly influence prognosis. Future prospective, multicenter studies are required to further validate and refine this model.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, age, sex, pathological grade, T-stage, N-stage, M-stage, and surgical approach serve as independent prognostic factors for patients with primary osteosarcoma of the long bones. The nomogram developed in this study demonstrates great discriminative power and clinical utility. Following rigorous external temporal validation and recalibration, this model accurately predicts survival probabilities for contemporary osteosarcoma patients, providing clinicians with an invaluable tool for formulating individualized treatment regimens and follow-up plans.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eACKNOWLEDGMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest:\u003c/strong\u003e Authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e Authors received no specific funding for this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u003c/strong\u003e The data that support the findings of this study are publicly available from the Surveillance, Epidemiology, and End Results (SEER) database (https://seer.cancer.gov/). Researchers can obtain full access to the SEER data by submitting a data access request and signing a data-use agreement. The derived data supporting the findings of this study are available from the corresponding author upon reasonable request and can be provided for peer review if required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical statement:\u003c/strong\u003e This study utilized publicly available, de-identified data from the Surveillance, Epidemiology, and End Results (SEER) database. As the data are fully anonymized and do not contain any personally identifiable information, the requirement for ethical approval and informed consent from an institutional review board or ethics committee was waived, in accordance with local legislation and institutional requirements. This study was conducted in accordance with the principles of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eShilong Wang:\u003c/strong\u003e Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, and Writing \u0026ndash; original draft.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLong Jia:\u003c/strong\u003e Conceptualization, Project administration, Resources, Supervision, Validation, and Writing \u0026ndash; review \u0026amp; editing\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBeird HC, Bielack SS, Flanagan AM, Gill J, Heymann D, Janeway KA, Livingston JA, Roberts RD, Strauss SJ, Gorlick R: \u003cstrong\u003eOsteosarcoma.\u003c/strong\u003e \u003cem\u003eNature reviews. Disease primers\u003c/em\u003e 2022, \u003cstrong\u003e8:\u003c/strong\u003e77.\u003c/li\u003e\n\u003cli\u003eWang X, Zhu K, Hu J, Zhang C: \u003cstrong\u003eAdvances and challenges in the treatment of osteosarcoma.\u003c/strong\u003e \u003cem\u003eProgress in biophysics and molecular biology\u003c/em\u003e 2025, \u003cstrong\u003e197:\u003c/strong\u003e60-74.\u003c/li\u003e\n\u003cli\u003eAnderson ME: \u003cstrong\u003eUpdate on Survival in Osteosarcoma.\u003c/strong\u003e \u003cem\u003eThe Orthopedic clinics of North America\u003c/em\u003e 2016, \u003cstrong\u003e47:\u003c/strong\u003e283-292.\u003c/li\u003e\n\u003cli\u003eRossi F, Rydzyk MM, Barba L, Malucelli E, Palam\u0026agrave; MEF, Gentili C, Mastrogiacomo M, Cedola A, Mancini L, Salom\u0026eacute; M, et al: \u003cstrong\u003eInsights into the osteosarcoma microenvironment: Multiscale analysis of structural and mineral heterogeneity.\u003c/strong\u003e \u003cem\u003eActa Biomater\u003c/em\u003e 2025, \u003cstrong\u003e199:\u003c/strong\u003e193-201.\u003c/li\u003e\n\u003cli\u003eCates JMM: \u003cstrong\u003eComparison of the AJCC, MSTS, and Modified Spanier Systems for Clinical and Pathologic Staging of Osteosarcoma.\u003c/strong\u003e \u003cem\u003eThe American journal of surgical pathology\u003c/em\u003e 2017, \u003cstrong\u003e41:\u003c/strong\u003e405-413.\u003c/li\u003e\n\u003cli\u003eIasonos A, Schrag D, Raj GV, Panageas KS: \u003cstrong\u003eHow to build and interpret a nomogram for cancer prognosis.\u003c/strong\u003e \u003cem\u003eJournal of clinical oncology : official journal of the American Society of Clinical Oncology\u003c/em\u003e 2008, \u003cstrong\u003e26:\u003c/strong\u003e1364-1370.\u003c/li\u003e\n\u003cli\u003eHe Y, Liu H, Wang S, Zhang J: \u003cstrong\u003eA nomogram for predicting cancer-specific survival in patients with osteosarcoma as secondary malignancy.\u003c/strong\u003e \u003cem\u003eSci Rep\u003c/em\u003e 2020, \u003cstrong\u003e10:\u003c/strong\u003e12817.\u003c/li\u003e\n\u003cli\u003eLu S, Wang Y, Liu G, Wang L, Wu P, Li Y, Cheng C: \u003cstrong\u003eConstruction and validation of nomogram to predict distant metastasis in osteosarcoma: a retrospective study.\u003c/strong\u003e \u003cem\u003eJ Orthop Surg Res\u003c/em\u003e 2021, \u003cstrong\u003e16:\u003c/strong\u003e231.\u003c/li\u003e\n\u003cli\u003eRoustit M, Jullien A, Jambon-Barbara C, Goudon H, Blaise S, Cracowski J, Khouri C: \u003cstrong\u003ePlacebo response in Raynaud\u0026apos;s Phenomenon clinical trials: The prominent role of regression towards the mean: Placebo response in Raynaud\u0026apos;s Phenomenon.\u003c/strong\u003e \u003cem\u003eSemin Arthritis Rheum\u003c/em\u003e 2022, \u003cstrong\u003e57:\u003c/strong\u003e152087.\u003c/li\u003e\n\u003cli\u003ePark HS, Lloyd S, Decker RH, Wilson LD, Yu JB: \u003cstrong\u003eOverview of the Surveillance, Epidemiology, and End Results database: evolution, data variables, and quality assurance.\u003c/strong\u003e \u003cem\u003eCurr Probl Cancer\u003c/em\u003e 2012, \u003cstrong\u003e36:\u003c/strong\u003e183-190.\u003c/li\u003e\n\u003cli\u003eD\u0026iacute;az Del Arco C, Ortega Medina L, Estrada Mu\u0026ntilde;oz L, Molina Rold\u0026aacute;n E, Garc\u0026iacute;a G\u0026oacute;mez de Las Heras S, Fern\u0026aacute;ndez Ace\u0026ntilde;ero MJ: \u003cstrong\u003eImpact of Age at Diagnosis on Clinicopathological Features, Prognosis, and Management of Gastric Cancer: A Retrospective Single-Center Experience from Spain.\u003c/strong\u003e \u003cem\u003eCancers (Basel)\u003c/em\u003e 2023, \u003cstrong\u003e15\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eBouferraa Y, Haibe Y, Chedid A, Jabra E, Charafeddine M, Temraz S, Mukherji D, El Saghir N, Shamseddine A: \u003cstrong\u003eThe impact of young age (\u0026lt;\u0026thinsp;40\u0026thinsp;years) on the outcome of a cohort of patients with primary non-metastatic breast cancer: analysis of 10-year survival of a prospective study.\u003c/strong\u003e \u003cem\u003eBMC Cancer\u003c/em\u003e 2022, \u003cstrong\u003e22:\u003c/strong\u003e27.\u003c/li\u003e\n\u003cli\u003eLu S, Wang Y, Liu G, Wang L, Wu P, Li Y, Cheng C: \u003cstrong\u003eConstruction and validation of nomogram to predict distant metastasis in osteosarcoma: a retrospective study.\u003c/strong\u003e \u003cem\u003eJ Orthop Surg Res\u003c/em\u003e 2021, \u003cstrong\u003e16:\u003c/strong\u003e231.\u003c/li\u003e\n\u003cli\u003eBielack SS, Kempf-Bielack B, Delling G, Exner GU, Flege S, Helmke K, Kotz R, Salzer-Kuntschik M, Werner M, Winkelmann W, et al: \u003cstrong\u003ePrognostic factors in high-grade osteosarcoma of the extremities or trunk: an analysis of 1,702 patients treated on neoadjuvant cooperative osteosarcoma study group protocols.\u003c/strong\u003e \u003cem\u003eJournal of clinical oncology : official journal of the American Society of Clinical Oncology\u003c/em\u003e 2002, \u003cstrong\u003e20:\u003c/strong\u003e776-790.\u003c/li\u003e\n\u003cli\u003eJiang Y, Wang T, Wei Z: \u003cstrong\u003eConstruction and Validation of Nomograms for Predicting the Prognosis of Juvenile Osteosarcoma: A Real-World Analysis in the SEER Database.\u003c/strong\u003e \u003cem\u003eTechnol Cancer Res Treat\u003c/em\u003e 2020, \u003cstrong\u003e19:\u003c/strong\u003e1533033820947718.\u003c/li\u003e\n\u003cli\u003evan Egmond-van Dam JC, Bekkering WP, Bramer JAM, Beishuizen A, Fiocco M, Dijkstra PDS: \u003cstrong\u003eFunctional outcome after surgery in patients with bone sarcoma around the knee; results from a long-term prospective study.\u003c/strong\u003e\u003cem\u003eJ Surg Oncol\u003c/em\u003e 2017, \u003cstrong\u003e115:\u003c/strong\u003e1028-1032.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Osteosarcoma, Long bones, Nomogram, Temporal validation, Recalibration, SEER","lastPublishedDoi":"10.21203/rs.3.rs-9410266/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9410266/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eExisting prognostic models for primary osteosarcoma of long bones often lack temporal external validation, making them susceptible to calibration drift. This study aimed to develop, temporally validate, and recalibrate a nomogram to predict cancer-specific survival (CSS) for these patients utilizing the Surveillance, Epidemiology, and End Results (SEER) database.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003ePatients diagnosed between 2010 and 2017 were identified and divided into a training cohort (2010\u0026ndash;2015, n\u0026thinsp;=\u0026thinsp;631) and a temporal external validation cohort (2016\u0026ndash;2017, n\u0026thinsp;=\u0026thinsp;210). Feature selection was performed by comparing LASSO penalized regression and traditional stepwise Cox regression. Model performance was evaluated using the concordance index (C-index), time-dependent receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA). Statistical recalibration was applied to address calibration drift in the validation cohort.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eSeven independent prognostic factors\u0026mdash;age, sex, tumor grade, T stage, N stage, M stage, and surgery\u0026mdash;were identified. Based on Occam's razor principle and clinical utility, the traditional 7-variable Cox model (C-index\u0026thinsp;=\u0026thinsp;0.751) was selected over the LASSO model (C-index\u0026thinsp;=\u0026thinsp;0.753) to construct the final nomogram. In the temporal external validation cohort, the nomogram maintained robust discrimination with an external C-index of 0.706. The initial calibration slope of 0.724 indicated statistical overfitting. However, after adjusting the baseline risk and applying a shrinkage factor, the recalibrated curve aligned perfectly with actual modern clinical outcomes.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe developed 7-variable nomogram serves as an accurate and reliable tool for predicting CSS in patients with primary osteosarcoma of the long bones. Rigorous temporal external validation and recalibration ensure its adaptability and utility in formulating individualized treatment regimens for contemporary patients.\u003c/p\u003e","manuscriptTitle":"Development and Validation of a Nomogram for Predicting Cancer-specific Survival in Patients with Primary Osteosarcoma of Long Bones: A SEER- Based Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-11 10:07:56","doi":"10.21203/rs.3.rs-9410266/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"126106212458895053778036253532189594979","date":"2026-05-14T08:51:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-14T08:42:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"54767081731399158730878726911004096258","date":"2026-05-14T07:02:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-07T13:49:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"166217410699181997452778333156000015687","date":"2026-05-05T05:58:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"269767692988669397547395538164144788281","date":"2026-05-01T02:08:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"203439654974325708287861557862502879167","date":"2026-04-30T14:32:45+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-30T13:29:36+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-30T11:49:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-17T02:39:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-17T02:39:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Oncology","date":"2026-04-14T04:00:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d94a9fea-5454-40a6-97b5-45829f91163e","owner":[],"postedDate":"May 11th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"126106212458895053778036253532189594979","date":"2026-05-14T08:51:08+00:00","index":112,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-14T08:42:35+00:00","index":111,"fulltext":""},{"type":"reviewerAgreed","content":"54767081731399158730878726911004096258","date":"2026-05-14T07:02:33+00:00","index":110,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-07T13:49:58+00:00","index":62,"fulltext":""},{"type":"reviewerAgreed","content":"166217410699181997452778333156000015687","date":"2026-05-05T05:58:09+00:00","index":61,"fulltext":""},{"type":"reviewerAgreed","content":"269767692988669397547395538164144788281","date":"2026-05-01T02:08:06+00:00","index":58,"fulltext":""},{"type":"reviewerAgreed","content":"203439654974325708287861557862502879167","date":"2026-04-30T14:32:45+00:00","index":56,"fulltext":""},{"type":"reviewersInvited","content":"86","date":"2026-04-30T13:29:36+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-30T11:49:29+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T10:07:56+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-11 10:07:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9410266","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9410266","identity":"rs-9410266","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}