Prognostic Risk Factors for Cancer-Specific Bone Metastasis: A Registry-Based Analysis of 13,742 Patients 

Prognostic Risk Factors for Cancer-Specific Bone Metastasis: A Registry-Based Analysis of 13,742 Patients | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Prognostic Risk Factors for Cancer-Specific Bone Metastasis: A Registry-Based Analysis of 13,742 Patients Zelin Yun, Yanchao Tang, Jie Sun, Juncai Lei, Gangqiang Zhang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8384769/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract Background Accurate survival prediction for patients with bone metastatic cancer remains challenging. Existing prognostic models frequently show poor external validity, primarily due to small sample sizes, single-center designs, and insufficient inclusion of pathological and molecular variables. Moreover, few studies have concentrated on the prognostic heterogeneity of bone metastasis (BM) across different cancers using large, standardized datasets within a cancer-specific manner. This retrospective, multicenter, registry-based cohort study was conducted to evaluate the prognostic significance of BM across multiple cancer types and to identify cancer-specific clinical factors associated with survival. Methods Baseline demographic and clinical characteristics of 13,742 patients with AJCC stage IV or TNM stage M1 metastatic cancer diagnosis were collected across 42 clinical studies registered in the cBioPortal for Cancer Genomics database. Overall survival (OS) following metastatic diagnosis was set as the primary outcome. Univariate analyses were conducted to identify potential prognostic risk factors mainly using the Kaplan–Meier, log-rank test, and non-parametric tests. Variables with p < 0.20 were included in multivariate Cox proportional hazards models for further validation. Multiple imputation and bootstrap were applied for the missing value process and validation. Results BM was associated with favorable outcomes compared with other metastatic sites in osteotropic cancers such as breast, prostate, and thyroid cancer, whereas it indicated a worse prognosis in hepatobiliary, uterine sarcoma, and colorectal cancer with low affinity to skeletal tissue. Among prognostic variables, no single metastatic site served as a universal adverse prognostic factor across all cancers. Poorly differentiated or undifferentiated histology independently correlated with reduced survival (HR = 1.249, p < 0.001). Age above 60 years was also associated with inferior survival (univariate analysis, p < 0.001), while the primary cancer type remained the most influential prognostic determinant (HR = 1.422–1.758, p < 0.001). Conclusions BM demonstrates cancer-specific and heterogeneous influences on survival. Population for survival prediction in traditional studies could be expanded within a cancer-specific framework. Among the included prognostic variables, primary cancer type, pathological differentiation, and age stratify outcomes significantly, highlighting the demand for pathology-integrated, cancer-specific prognostic models. Incorporation of standardized treatment and molecular variables is essential for improving model precision and clinical applicability in the future. Health sciences/Biomarkers Biological sciences/Cancer Health sciences/Oncology Bone Metastasis Registry-Based Prognosis Cancer-Specific Pathological Differentiation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Bone metastasis (BM) represents a prevalent and debilitating complication in patients with advanced cancer, frequently leading to severe neurological pain, pathological fractures, spinal instability, motor weakness, sensory disturbance, and sphincter dysfunction, significantly compromising the quality of life [1-3]. Over the past decade, with rapid advancements in comprehensive oncological management, including stereotactic body radiation therapy, immunotherapy, and molecular targeted therapy, the life expectancy and quality of life of patients with advanced malignancies have been markedly improved [4-9]. Therefore, the indications for surgical intervention aimed at improving quality of life and preserving neurological function have expanded substantially compared with the past. According to the four-point consensus for preoperative assessment established by the World Federation of Neurosurgical Societies (WFNS), the evaluation of life expectancy (at least >3 months) is primarily crucial in the determination of surgical intervention [10]. In this context, the precise identification of prognostic risk factors for life expectancy and surgical guidance is paramount to optimize the clinical benefit of BM patients. However, previous prognostic models have generally demonstrated unsatisfying accuracy in external validation, which can be attributed to several factors: firstly, most studies were based on single-center or small-sample data. The limited patient numbers and the potentially unbalanced distribution of cancer types often led to systematic errors (e.g., overfitting bias), resulting in deficient model generalizability [11-18]. Secondly, internal heterogeneity among patients across different cancers makes it challenging for a single model to accurately apply to diverse populations [19-23]. Moreover, previous predictive models insufficiently integrated histological or pathological, genetic, or molecular indicators which are highly specific to cancer subtypes, leading to an insufficient assessment of the biological characteristics of the primary tumors [24-28]. Meanwhile, the constant improvement of survival (discussed above) renders the input data of previous models outdated, necessitating the updates to reflect contemporary clinical progress [29, 30]. These dilemmas raise the question of how to reasonably expand the sample size and discover cancer-specific variables that can significantly influence survival even at an advanced stage. Therefore, in this study, novel risk factors such as pathological subtype and metastatic sites were explored, particularly from a cancer-specific perspective, thereby providing more precise evidence for the life expectancy prediction and surgical decision-making process in BM patients. Moreover, the feasibility of extending the recruited population from BM patients to all patients with metastatic cancer was explored and evaluated. It was started from the assumption that there are no significant differences in the survival distribution of BM patients versus patients with other sites of metastasis (OSM). The eligible population for predictive model construction would substantially expand if the above assumption were validated or effectively refined. Methods Study design and setting This study strictly followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines and was conducted in accordance with the REporting of studies Conducted using Observational Routinely collected health Data (RECORD) statement [ 31 , 32 ]. Informed consent was waived owing to the retrospective design of the study and the anonymization of all patient information. All data used in this research were obtained from the accessible cBioPortal for Cancer Genomics database. The study was conducted with the support of a tertiary hospital in Beijing, China. Ethical approval was granted by the Research Ethics Committee of our institution (approval number: LM2025366). Patients Clinical data of patients diagnosed with metastatic cancer between 2015 and 2025 were extracted from the cBioPortal for Cancer Genomics database (publicly accessible) [ 33 ]. Recruited cases were diagnosed with tumor-node-metastasis (TNM) stage M1 or American Joint Committee on Cancer (AJCC) stage IV and had a clear survival outcome (n = 17,533, including 31 cancers) [ 34 , 35 ]. Studies were excluded if the number of patients with bone metastasis (BM) was fewer than 5 in a single study, the specific cancer type was undefined (e.g., cancer of unknown primary), or the study cohort consisted of patients with primary bone tumors (n = 3,791). BM patients were recognized when labels for the metastatic sites included “bone” or a specific skeletal site (n = 6,011). Ultimately, a total of 13,742 patients with metastatic cancer involving 25 types of cancer were included in the final (Fig. 1 ). Data Sources and Variables In total, 42 clinical studies were obtained from the cBioPortal for Cancer Genomics database involving 25 distinct cancer types. The clinical variables included study identity document (ID), patient ID, sample ID, metastatic sites (lung, liver, intra-abdominal region, pelvis including reproductive system, bladder, and rectum, central nervous system, adrenal gland, bone, spine, and vertebrae), primary cancer type, pathological subtype, overall survival status, overall survival time, age at diagnosis, current age, age at surgical procedure, sex, race, religion, body mass index (BMI), Gleason score, prior treatment regimen, comorbidities, TNM stage, and AJCC stage. Data Preprocessing Gene-related variables that were not associated with clinical information were removed. Clinical variables with the same meanings but different names across studies were merged: (1) variables differing only in letter case, (2) variables representing synonymous terms, and (3) age at diagnosis was missing but replaced by age at sampling in individual studies. Variables with a missing rate greater than 30% (e.g., BMI, religion, prior treatment regimen, etc.) were excluded [ 36 ]. Multiple imputation was applied to handle missing values in continuous variables, such as age at diagnosis [ 37 , 38 ]. Both structured clinical data (e.g., laboratory test results) and unstructured information (e.g., electronic medical records and imaging reports) were adopted to determine the metastatic sites. Primary Outcomes The primary outcome was the overall survival (OS) defined as the time from the diagnosis of metastatic cancer (TNM stage M1 or AJCC stage IV) to death. Statistical Analysis The normal distribution of continuous variables was assessed using the Kolmogorov–Smirnov (K–S) test [ 39 ]. The Mann–Whitney test and Kruskal–Wallis test were respectively applied to compare the difference of variable distribution between two or more groups in non-normally distributed data [ 39 ]. Variables not following a normal distribution were summarized as median with interquartile range (IQR). The coefficient of variation was used to reflect the degree of dispersion in survival time among different populations. A specific cancer was included in a univariate analysis if its subset population with the occurrence of the corresponding event and primary outcome was approximately equal to or more than 30 [ 40 ]. The Kaplan–Meier curve and log-rank (Mantel–Cox) test were used to evaluate the effect of a single risk factor on survival time [ 37 ]. Variables with a p < 0.2 in the univariate analysis were included in the multivariate model [ 41 , 42 ]. Multivariate analysis was performed using the Cox proportional hazards model [ 43 ]. The collinearity was assessed by the variance inflation factor (VIF) with an acceptable threshold of < 5. The proportional hazards (PH) assumption was verified using the Schoenfeld residuals test. Exhaustive search, tree models, and the maximally selected rank statistics (MAXSTAT) were applied to identify optimal cut-off values for age-related risk of survival time [ 44 ]. The model stability was evaluated using the bootstrap method, and 95% confidence intervals (CIs) were reported. Statistical significance was defined as p < 0.05. The clinical effect size of the significant difference was further assessed using Cliff’s δ and the probability of superiority (PS) both with 95% CIs. Statistical analyses were performed using IBM SPSS Statistics (version 31.0.0.0; IBM Corp., Armonk, NY, USA) and R (version 4.5.1; R Foundation for Statistical Computing, Vienna, Austria). Survival curves were generated using GraphPad Prism (version 10.0.0; GraphPad Software, Boston, MA, USA). Results Patient Baseline Characteristics A total of 13,742 patients were included in the final analysis involving 25 different cancer types. The population of BM patients was more than 100 in 11 cancers. The proportion of bone metastasis varied substantially across cancer types, ranging from 10.1% to 74.7%. Approximately half of the cancers showed a predominant distribution of pathological subtypes (Table 1 ). Table 1 Baseline patient demographics and clinical characteristics BM (n, %) Sex (male, %) Age at Diagnosis (years) Race (white, %) Main Cancer Type Detailed (%) Bladder Cancer (n = 463) 189 (40.8) 69.7 68.06 (60.39–74.28) 87.7 Bladder Urothelial Carcinoma (80.3) Breast Cancer (n = 1960) 942 (48.1) 1.0 54.68 (45.72–63.05) 71.4 Breast Invasive Ductal Carcinoma (64.5) Colorectal Cancer (n = 1573) 426 (27.1) 55.1 57.03 (48.12–66.50) 78.6 Colon Adenocarcinoma (67.2) Esophagogastric Cancer (n = 522) 155 (29.7) 71.1 61.33 (51.72–69.47) 79.0 Stomach Adenocarcinoma (39.7) Esophageal Adenocarcinoma (34.1) Head and Neck Cancer (n = 276) 142 (51.4) 79.9 60.56 (52.14-68.00) 78.1 Oral Cavity Squamous Cell Carcinoma (29.3) Hepatobiliary Cancer (n = 610) 135 (22.1) 53.9 63.71 (55.54–71.27) 79.7 Intrahepatic Cholangiocarcinoma (45.1) Melanoma (n = 334) 173 (51.7) 68.3 62.59 (50.55–71.90) 90.1 Cutaneous Melanoma (56.3) Non-Small Cell Lung Cancer (n = 2835) 1677 (59.2) 45.9 67.33 (59.00-74.32) 68.5 Lung Adenocarcinoma (81.5) Pancreatic Cancer (n = 2060) 827 (40.1) 53.5 66.00 (58.00–73.00) 80.5 Pancreatic Adenocarcinoma (92.3) Prostate Cancer (n = 860) 642 (74.7) 100 67.80 (61.06-75.00) 70.2 Prostate Adenocarcinoma (98.4) Small Cell Lung Cancer (n = 224) 110 (49.1) 58.8 66.34 (59.70-72.28) 82.6 Small Cell Lung Cancer (91.1) Anal Cancer (n = 40) 12 (30.0) 50.0 60.82 (55.55–69.15) 75.0 Anal Squamous Cell Carcinoma (100.0) Appendiceal Cancer (n = 69) 7 (10.1) 50.7 62.02 (47.85–68.69) 73.9 Appendiceal Adenocarcinoma (56.5) Cervical Cancer (n = 43) 12 (27.9) 0 51.48 (41.48–56.91) 60.5 Cervical Squamous Cell Carcinoma (90.7) Endometrial Cancer (n = 367) 86 (23.4) 0 65.97 (61.24–71.41) 67.6 Uterine Papillary Serous Carcinoma (37.60) Gastrointestinal Neuroendocrine Tumor (n = 80) 19 (23.8) 66.2 60.15 (51.41–70.22) 71.2 Gastrointestinal Stromal Tumor (65.0) Germ Cell Tumor (n = 51) 20 (39.2) 98 32.42 (25.50–43.88) 72.5 Mixed Germ Cell Tumor (47.1) Ovarian Cancer (n = 431) 87 (20.2) 0 61.12 (52.46–68.73) 81.5 Serous Ovarian Cancer (83.1) Renal Cell Cancer (n = 164) 92 (56.1) 73.2 59.63 (53.58–65.88) 83.7 Renal Clear Cell Carcinoma (90.3) Salivary Gland Cancer (n = 89) 57 (64.0) 51.7 46.83 (24.67–58.58) 65.2 Adenoid Cystic Carcinoma (96.6) Skin Cancer (n = 32) 7 (21.9) 81.3 65.75 (62.44–76.43) 92.3 Cutaneous Squamous Cell Carcinoma (93.8) Small Bowel Cancer (n = 51) 8 (14.0) 64.7 61.79 (55.08–67.82) 78.0 Small Bowel Cancer (60.8) Soft Tissue Sarcoma (n = 328) 88 (26.8) 55.8 59.75 (51.00–69.17) 77.4 Leiomyosarcoma (29.0) Thyroid Cancer (n = 132) 71 (53.8) 50.7 65.48 (57.91–72.96) 61.8 Poorly Differentiated Thyroid Cancer (47.8) Uterine Sarcoma (n = 148) 27 (18.2) 0 55.00 (51.00–62.82) 57.4 Uterine Leiomyosarcoma (87.2) BM = bone metastasis, IQR = interquartile range. Age at diagnosis was presented as median (IQR). Table 2 Comparison of survival between BM and OSM population Overall Survival (months) BM Survival (months) OSM Survival (months) p PS (BM > OSM) 95% CI Cliff's δ 95% CI Breast Cancer 19.95 (9.75–36.28) 19.39 (10.13–34.82) 20.40 (9.52–37.93) 0.200 0.483 (0.459, 0.507) -0.033 (-0.083, 0.014) Thyroid Cancer 16.51 (7.52–26.24) 17.79 (10.77–27.4) 12.32 (4.8-23.02) 0.055 0.595 (0.491, 0.690) 0.191 (-0.008, 0.385) Uterine Sarcoma 27.78 (13.24–60.90) 16.59 (9.52–28.16) 35.28 (15.84-75.00) 0.002* 0.314 (0.218, 0.412) -0.373 (-0.560, -0.165) Colorectal Cancer 15.31 (7.62–27.09) 16.45 (8.63–29.52) 14.73 (7.30-26.02) 0.010* 0.542 (0.512, 0.573) 0.085 (0.023, 0.147) Soft Tissue Sarcoma 17.12 (7.96–32.75) 15.41 (7.85–24.36) 18.17 (8.08–38.54) 0.054 0.430 (0.368, 0.499) -0.139 (-0.271, 0.014) Prostate Cancer 14.98 (6.29–28.18) 15.36 (6.70- 28.81) 13.13 (5.25–24.34) 0.047* 0.545 (0.501, 0.592) 0.091 (0.003, 0.183) Small Bowel Cancer 12.45 (7.05–18.66) 14.79 (7.36–19.60) 11.79 (7.05–18.66) 0.632 Endometrial Cancer 11.93 (5.45–20.49) 14.39 (9.21-23.00) 11.79 (4.86–18.79) 0.012* 0.589 (0.583, 0.595) 0.178 (0.166, 0.191) Renal Cell Cancer 13.34 (5.65–27.12) 13.34 (5.54–28.62) 13.09 (5.81–24.59) 1.000 Ovarian Cancer 12.85 (7.18–18.56) 12.32 (7.63–20.45) 13.08 (6.45–18.02) 0.582 Esophagogastric Cancer 12.75 (6.40-20.76) 12.22 (5.92–19.67) 13.05 (6.54–20.90) 0.437 Anal Cancer 11.96 (6.80-19.77) 12.17 (6.80–20.50) 11.96 (6.93–19.60) 0.941 Salivary Gland Cancer 13.40 (7.99–24.94) 11.53 (6.47–23.59) 18.77 (10.45–30.96) 0.030* 0.361 (0.248, 0.479) -0.279 (-0.506, -0.038) Gastrointestinal Neuroendocrine Tumor 12.65 (7.25–22.41) 11.30 (7.21–22.03) 12.85 (7.26–22.11) 0.964 Non-Small Cell Lung Cancer 11.30 (4.90-22.02) 11.05 (4.34–22.14) 11.40 (4.95–21.56) 0.325 Melanoma 14.04 (6.21–38.36) 10.81 (4.30-20.63) 30.00 (11.00-60.46) < 0.001* 0.268 (0.216, 0.321) -0.464 (-0.568, 0.358) Hepatobiliary Cancer 11.56 (5.71–21.57) 10.09 (4.57–19.70) 12.06 (6.21–22.80) 0.099 0.454 (0.398, 0.509) -0.093 (-0.205, 0.019) Bladder Cancer 9.96 (4.06–15.96) 9.76 (3.94–15.70) 10.14 (4.17–16.09) 0.815 Head and Neck Cancer 8.05 (3.31–14.84) 9.07 (3.70-15.69) 7.00 (3.03–13.13) 0.128 0.553 (0.484, 0.619) 0.106 (-0.032, 0.238) Pancreatic Cancer 9.11 (4.14–17.40) 8.94 (3.93–17.47) 9.30 (4.37–17.39) 0.208 Cervical Cancer 10.64 (5.08–16.86) 8.68 (2.69–18.12) 10.64 (6.00-16.34) 0.776 Small Cell Lung Cancer 9.55 (4.77–14.53) 8.42 (4.83–13.52) 11.06 (4.79–15.40) 0.114 0.439 (0.363, 0.508) -0.122 (-0.274, 0.015) Germ Cell Tumor 8.41 (2.52–18.55) 8.20 (1.80–9.70) 10.91 (3.96–25.53) 0.116 0.368 (0.330, 0.406) -0.265 (-0.340, 0.189) Skin Cancer 9.78 (5.50-17.78) 7.72 (5.63–21.14) 9.89 (5.55–17.41) 1.000 Appendiceal Cancer 11.50 (6.93–18.56) 3.84 (3.27–11.88) 12.64 (7.74–18.89) 0.058 0.279 (0.237, 0.321) -0.442 (-0.527, 0.358) BM = bone Metastasis, OSM = other sites metastasis, PS = probability of superiority, CI = confidence interval. p *: p < 0.05, p : 0.05 < p ≤ 0.20, PS and Cliff’s δ were calculated only for cancers with p ≤ 0.20. BM vs. OSM Significant differences in survival between BM and OSM patients were observed in uterine sarcoma ( p = 0.002), colorectal cancer ( p = 0.010), prostate cancer ( p = 0.047), endometrial cancer ( p = 0.012), salivary gland cancer ( p = 0.030), and melanoma ( p < 0.001) (Table 2). In addition, there were 8 other cancers that demonstrated a p- value ranging between 0.05 and 0.20. Internal survival analysis in 9 of 14 cancers showed that BM patients tended to have a shorter survival except for thyroid, colorectal, prostate, endometrial, and head and neck cancer. The absolute values of Cliff’s δ ranged from 0.033 to 0.464. The most pronounced difference was observed in melanoma ( p < 0.001, PS = 0.268, |Cliff’s δ|=0.464) (Table 2). Cancer Types Significant survival differences were observed among BM and further only bone metastasis (OBM) patients across different cancer types (both p < 0.001; Fig. 2 ). The CV of BM and OBM patients ranged from 76.04% to 103.81% and 69.40% to 138.20% (Supplementary Table 1 and Table 2). All types of cancer were categorized into three groups based on median (IQR) (Table 2). The cancers with the most favorable prognosis included breast cancer (19.39, 10.13–34.82), thyroid cancer (17.79, 10.77–27.4), uterine sarcoma (16.59, 9.52–28.16), colorectal cancer (16.45, 8.63–29.52), soft tissue sarcoma (15.41, 7.85–24.36), and prostate cancer (15.36, 6.70-28.81). In contrast, hepatobiliary cancer (10.09, 4.57–19.70), bladder cancer (9.76, 3.94–15.70), head and neck cancer (9.07, 3.70-15.69), pancreatic cancer (8.94, 3.93–17.47), cervical cancer (8.68, 2.69–18.12), small cell lung cancer (8.42, 4.83–13.52), germ cell tumor (8.20, 1.80–9.70), skin cancer (7.72, 5.63–21.14), and appendiceal cancer (3.84, 3.27–11.88) demonstrated the poorest survival outcomes. Metastatic Sites Different metastatic sites were associated with significantly different survival outcomes among BM patients in different cancers (Fig. 3 , Supplementary Table 3), such as brain metastasis in breast cancer ( p < 0.001), liver metastasis in prostate cancer ( p < 0.001), bone plus liver metastasis in colorectal cancer ( p < 0.001), and abdominal metastasis in soft tissue sarcoma ( p < 0.001). However, no metastatic site consistently exerted a significant impact on survival across all cancers, neither as a risk nor a protective factor. Notably, as illustrated by the Kaplan–Meier survival curves, when BM was analyzed as a prognostic factor, it was associated with longer survival in breast, prostate, and thyroid cancer, whereas in colorectal, hepatocellular carcinoma, soft tissue sarcoma, and uterine sarcoma, it tended to indicate shorter survival (Fig. 3 ). Pathological Subtypes BM patients with poorly differentiated or undifferentiated pathological subtypes demonstrated significantly shorter survival compared with other pathological subtypes in ovarian cancer (low-grade serous ovarian cancer, p = 0.042), thyroid cancer (anaplastic thyroid carcinoma, p < 0.001), pancreatic cancer (undifferentiated carcinoma of the pancreas, p < 0.001), non–small cell lung cancer (NSCLC) (poorly differentiated NSCLC, p < 0.001), breast cancer (triple negative, p < 0.001), and soft tissue sarcoma (dedifferentiated liposarcoma, p < 0.001) (Fig. 4 , Supplementary Table 4). Particularly, this difference was mostly obvious within the first 12 months. Notably, no significant survival differences were observed among the remaining differentiated pathological subtypes. Moreover, in NSCLC, patients with lung adenocarcinoma demonstrated a better survival outcome. Sex and Age Sex analysis was conducted among cancers with a relatively balanced sex distribution (excluding reproductive system primary cancers). The male proportion ranged from 38.5% to 81.0%. Except for NSCLC ( p = 0.028), no significant survival differences were observed between male and female patients across all the cancers (Supplementary Table 5). Age thresholds associated with significant survival differences were identified in 18 cancer types, ranging from 40 to 76 years, with 12 cancers clustering between 55 and 70 years (Supplementary Table 6). Patients older than 60 years showed significantly poorer survival ( p 60 years was applied for all cancers (Fig. 5 ). Cox Proportional Hazard Regression Model The multivariate Cox proportional hazard regression model incorporated the following covariates: primary cancer type (1, 2, and 3; Table 2), age at diagnosis, pathological subtype (poorly differentiated or undifferentiated vs. differentiated), sex, OBM, central nervous system metastasis, lung metastasis, and liver metastasis (Fig. 6 ). The results indicated that the primary cancer type (2 vs. 1, HR = 1.422, p < 0.001; 3 vs. 1, HR = 1.758, p < 0.001), poorly differentiated or undifferentiated (HR = 1.249, p < 0.001), male sex (HR = 1.086, p = 0.002), and presence of liver metastasis (HR = 1.066, p = 0.029) were identified as independent risk factors for survival in BM (Fig. 6 ). Discussion BM patients represent a heterogeneous and clinically important population. With the rapid advancement of comprehensive and multidisciplinary treatment strategies, accurately predicting life expectancy to align with the expanding indications for local surgical interventions has become a central challenge in optimizing the clinical management of patients with metastatic spinal tumor. In this multicenter, public database–based cohort study, 13,742 metastatic patients across 25 primary cancers were included, and several potential prognostic risk factors for the survival of BM patients were identified. The prognosis of BM patients was demonstrated to be significantly different according to primary cancer types, pathological differentiation, and metastatic distribution, which stresses the necessity for cancer-specific survival analysis and predictive model construction. Heterogeneity and Clinical Implications of Bone Metastasis BM has historically been regarded as a negative prognostic indicator; however, our study illustrated that its impact is not uniform across different cancer types [ 45 , 46 ]. In cancers with osteotropic metastasis (having an affinity for bone tissue), such as breast, prostate, thyroid, head and neck, colorectal, and endometrial cancers identified in this study, BM did not consistently shorten survival compared with OSM. In contrast, in tumors with low bone affinity and more aggressive behavior, including melanoma, hepatobiliary, and pancreatic cancers, BM was strongly correlated with worse survival. The observations are consistent with the previous research of Bollen, Luksanapruksa, and et al., in which BM occurring in breast or thyroid cancers generally indicates a more favorable prognosis than visceral metastasis [ 46 – 49 ]. These results confirm that the prognostic influence of spinal metastasis varies substantially among different primary cancers, arguing against the notion that BM or visceral metastasis represents a universal high-risk factor and supporting the rationale for constructing cancer-specific prognostic models. While the findings above suggest that our initial assumption of a uniform survival distribution across metastatic cancers does not hold, it does not reject the feasibility of expanding the recruited cohort in a cancer-specific analysis. By treating BM as an independent prognostic factor, both BM and OSM patients can be integrated to develop cancer-specific survival prediction models that better capture the biological and clinical diverse characteristics of metastatic disease and reveal potential prognostic risk factors. Pathological Differentiation as a Novel Prognostic Risk Factor The univariate analysis and multivariate Cox regression model revealed that pathological differentiation was a cancer-specific and robust prognostic factor of survival of BM patients. Poorly differentiated or undifferentiated cancers—such as triple-negative breast carcinoma, anaplastic thyroid carcinoma, and undifferentiated pancreatic carcinoma—exhibited significantly shorter survival times. Similar findings were reported by few studies, which demonstrated that histologic grade cast an independent influence on survival after adjustment for age, metastatic pattern, and treatment [ 49 – 53 ]. But the pathological differentiation was not widely included in existing prognostic prediction models. The underlying mechanism may be that poorly or undifferentiated metastatic cancers possess enhanced epithelial-mesenchymal transition (EMT) capability, angiogenic potential, and immune evasion, which collectively accelerate metastatic progression [ 54 – 56 ]. These aggressive biological characteristics may enable tumor cells to invade organs or anatomic sites that are otherwise less permissive to metastasis, leading to increased tumor burden and worse outcomes [ 57 ]. This phenomenon also explains the reason that no significant survival differences were observed among other histological subtypes once metastasis had occurred within a single cancer type. Therefore, incorporating the level of pathological differentiation into future prognostic models may substantially improve predictive accuracy of survival estimation in clinical management of BM patients. Cancer-specific Molecular Targeted Therapy The improved survival observed in lung adenocarcinoma compared with squamous or small-cell carcinoma might be attributed to the wider application of molecular targeted therapy such as epidermal growth factor receptor (EGFR) inhibitors in patients with lung adenocarcinoma compared with other NSCLC subtypes [ 58 , 59 ]. Therefore, recruiting patients who have received molecular targeted therapy with clear survival outcomes for life expectancy prediction model construction will become increasingly important. This approach was currently limited by the small number of eligible patients. However, as discussed above, expanding the analytical cohort in a cancer-specific manner is making this strategy feasible. Based on such a framework, future models could categorize patients into distinct pathological subtypes characterized by different molecular targets, such as EGFR-mutated lung adenocarcinoma and HER2-positive breast cancer, each demonstrating unique survival characteristics associated with cancer-specific molecular biomarkers [ 60 , 61 ]. By incorporating molecularly defined patient subgroups and their various therapeutic impacts on survival outcome, the corresponding cancer-specific predictive models are expected to provide more precise life expectancy estimation and enhanced biologic interpretability for clinical decision-making in BM patients. Primary Cancer Type, Age, and Sex Consistent with previous studies, primary cancer type emerged as the most powerful determinant of survival [ 46 , 47 ]. In this study, different primary cancers were stratified into three prognostic tiers, yielding a survival hierarchy that closely parallels the scoring system developed by Tokuhashi, Katagiri, and et al., but greatly expands coverage of cancer types and comprehensively reflects contemporary treatment paradigms [ 13 ]. The correlation between age above 60 years and shorter survival is likely to be explained by poorer baseline functional status, treatment tolerance, age-related immune decline, and greater comorbidity burden [ 62 – 65 ]. The age threshold across multiple cancers is consistent with previous studies and suggests that it represents a clinically reasonable prognostic factor for risk stratification and surgical decision-making [ 66 – 68 ]. In contrast, sex did not remain a significant prognostic variable after adjustment for primary cancer type, indicating that previously reported sex disparities in survival may have been confounded by the unbalanced distribution of sex-specific cancer and may not be widely adopted for BM patient prognosis prediction [ 69 – 73 ]. This finding demonstrates that demographic variables may exert only secondary prognostic influence once primary cancer and pathological type are determined. Metastatic Distribution and Organ-Specific Effects No metastatic site was proved to be a universal risk factor across all cancers in this study, which stands in contrast to previous studies that broadly applied visceral or brain metastasis as adverse prognostic indicators [ 13 , 46 ]. Although specific sites may generate similar negative impacts on certain cancers (e.g., liver metastasis in colorectal and prostate cancer, brain metastasis in breast cancer and NSCLC, and el at.), these effects were inconsistent across other cancers. This heterogeneity has been sporadically reported in recent studies and further confirmed in our study [ 74 – 77 ]. Notably, OBM was associated with relatively favorable outcomes in osteotropic cancers, including breast, prostate, and thyroid cancer as previously discussed. This finding reflects the heterogeneous affinity to bone tissue among different primary cancers, which supports the argument that BM should be regarded as an independent prognostic factor in assessing metastatic cancers [ 78 ]. The mechanisms of osteotropic phenomenon are thought to be regulated by complicated molecular pathways, including chemokine (C-X-C motif) receptor 4 (CXCR4), integrins, and extracellular vesicle-mediated premetastatic niche formation, which vary substantially across different tumors [ 79 – 81 ]. The lack of a clear consensus further emphasizes the necessity for sophisticated, cancer-specific analyses rather than indiscriminate pooled analyses, which may bring significant selection bias due to the unbalanced distribution of different cancer populations, and ultimately reduce predictive model generalizability. Limitation This study incorporated a large multicenter cohort of metastatic cancer patients, utilizing survival data collected over the past decade. The data preprocessing protocol, involving the standardized data curation, imputation of missing data, and bootstrap validation method, was conducted according to recently published guidelines or consensuses for prognostic model development [ 37 ]. The univariate and multivariate survival analysis also adhered to the latest statistical framework, as well as referencing the previous prognostic prediction model [ 13 , 38 , 82 , 83 ]. Nonetheless, several limitations should be seriously acknowledged. First, in approximately 50% of the included primary cancers, the number of BM patients was fewer than 100, and the heterogeneity in study design among contributing centers may limit the overall generalizability of the findings. Second, the missing or insufficient results of the immunohistochemical, hematologic, and radiologic examinations, as well as the baseline neurological or general function evaluations, including complete blood counts, serum biochemical testing, CT-based bone stability evaluation, Frankel grading, and Eastern Cooperative Oncology Group (ECOG) performance score, greatly restricted the integration of existing variables into the subsequent life expectancy prediction model. This represents an important limitation of the present study, which we aim to address in the future by including data from our institutional patient cohort. Furthermore, when BM was involved, the specific skeletal sites in most patients are recorded simply as “bone” rather than being accurately described as particular locations (e.g., spine, rib, skull, or extremity). Although epidemiological patterns indicate that most bone metastases are observed in the spine, the deficient anatomical description in this study still introduces challenges in identifying prognostic risk factors associated with different skeletal sites and deriving more targeted, site-specific conclusion [ 1 – 3 ]. Finally, treatment-related information was incomplete for most patients, particularly regarding the use of molecular targeted therapy, chemotherapy drug regimen, surgical procedure and margin, and radiation technique. The lack of standardized treatment data prevented the incorporation of these potentially protective, cancer-specific therapeutic variables into the prognostic prediction model, which is a limitation commonly shared by many large-scale registry-based studies [ 46 , 84 – 88 ]. Our future work will focus on integrating our grade A tertiary hospital data with complete examination results and treatment responses to develop more refined, cancer-specific prognostic prediction models for surgical decision-making in BM patients. Conclusion BM has an independent, substantial influence on survival across different metastatic cancers, indicating that BM patients represent a distinct clinical subgroup with great heterogeneity. Primary cancer type, pathological subtypes with poor differentiation, and age at diagnosis above 60 may further significantly influence the prognosis of patients with spinal metastasis. No single metastatic site served as a universal prognostic factor across different cancer types. BM tended to indicate a favorable outcome in cancers with osteotropic characteristics. The emergence of distinct metastatic sites exerted heterogeneous impacts across cancers, differing from the conventional assumption. This study suggests that expanding the recruited population for life expectancy prediction model construction within a cancer-specific framework is feasible and clinically meaningful. It is necessary to analyze the metastatic patterns within each cancer and include more cancer-specific prognostic risk factors, such as pathological subtypes, when constructing predictive models. In the future, developing prognostic models stratified by primary cancer type may represent a promising direction to improve model generalizability and provide precise survival estimation to guide surgical decision-making. Abbreviations AJCC: American Joint Committee on Cancer; BM: Bone Metastasis; BMI: Body Mass Index; CI: Confidence Interval; CV: Coefficient of Variation; EGFR: Epidermal Growth Factor Receptor; EMT: Epithelial-Mesenchymal Transition; ECOG: Eastern Cooperative Oncology Group; HF: Hazard Ratio; IHC: Immunohistochemistry; IQR: Interquartile Range; K–S: Kolmogorov–Smirnov; MAXSTAT: Maximally Selected Rank Statistics; NSCLC: Non–Small Cell Lung Cancer; OBM: Only Bone Metastasis; OS: Overall Survival; OSM: Other Sites of Metastasis; PH: Proportional Hazards; PS: Probability of Superiority; RECORD: Reporting of Studies Conducted using Observational Routinely-Collected Health Data; STROBE: Strengthening the Reporting of Observational Studies in Epidemiology; TNM: Tumor-Node-Metastasis; VIF: Variance Inflation Factor; WFNS: World Federation of Neurosurgical Societies. Declarations Ethics approval and consent to participate The study is retrospective and registry-based without clinical or experimental intervention, and the data used were collected from a public database. For these reasons, informed consent was applied for exemption. The study was conducted with the support of Peking University Third Hospital. Ethical approval was granted by the Research Ethics Committee (approval number: LM2025366) Consent for publication Not applicable. Availability of data and materials The datasets supporting the conclusions of this study are available from the corresponding author on reasonable request or can be accessed from the cBioPortal for Cancer Genomics (https://www.cbioportal.org/). Competing interests The authors declare that they have no competing interests. Funding This study was supported by the Peking University Third Hospital (Grant No. BYSYZD2023017) and the National Natural Science Foundation of China (Grant Nos. 82201644 and 82471505). The funding bodies had no role in the design of the study, data collection, analysis, interpretation of data, or in writing the manuscript. Authors’ contributions Z.L. Yun, Y.C. Tang, and J. Sun conceived and designed the study. Z.L. Yun, J.C. Lei, and G.Q. Zhang collected the data. Z.L. Yun and Y.C. Tang performed the statistical analysis. Z.L. Yun drafted the manuscript. F. Wei and X.G. Liu supervised the study and were responsible for manuscript revision and correspondence. All authors read and approved the final manuscript. Acknowledgements We sincerely thank Prof. Wei and Prof. Liu for their valuable guidance on integrating this project with clinical practice. The authors gratefully acknowledge Peking University Third Hospital and the National Natural Science Foundation of China for hardware support in data processing and financial funding for the overall implementation of this project. Authors’ information Not applicable. Clinical trial number Not applicable. 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Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yanchao","middleName":"","lastName":"Tang","suffix":""},{"id":567222241,"identity":"e2a2bd2c-0e34-4987-9a77-c972b578fad7","order_by":2,"name":"Jie Sun","email":"","orcid":"","institution":"University of Michigan","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Sun","suffix":""},{"id":567222242,"identity":"f901308f-896d-42dd-b8b5-886128d0cb8e","order_by":3,"name":"Juncai Lei","email":"","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":false,"prefix":"","firstName":"Juncai","middleName":"","lastName":"Lei","suffix":""},{"id":567222243,"identity":"b255450a-6cf7-4caa-8555-e038a5c55bba","order_by":4,"name":"Gangqiang Zhang","email":"","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":false,"prefix":"","firstName":"Gangqiang","middleName":"","lastName":"Zhang","suffix":""},{"id":567222244,"identity":"4c1da221-54c3-4a84-b601-74450c50585c","order_by":5,"name":"Feng Wei","email":"","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":false,"prefix":"","firstName":"Feng","middleName":"","lastName":"Wei","suffix":""},{"id":567222246,"identity":"819c7411-1da3-45ca-8fe7-4aa52dae86cc","order_by":6,"name":"Xiaoguang Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYFAC5oYDDGxgFuODhIoaYrQwQrTwADUbPDhzjDgtDFAtbJIPW5gJazA4v7DxME+Zjbw9e++zisQGNgb+9u4EvFokZzxsOMxzLs2wh+e42Y3EHTIMEmfObsCrhV/iYMNh3rbDCTwSaWw3Es+wMRhI5OLXwoaspSCxjZmwFn7+RoQWBqK0SM5gbDg4B+SXM8eYJRLOHOMh6BeD84cPf3gDDDH29jbGjz8qauT423vxa2GQSEDl8+BXDvbMAcJqRsEoGAWjYIQDAHogSKe5NwdQAAAAAElFTkSuQmCC","orcid":"","institution":"Peking University Third 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12:47:34","extension":"docx","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":41881,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-8384769/v1/48dc24d0d76a16f80291bdfa.docx"},{"id":99788567,"identity":"16db534e-2f06-4299-9c04-a99a9c3ac913","added_by":"auto","created_at":"2026-01-08 12:47:07","extension":"xml","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":211979,"visible":true,"origin":"","legend":"","description":"","filename":"1835ea112cc94d24a3bf6790df8a13c51enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8384769/v1/a5576326ed3b77f37dc79f19.xml"},{"id":99340283,"identity":"b019e935-0d88-477a-8df9-564e43e27f57","added_by":"auto","created_at":"2026-01-01 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12:47:57","extension":"pdf","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":201029,"visible":true,"origin":"","legend":"","description":"","filename":"Fig.5.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8384769/v1/a6326b4da16ebe87060b0e8b.pdf"},{"id":99340278,"identity":"e8c2f5b4-bbc3-4efd-9be6-fd77aca2476f","added_by":"auto","created_at":"2026-01-01 05:41:29","extension":"pdf","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":143740,"visible":true,"origin":"","legend":"","description":"","filename":"Fig.6.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8384769/v1/acf2556441bef1281f6cb7cf.pdf"},{"id":99340282,"identity":"e396aa79-2c09-486e-ab76-285732f415e7","added_by":"auto","created_at":"2026-01-01 05:41:29","extension":"xml","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":213965,"visible":true,"origin":"","legend":"","description":"","filename":"1835ea112cc94d24a3bf6790df8a13c51structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8384769/v1/88d91b050907bbb6a569d230.xml"},{"id":99788828,"identity":"f241f239-638b-47ac-a344-55497bfc70f6","added_by":"auto","created_at":"2026-01-08 12:47:58","extension":"html","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":234071,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8384769/v1/aee652978d9de7a815909749.html"},{"id":99340267,"identity":"27580b15-8d7d-43e1-8966-be1e089d6054","added_by":"auto","created_at":"2026-01-01 05:41:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":236501,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of included patients.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8384769/v1/c56ed6793d832f4240cffc2c.png"},{"id":99340265,"identity":"d5f4ca2d-b89a-4126-a6a3-2ce56639bd56","added_by":"auto","created_at":"2026-01-01 05:41:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":571379,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier survival curves of survival comparisons among different primary cancers. a: patients with bone metastases possibly combined with other sites of metastasis; b: patients only with bone metastasis.\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-8384769/v1/1c74838c2f54a8ebd32c320b.png"},{"id":99788317,"identity":"cb517078-e554-48dd-ae03-58988e53e071","added_by":"auto","created_at":"2026-01-08 12:46:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":624477,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier survival curves according to metastatic sites in BM patients. NSCLC=non-small cell lung cancer, BM=bone metastasis.\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-8384769/v1/29583435ef6436990de625ad.png"},{"id":99788851,"identity":"06a43cd2-561d-449c-b76a-c20899c42e7f","added_by":"auto","created_at":"2026-01-08 12:48:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":580854,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier survival curves according to pathological differentiation and histological subtypes in BM patients\u003cem\u003e. \u003c/em\u003eNSCLC=non-small cell lung cancer, BM=bone metastasis.\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-8384769/v1/e423d02719e9374f3e265fb5.png"},{"id":99788751,"identity":"2cd19b6c-c925-44b6-8c33-cdcce036045d","added_by":"auto","created_at":"2026-01-08 12:47:41","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":102937,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier survival curves according to age at diagnosis in patients with bone metastases.\u003c/p\u003e","description":"","filename":"Fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-8384769/v1/2fe8061e738674dc07be71c9.png"},{"id":99788395,"identity":"05a263aa-5835-4016-8c4d-edcef6b008e1","added_by":"auto","created_at":"2026-01-08 12:46:34","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":471439,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot based on multivariate Cox proportional hazards model. The prognostic factors for survival in bone metastasis patients were demonstrated. HR=hazard ratio, CI=confidence interval, UD=undifferentiated, CNS=central nervous system.\u003c/p\u003e","description":"","filename":"Fig.6.png","url":"https://assets-eu.researchsquare.com/files/rs-8384769/v1/9aff99aaf3052c30292e160b.png"},{"id":105223348,"identity":"e710823e-70d6-4d79-a8d2-c91cbaf650c6","added_by":"auto","created_at":"2026-03-23 16:04:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3053722,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8384769/v1/3dc8b142-953e-4993-9103-0142a7d8932d.pdf"},{"id":99788071,"identity":"8b6d111f-f052-49be-8936-eeaf49491683","added_by":"auto","created_at":"2026-01-08 12:44:13","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":41881,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-8384769/v1/810bbd3c6a76b337dd35758d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prognostic Risk Factors for Cancer-Specific Bone Metastasis: A Registry-Based Analysis of 13,742 Patients ","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBone metastasis (BM) represents a prevalent and debilitating complication in patients with advanced cancer, frequently leading to severe neurological pain, pathological fractures, spinal instability, motor weakness, sensory disturbance, and sphincter dysfunction, significantly compromising the quality of life [1-3]. Over the past decade, with rapid advancements in comprehensive oncological management, including stereotactic body radiation therapy, immunotherapy, and molecular targeted therapy, the life expectancy and quality of life of patients with advanced malignancies have been markedly improved [4-9]. Therefore, the indications for surgical intervention aimed at improving quality of life and preserving neurological function have expanded substantially compared with the past. According to the four-point consensus for preoperative assessment established by the World Federation of Neurosurgical Societies (WFNS), the evaluation of life expectancy (at least \u0026gt;3 months) is primarily crucial in the determination of surgical intervention [10]. In this context, the precise identification of prognostic risk factors for life expectancy and surgical guidance is paramount to optimize the clinical benefit of BM patients.\u003c/p\u003e\n\u003cp\u003eHowever, previous prognostic models have generally demonstrated unsatisfying accuracy in external validation, which can be attributed to several factors: firstly, most studies were based on single-center or small-sample data. The limited patient numbers and the potentially unbalanced distribution of cancer types often led to systematic errors (e.g., overfitting bias), resulting in deficient model generalizability [11-18]. Secondly, internal heterogeneity among patients across different cancers makes it challenging for a single model to accurately apply to diverse populations [19-23]. Moreover, previous predictive models insufficiently integrated histological or pathological, genetic, or molecular indicators which are highly specific to cancer subtypes, leading to an insufficient assessment of the biological characteristics of the primary tumors [24-28]. \u0026nbsp;Meanwhile, the constant improvement of survival (discussed above) renders the input data of previous models outdated, necessitating the updates to reflect contemporary clinical progress [29, 30].\u003c/p\u003e\n\u003cp\u003eThese dilemmas raise the question of how to reasonably expand the sample size and discover cancer-specific variables that can significantly influence survival even at an advanced stage. Therefore, in this study, novel risk factors such as pathological subtype and metastatic sites were explored, particularly from a cancer-specific perspective, thereby providing more precise evidence for the life expectancy prediction and surgical decision-making process in BM patients. Moreover, the feasibility of extending the recruited population from BM patients to all patients with metastatic cancer was explored and evaluated. It was started from the assumption that there are no significant differences in the survival distribution of BM patients versus patients with other sites of metastasis (OSM). The eligible population for predictive model construction would substantially expand if the above assumption were validated or effectively refined.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eStudy design and setting\u003c/p\u003e \u003cp\u003eThis study strictly followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines and was conducted in accordance with the REporting of studies Conducted using Observational Routinely collected health Data (RECORD) statement [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Informed consent was waived owing to the retrospective design of the study and the anonymization of all patient information. All data used in this research were obtained from the accessible cBioPortal for Cancer Genomics database. The study was conducted with the support of a tertiary hospital in Beijing, China. Ethical approval was granted by the Research Ethics Committee of our institution (approval number: LM2025366).\u003c/p\u003e \u003cp\u003ePatients\u003c/p\u003e \u003cp\u003eClinical data of patients diagnosed with metastatic cancer between 2015 and 2025 were extracted from the cBioPortal for Cancer Genomics database (publicly accessible) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Recruited cases were diagnosed with tumor-node-metastasis (TNM) stage M1 or American Joint Committee on Cancer (AJCC) stage IV and had a clear survival outcome (n\u0026thinsp;=\u0026thinsp;17,533, including 31 cancers) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Studies were excluded if the number of patients with bone metastasis (BM) was fewer than 5 in a single study, the specific cancer type was undefined (e.g., cancer of unknown primary), or the study cohort consisted of patients with primary bone tumors (n\u0026thinsp;=\u0026thinsp;3,791). BM patients were recognized when labels for the metastatic sites included \u0026ldquo;bone\u0026rdquo; or a specific skeletal site (n\u0026thinsp;=\u0026thinsp;6,011). Ultimately, a total of 13,742 patients with metastatic cancer involving 25 types of cancer were included in the final (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eData Sources and Variables\u003c/p\u003e \u003cp\u003eIn total, 42 clinical studies were obtained from the cBioPortal for Cancer Genomics database involving 25 distinct cancer types. The clinical variables included study identity document (ID), patient ID, sample ID, metastatic sites (lung, liver, intra-abdominal region, pelvis including reproductive system, bladder, and rectum, central nervous system, adrenal gland, bone, spine, and vertebrae), primary cancer type, pathological subtype, overall survival status, overall survival time, age at diagnosis, current age, age at surgical procedure, sex, race, religion, body mass index (BMI), Gleason score, prior treatment regimen, comorbidities, TNM stage, and AJCC stage.\u003c/p\u003e \u003cp\u003eData Preprocessing\u003c/p\u003e \u003cp\u003eGene-related variables that were not associated with clinical information were removed. Clinical variables with the same meanings but different names across studies were merged: (1) variables differing only in letter case, (2) variables representing synonymous terms, and (3) age at diagnosis was missing but replaced by age at sampling in individual studies. Variables with a missing rate greater than 30% (e.g., BMI, religion, prior treatment regimen, etc.) were excluded [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Multiple imputation was applied to handle missing values in continuous variables, such as age at diagnosis [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Both structured clinical data (e.g., laboratory test results) and unstructured information (e.g., electronic medical records and imaging reports) were adopted to determine the metastatic sites.\u003c/p\u003e \u003cp\u003ePrimary Outcomes\u003c/p\u003e \u003cp\u003eThe primary outcome was the overall survival (OS) defined as the time from the diagnosis of metastatic cancer (TNM stage M1 or AJCC stage IV) to death.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eThe normal distribution of continuous variables was assessed using the Kolmogorov\u0026ndash;Smirnov (K\u0026ndash;S) test [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The Mann\u0026ndash;Whitney test and Kruskal\u0026ndash;Wallis test were respectively applied to compare the difference of variable distribution between two or more groups in non-normally distributed data [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Variables not following a normal distribution were summarized as median with interquartile range (IQR). The coefficient of variation was used to reflect the degree of dispersion in survival time among different populations.\u003c/p\u003e \u003cp\u003eA specific cancer was included in a univariate analysis if its subset population with the occurrence of the corresponding event and primary outcome was approximately equal to or more than 30 [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The Kaplan\u0026ndash;Meier curve and log-rank (Mantel\u0026ndash;Cox) test were used to evaluate the effect of a single risk factor on survival time [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Variables with a \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.2 in the univariate analysis were included in the multivariate model [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Multivariate analysis was performed using the Cox proportional hazards model [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. The collinearity was assessed by the variance inflation factor (VIF) with an acceptable threshold of \u0026lt;\u0026thinsp;5. The proportional hazards (PH) assumption was verified using the Schoenfeld residuals test. Exhaustive search, tree models, and the maximally selected rank statistics (MAXSTAT) were applied to identify optimal cut-off values for age-related risk of survival time [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The model stability was evaluated using the bootstrap method, and 95% confidence intervals (CIs) were reported.\u003c/p\u003e \u003cp\u003eStatistical significance was defined as \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The clinical effect size of the significant difference was further assessed using Cliff\u0026rsquo;s δ and the probability of superiority (PS) both with 95% CIs. Statistical analyses were performed using IBM SPSS Statistics (version 31.0.0.0; IBM Corp., Armonk, NY, USA) and R (version 4.5.1; R Foundation for Statistical Computing, Vienna, Austria). Survival curves were generated using GraphPad Prism (version 10.0.0; GraphPad Software, Boston, MA, USA).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003ePatient Baseline Characteristics\u003c/p\u003e \u003cp\u003eA total of 13,742 patients were included in the final analysis involving 25 different cancer types. The population of BM patients was more than 100 in 11 cancers. The proportion of bone metastasis varied substantially across cancer types, ranging from 10.1% to 74.7%. Approximately half of the cancers showed a predominant distribution of pathological subtypes (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline patient demographics and clinical characteristics\u003c/p\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBM (n, %)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSex (male, %)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAge at Diagnosis (years)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRace (white, %)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMain Cancer Type Detailed (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBladder Cancer (n\u0026thinsp;=\u0026thinsp;463)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e189 (40.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e68.06 (60.39\u0026ndash;74.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e87.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBladder Urothelial Carcinoma (80.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreast Cancer (n\u0026thinsp;=\u0026thinsp;1960)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e942 (48.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54.68 (45.72\u0026ndash;63.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e71.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBreast Invasive Ductal Carcinoma (64.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eColorectal Cancer (n\u0026thinsp;=\u0026thinsp;1573)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e426 (27.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57.03 (48.12\u0026ndash;66.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e78.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eColon Adenocarcinoma (67.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEsophagogastric Cancer (n\u0026thinsp;=\u0026thinsp;522)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e155 (29.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e61.33 (51.72\u0026ndash;69.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e79.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStomach Adenocarcinoma (39.7)\u003c/p\u003e \u003cp\u003eEsophageal Adenocarcinoma (34.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHead and Neck Cancer (n\u0026thinsp;=\u0026thinsp;276)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e142 (51.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60.56 (52.14-68.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e78.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOral Cavity Squamous Cell Carcinoma (29.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHepatobiliary Cancer (n\u0026thinsp;=\u0026thinsp;610)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e135 (22.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e63.71 (55.54\u0026ndash;71.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e79.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIntrahepatic Cholangiocarcinoma (45.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMelanoma (n\u0026thinsp;=\u0026thinsp;334)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e173 (51.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e62.59 (50.55\u0026ndash;71.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e90.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCutaneous Melanoma (56.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Small Cell Lung Cancer (n\u0026thinsp;=\u0026thinsp;2835)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1677 (59.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e67.33 (59.00-74.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e68.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLung Adenocarcinoma (81.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePancreatic Cancer (n\u0026thinsp;=\u0026thinsp;2060)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e827 (40.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66.00 (58.00\u0026ndash;73.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e80.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePancreatic Adenocarcinoma (92.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProstate Cancer (n\u0026thinsp;=\u0026thinsp;860)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e642 (74.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e67.80 (61.06-75.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e70.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eProstate Adenocarcinoma (98.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmall Cell Lung Cancer (n\u0026thinsp;=\u0026thinsp;224)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e110 (49.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66.34 (59.70-72.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e82.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSmall Cell Lung Cancer (91.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnal Cancer (n\u0026thinsp;=\u0026thinsp;40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12 (30.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60.82 (55.55\u0026ndash;69.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e75.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAnal Squamous Cell Carcinoma (100.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAppendiceal Cancer (n\u0026thinsp;=\u0026thinsp;69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7 (10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e62.02 (47.85\u0026ndash;68.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e73.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAppendiceal Adenocarcinoma (56.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCervical Cancer (n\u0026thinsp;=\u0026thinsp;43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12 (27.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e51.48 (41.48\u0026ndash;56.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e60.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCervical Squamous Cell Carcinoma (90.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndometrial Cancer (n\u0026thinsp;=\u0026thinsp;367)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e86 (23.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65.97 (61.24\u0026ndash;71.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e67.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUterine Papillary Serous Carcinoma (37.60)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGastrointestinal Neuroendocrine Tumor (n\u0026thinsp;=\u0026thinsp;80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19 (23.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60.15 (51.41\u0026ndash;70.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e71.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGastrointestinal Stromal Tumor (65.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGerm Cell Tumor (n\u0026thinsp;=\u0026thinsp;51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20 (39.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.42 (25.50\u0026ndash;43.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e72.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMixed Germ Cell Tumor (47.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOvarian Cancer (n\u0026thinsp;=\u0026thinsp;431)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e87 (20.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e61.12 (52.46\u0026ndash;68.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e81.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSerous Ovarian Cancer (83.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenal Cell Cancer (n\u0026thinsp;=\u0026thinsp;164)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e92 (56.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.63 (53.58\u0026ndash;65.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e83.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRenal Clear Cell Carcinoma (90.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSalivary Gland Cancer (n\u0026thinsp;=\u0026thinsp;89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57 (64.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46.83 (24.67\u0026ndash;58.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e65.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAdenoid Cystic Carcinoma (96.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkin Cancer (n\u0026thinsp;=\u0026thinsp;32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7 (21.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65.75 (62.44\u0026ndash;76.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e92.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCutaneous Squamous Cell Carcinoma (93.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmall Bowel Cancer (n\u0026thinsp;=\u0026thinsp;51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8 (14.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e61.79 (55.08\u0026ndash;67.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e78.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSmall Bowel Cancer (60.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoft Tissue Sarcoma (n\u0026thinsp;=\u0026thinsp;328)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e88 (26.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.75 (51.00\u0026ndash;69.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e77.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLeiomyosarcoma (29.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThyroid Cancer (n\u0026thinsp;=\u0026thinsp;132)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71 (53.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65.48 (57.91\u0026ndash;72.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e61.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePoorly Differentiated Thyroid Cancer (47.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUterine Sarcoma (n\u0026thinsp;=\u0026thinsp;148)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27 (18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55.00 (51.00\u0026ndash;62.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e57.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUterine Leiomyosarcoma (87.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eBM\u0026thinsp;=\u0026thinsp;bone metastasis, IQR\u0026thinsp;=\u0026thinsp;interquartile range. Age at diagnosis was presented as median (IQR).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of survival between BM and OSM population\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall Survival (months)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBM Survival (months)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOSM Survival (months)\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\u003ePS (BM\u0026thinsp;\u0026gt;\u0026thinsp;OSM) 95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCliff's δ 95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreast Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19.95 (9.75\u0026ndash;36.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.39 (10.13\u0026ndash;34.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.40 (9.52\u0026ndash;37.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.200\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.483 (0.459, 0.507)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.033 (-0.083, 0.014)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThyroid Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.51 (7.52\u0026ndash;26.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.79 (10.77\u0026ndash;27.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.32 (4.8-23.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.055\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.595 (0.491, 0.690)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.191 (-0.008, 0.385)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUterine Sarcoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.78 (13.24\u0026ndash;60.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.59 (9.52\u0026ndash;28.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.28 (15.84-75.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.314 (0.218, 0.412)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.373 (-0.560, -0.165)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eColorectal Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.31 (7.62\u0026ndash;27.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.45 (8.63\u0026ndash;29.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.73 (7.30-26.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.010*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.542 (0.512, 0.573)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.085 (0.023, 0.147)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoft Tissue Sarcoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.12 (7.96\u0026ndash;32.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.41 (7.85\u0026ndash;24.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.17 (8.08\u0026ndash;38.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.054\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.430 (0.368, 0.499)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.139 (-0.271, 0.014)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProstate Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.98 (6.29\u0026ndash;28.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.36 (6.70- 28.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.13 (5.25\u0026ndash;24.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.047*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.545 (0.501, 0.592)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.091 (0.003, 0.183)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmall Bowel Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.45 (7.05\u0026ndash;18.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.79 (7.36\u0026ndash;19.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.79 (7.05\u0026ndash;18.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndometrial Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.93 (5.45\u0026ndash;20.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.39 (9.21-23.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.79 (4.86\u0026ndash;18.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.012*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.589 (0.583, 0.595)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.178 (0.166, 0.191)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenal Cell Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.34 (5.65\u0026ndash;27.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.34 (5.54\u0026ndash;28.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.09 (5.81\u0026ndash;24.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOvarian Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.85 (7.18\u0026ndash;18.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.32 (7.63\u0026ndash;20.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.08 (6.45\u0026ndash;18.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEsophagogastric Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.75 (6.40-20.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.22 (5.92\u0026ndash;19.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.05 (6.54\u0026ndash;20.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnal Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.96 (6.80-19.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.17 (6.80\u0026ndash;20.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.96 (6.93\u0026ndash;19.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSalivary Gland Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.40 (7.99\u0026ndash;24.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.53 (6.47\u0026ndash;23.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.77 (10.45\u0026ndash;30.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.030*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.361 (0.248, 0.479)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.279 (-0.506, -0.038)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGastrointestinal Neuroendocrine Tumor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.65 (7.25\u0026ndash;22.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.30 (7.21\u0026ndash;22.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.85 (7.26\u0026ndash;22.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Small Cell Lung Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.30 (4.90-22.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.05 (4.34\u0026ndash;22.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.40 (4.95\u0026ndash;21.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMelanoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.04 (6.21\u0026ndash;38.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.81 (4.30-20.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.00 (11.00-60.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.268 (0.216, 0.321)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.464 (-0.568, 0.358)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHepatobiliary Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.56 (5.71\u0026ndash;21.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.09 (4.57\u0026ndash;19.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.06 (6.21\u0026ndash;22.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.099\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.454 (0.398, 0.509)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.093 (-0.205, 0.019)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBladder Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.96 (4.06\u0026ndash;15.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.76 (3.94\u0026ndash;15.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.14 (4.17\u0026ndash;16.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHead and Neck Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.05 (3.31\u0026ndash;14.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.07 (3.70-15.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.00 (3.03\u0026ndash;13.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.128\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.553 (0.484, 0.619)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.106 (-0.032, 0.238)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePancreatic Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.11 (4.14\u0026ndash;17.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.94 (3.93\u0026ndash;17.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.30 (4.37\u0026ndash;17.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCervical Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.64 (5.08\u0026ndash;16.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.68 (2.69\u0026ndash;18.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.64 (6.00-16.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmall Cell Lung Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.55 (4.77\u0026ndash;14.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.42 (4.83\u0026ndash;13.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.06 (4.79\u0026ndash;15.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.114\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.439 (0.363, 0.508)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.122 (-0.274, 0.015)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGerm Cell Tumor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.41 (2.52\u0026ndash;18.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.20 (1.80\u0026ndash;9.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.91 (3.96\u0026ndash;25.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.116\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.368 (0.330, 0.406)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.265 (-0.340, 0.189)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkin Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.78 (5.50-17.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.72 (5.63\u0026ndash;21.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.89 (5.55\u0026ndash;17.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAppendiceal Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.50 (6.93\u0026ndash;18.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.84 (3.27\u0026ndash;11.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.64 (7.74\u0026ndash;18.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.058\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.279 (0.237, 0.321)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.442 (-0.527, 0.358)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eBM\u0026thinsp;=\u0026thinsp;bone Metastasis, OSM\u0026thinsp;=\u0026thinsp;other sites metastasis, PS\u0026thinsp;=\u0026thinsp;probability of superiority, CI\u0026thinsp;=\u0026thinsp;confidence interval. \u003cem\u003ep\u003c/em\u003e*: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003cb\u003ep\u003c/b\u003e: 0.05\u0026thinsp;\u0026lt;\u0026thinsp;\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.20, PS and Cliff\u0026rsquo;s δ were calculated only for cancers with \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.20.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBM vs. OSM\u003c/p\u003e \u003cp\u003eSignificant differences in survival between BM and OSM patients were observed in uterine sarcoma (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002), colorectal cancer (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010), prostate cancer (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.047), endometrial cancer (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012), salivary gland cancer (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.030), and melanoma (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;2). In addition, there were 8 other cancers that demonstrated a \u003cem\u003ep-\u003c/em\u003evalue ranging between 0.05 and 0.20. Internal survival analysis in 9 of 14 cancers showed that BM patients tended to have a shorter survival except for thyroid, colorectal, prostate, endometrial, and head and neck cancer. The absolute values of Cliff\u0026rsquo;s δ ranged from 0.033 to 0.464. The most pronounced difference was observed in melanoma (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, PS\u0026thinsp;=\u0026thinsp;0.268, |Cliff\u0026rsquo;s δ|=0.464) (Table\u0026nbsp;2).\u003c/p\u003e \u003cp\u003eCancer Types\u003c/p\u003e \u003cp\u003eSignificant survival differences were observed among BM and further only bone metastasis (OBM) patients across different cancer types (both \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The CV of BM and OBM patients ranged from 76.04% to 103.81% and 69.40% to 138.20% (Supplementary Table\u0026nbsp;1 and Table\u0026nbsp;2). All types of cancer were categorized into three groups based on median (IQR) (Table\u0026nbsp;2). The cancers with the most favorable prognosis included breast cancer (19.39, 10.13\u0026ndash;34.82), thyroid cancer (17.79, 10.77\u0026ndash;27.4), uterine sarcoma (16.59, 9.52\u0026ndash;28.16), colorectal cancer (16.45, 8.63\u0026ndash;29.52), soft tissue sarcoma (15.41, 7.85\u0026ndash;24.36), and prostate cancer (15.36, 6.70-28.81). In contrast, hepatobiliary cancer (10.09, 4.57\u0026ndash;19.70), bladder cancer (9.76, 3.94\u0026ndash;15.70), head and neck cancer (9.07, 3.70-15.69), pancreatic cancer (8.94, 3.93\u0026ndash;17.47), cervical cancer (8.68, 2.69\u0026ndash;18.12), small cell lung cancer (8.42, 4.83\u0026ndash;13.52), germ cell tumor (8.20, 1.80\u0026ndash;9.70), skin cancer (7.72, 5.63\u0026ndash;21.14), and appendiceal cancer (3.84, 3.27\u0026ndash;11.88) demonstrated the poorest survival outcomes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMetastatic Sites\u003c/p\u003e \u003cp\u003eDifferent metastatic sites were associated with significantly different survival outcomes among BM patients in different cancers (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Supplementary Table\u0026nbsp;3), such as brain metastasis in breast cancer (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), liver metastasis in prostate cancer (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), bone plus liver metastasis in colorectal cancer (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and abdominal metastasis in soft tissue sarcoma (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, no metastatic site consistently exerted a significant impact on survival across all cancers, neither as a risk nor a protective factor. Notably, as illustrated by the Kaplan\u0026ndash;Meier survival curves, when BM was analyzed as a prognostic factor, it was associated with longer survival in breast, prostate, and thyroid cancer, whereas in colorectal, hepatocellular carcinoma, soft tissue sarcoma, and uterine sarcoma, it tended to indicate shorter survival (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePathological Subtypes\u003c/p\u003e \u003cp\u003eBM patients with poorly differentiated or undifferentiated pathological subtypes demonstrated significantly shorter survival compared with other pathological subtypes in ovarian cancer (low-grade serous ovarian cancer, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.042), thyroid cancer (anaplastic thyroid carcinoma, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), pancreatic cancer (undifferentiated carcinoma of the pancreas, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), non\u0026ndash;small cell lung cancer (NSCLC) (poorly differentiated NSCLC, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), breast cancer (triple negative, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and soft tissue sarcoma (dedifferentiated liposarcoma, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Supplementary Table\u0026nbsp;4). Particularly, this difference was mostly obvious within the first 12 months. Notably, no significant survival differences were observed among the remaining differentiated pathological subtypes. Moreover, in NSCLC, patients with lung adenocarcinoma demonstrated a better survival outcome.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSex and Age\u003c/p\u003e \u003cp\u003eSex analysis was conducted among cancers with a relatively balanced sex distribution (excluding reproductive system primary cancers). The male proportion ranged from 38.5% to 81.0%. Except for NSCLC (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.028), no significant survival differences were observed between male and female patients across all the cancers (Supplementary Table\u0026nbsp;5). Age thresholds associated with significant survival differences were identified in 18 cancer types, ranging from 40 to 76 years, with 12 cancers clustering between 55 and 70 years (Supplementary Table\u0026nbsp;6). Patients older than 60 years showed significantly poorer survival (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) when a threshold of \u0026gt;\u0026thinsp;60 years was applied for all cancers (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCox Proportional Hazard Regression Model\u003c/p\u003e \u003cp\u003eThe multivariate Cox proportional hazard regression model incorporated the following covariates: primary cancer type (1, 2, and 3; Table\u0026nbsp;2), age at diagnosis, pathological subtype (poorly differentiated or undifferentiated vs. differentiated), sex, OBM, central nervous system metastasis, lung metastasis, and liver metastasis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The results indicated that the primary cancer type (2 vs. 1, HR\u0026thinsp;=\u0026thinsp;1.422, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; 3 vs. 1, HR\u0026thinsp;=\u0026thinsp;1.758, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), poorly differentiated or undifferentiated (HR\u0026thinsp;=\u0026thinsp;1.249, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), male sex (HR\u0026thinsp;=\u0026thinsp;1.086, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002), and presence of liver metastasis (HR\u0026thinsp;=\u0026thinsp;1.066, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029) were identified as independent risk factors for survival in BM (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eBM patients represent a heterogeneous and clinically important population. With the rapid advancement of comprehensive and multidisciplinary treatment strategies, accurately predicting life expectancy to align with the expanding indications for local surgical interventions has become a central challenge in optimizing the clinical management of patients with metastatic spinal tumor. In this multicenter, public database\u0026ndash;based cohort study, 13,742 metastatic patients across 25 primary cancers were included, and several potential prognostic risk factors for the survival of BM patients were identified. The prognosis of BM patients was demonstrated to be significantly different according to primary cancer types, pathological differentiation, and metastatic distribution, which stresses the necessity for cancer-specific survival analysis and predictive model construction.\u003c/p\u003e \u003cp\u003eHeterogeneity and Clinical Implications of Bone Metastasis\u003c/p\u003e \u003cp\u003eBM has historically been regarded as a negative prognostic indicator; however, our study illustrated that its impact is not uniform across different cancer types [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. In cancers with osteotropic metastasis (having an affinity for bone tissue), such as breast, prostate, thyroid, head and neck, colorectal, and endometrial cancers identified in this study, BM did not consistently shorten survival compared with OSM. In contrast, in tumors with low bone affinity and more aggressive behavior, including melanoma, hepatobiliary, and pancreatic cancers, BM was strongly correlated with worse survival. The observations are consistent with the previous research of Bollen, Luksanapruksa, and et al., in which BM occurring in breast or thyroid cancers generally indicates a more favorable prognosis than visceral metastasis [\u003cspan additionalcitationids=\"CR47 CR48\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. These results confirm that the prognostic influence of spinal metastasis varies substantially among different primary cancers, arguing against the notion that BM or visceral metastasis represents a universal high-risk factor and supporting the rationale for constructing cancer-specific prognostic models. While the findings above suggest that our initial assumption of a uniform survival distribution across metastatic cancers does not hold, it does not reject the feasibility of expanding the recruited cohort in a cancer-specific analysis. By treating BM as an independent prognostic factor, both BM and OSM patients can be integrated to develop cancer-specific survival prediction models that better capture the biological and clinical diverse characteristics of metastatic disease and reveal potential prognostic risk factors.\u003c/p\u003e \u003cp\u003ePathological Differentiation as a Novel Prognostic Risk Factor\u003c/p\u003e \u003cp\u003eThe univariate analysis and multivariate Cox regression model revealed that pathological differentiation was a cancer-specific and robust prognostic factor of survival of BM patients. Poorly differentiated or undifferentiated cancers\u0026mdash;such as triple-negative breast carcinoma, anaplastic thyroid carcinoma, and undifferentiated pancreatic carcinoma\u0026mdash;exhibited significantly shorter survival times. Similar findings were reported by few studies, which demonstrated that histologic grade cast an independent influence on survival after adjustment for age, metastatic pattern, and treatment [\u003cspan additionalcitationids=\"CR50 CR51 CR52\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. But the pathological differentiation was not widely included in existing prognostic prediction models. The underlying mechanism may be that poorly or undifferentiated metastatic cancers possess enhanced epithelial-mesenchymal transition (EMT) capability, angiogenic potential, and immune evasion, which collectively accelerate metastatic progression [\u003cspan additionalcitationids=\"CR55\" citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. These aggressive biological characteristics may enable tumor cells to invade organs or anatomic sites that are otherwise less permissive to metastasis, leading to increased tumor burden and worse outcomes [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. This phenomenon also explains the reason that no significant survival differences were observed among other histological subtypes once metastasis had occurred within a single cancer type. Therefore, incorporating the level of pathological differentiation into future prognostic models may substantially improve predictive accuracy of survival estimation in clinical management of BM patients.\u003c/p\u003e \u003cp\u003eCancer-specific Molecular Targeted Therapy\u003c/p\u003e \u003cp\u003eThe improved survival observed in lung adenocarcinoma compared with squamous or small-cell carcinoma might be attributed to the wider application of molecular targeted therapy such as epidermal growth factor receptor (EGFR) inhibitors in patients with lung adenocarcinoma compared with other NSCLC subtypes [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Therefore, recruiting patients who have received molecular targeted therapy with clear survival outcomes for life expectancy prediction model construction will become increasingly important. This approach was currently limited by the small number of eligible patients. However, as discussed above, expanding the analytical cohort in a cancer-specific manner is making this strategy feasible. Based on such a framework, future models could categorize patients into distinct pathological subtypes characterized by different molecular targets, such as EGFR-mutated lung adenocarcinoma and HER2-positive breast cancer, each demonstrating unique survival characteristics associated with cancer-specific molecular biomarkers [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. By incorporating molecularly defined patient subgroups and their various therapeutic impacts on survival outcome, the corresponding cancer-specific predictive models are expected to provide more precise life expectancy estimation and enhanced biologic interpretability for clinical decision-making in BM patients.\u003c/p\u003e \u003cp\u003ePrimary Cancer Type, Age, and Sex\u003c/p\u003e \u003cp\u003eConsistent with previous studies, primary cancer type emerged as the most powerful determinant of survival [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. In this study, different primary cancers were stratified into three prognostic tiers, yielding a survival hierarchy that closely parallels the scoring system developed by Tokuhashi, Katagiri, and et al., but greatly expands coverage of cancer types and comprehensively reflects contemporary treatment paradigms [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The correlation between age above 60 years and shorter survival is likely to be explained by poorer baseline functional status, treatment tolerance, age-related immune decline, and greater comorbidity burden [\u003cspan additionalcitationids=\"CR63 CR64\" citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. The age threshold across multiple cancers is consistent with previous studies and suggests that it represents a clinically reasonable prognostic factor for risk stratification and surgical decision-making [\u003cspan additionalcitationids=\"CR67\" citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. In contrast, sex did not remain a significant prognostic variable after adjustment for primary cancer type, indicating that previously reported sex disparities in survival may have been confounded by the unbalanced distribution of sex-specific cancer and may not be widely adopted for BM patient prognosis prediction [\u003cspan additionalcitationids=\"CR70 CR71 CR72\" citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. This finding demonstrates that demographic variables may exert only secondary prognostic influence once primary cancer and pathological type are determined.\u003c/p\u003e \u003cp\u003eMetastatic Distribution and Organ-Specific Effects\u003c/p\u003e \u003cp\u003eNo metastatic site was proved to be a universal risk factor across all cancers in this study, which stands in contrast to previous studies that broadly applied visceral or brain metastasis as adverse prognostic indicators [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Although specific sites may generate similar negative impacts on certain cancers (e.g., liver metastasis in colorectal and prostate cancer, brain metastasis in breast cancer and NSCLC, and el at.), these effects were inconsistent across other cancers. This heterogeneity has been sporadically reported in recent studies and further confirmed in our study [\u003cspan additionalcitationids=\"CR75 CR76\" citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. Notably, OBM was associated with relatively favorable outcomes in osteotropic cancers, including breast, prostate, and thyroid cancer as previously discussed. This finding reflects the heterogeneous affinity to bone tissue among different primary cancers, which supports the argument that BM should be regarded as an independent prognostic factor in assessing metastatic cancers [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. The mechanisms of osteotropic phenomenon are thought to be regulated by complicated molecular pathways, including chemokine (C-X-C motif) receptor 4 (CXCR4), integrins, and extracellular vesicle-mediated premetastatic niche formation, which vary substantially across different tumors [\u003cspan additionalcitationids=\"CR80\" citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]. The lack of a clear consensus further emphasizes the necessity for sophisticated, cancer-specific analyses rather than indiscriminate pooled analyses, which may bring significant selection bias due to the unbalanced distribution of different cancer populations, and ultimately reduce predictive model generalizability.\u003c/p\u003e \u003cp\u003eLimitation\u003c/p\u003e \u003cp\u003eThis study incorporated a large multicenter cohort of metastatic cancer patients, utilizing survival data collected over the past decade. The data preprocessing protocol, involving the standardized data curation, imputation of missing data, and bootstrap validation method, was conducted according to recently published guidelines or consensuses for prognostic model development [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The univariate and multivariate survival analysis also adhered to the latest statistical framework, as well as referencing the previous prognostic prediction model [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNonetheless, several limitations should be seriously acknowledged. First, in approximately 50% of the included primary cancers, the number of BM patients was fewer than 100, and the heterogeneity in study design among contributing centers may limit the overall generalizability of the findings. Second, the missing or insufficient results of the immunohistochemical, hematologic, and radiologic examinations, as well as the baseline neurological or general function evaluations, including complete blood counts, serum biochemical testing, CT-based bone stability evaluation, Frankel grading, and Eastern Cooperative Oncology Group (ECOG) performance score, greatly restricted the integration of existing variables into the subsequent life expectancy prediction model. This represents an important limitation of the present study, which we aim to address in the future by including data from our institutional patient cohort. Furthermore, when BM was involved, the specific skeletal sites in most patients are recorded simply as \u0026ldquo;bone\u0026rdquo; rather than being accurately described as particular locations (e.g., spine, rib, skull, or extremity). Although epidemiological patterns indicate that most bone metastases are observed in the spine, the deficient anatomical description in this study still introduces challenges in identifying prognostic risk factors associated with different skeletal sites and deriving more targeted, site-specific conclusion [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Finally, treatment-related information was incomplete for most patients, particularly regarding the use of molecular targeted therapy, chemotherapy drug regimen, surgical procedure and margin, and radiation technique. The lack of standardized treatment data prevented the incorporation of these potentially protective, cancer-specific therapeutic variables into the prognostic prediction model, which is a limitation commonly shared by many large-scale registry-based studies [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan additionalcitationids=\"CR85 CR86 CR87\" citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e]. Our future work will focus on integrating our grade A tertiary hospital data with complete examination results and treatment responses to develop more refined, cancer-specific prognostic prediction models for surgical decision-making in BM patients.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eBM has an independent, substantial influence on survival across different metastatic cancers, indicating that BM patients represent a distinct clinical subgroup with great heterogeneity. Primary cancer type, pathological subtypes with poor differentiation, and age at diagnosis above 60 may further significantly influence the prognosis of patients with spinal metastasis. No single metastatic site served as a universal prognostic factor across different cancer types. BM tended to indicate a favorable outcome in cancers with osteotropic characteristics. The emergence of distinct metastatic sites exerted heterogeneous impacts across cancers, differing from the conventional assumption. This study suggests that expanding the recruited population for life expectancy prediction model construction within a cancer-specific framework is feasible and clinically meaningful. It is necessary to analyze the metastatic patterns within each cancer and include more cancer-specific prognostic risk factors, such as pathological subtypes, when constructing predictive models. In the future, developing prognostic models stratified by primary cancer type may represent a promising direction to improve model generalizability and provide precise survival estimation to guide surgical decision-making.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAJCC: American Joint Committee on Cancer; BM: Bone Metastasis; BMI: Body Mass Index; CI: Confidence Interval; CV: Coefficient of Variation; EGFR: Epidermal Growth Factor Receptor; EMT: Epithelial-Mesenchymal Transition; ECOG: Eastern Cooperative Oncology Group; HF: Hazard Ratio; IHC: Immunohistochemistry; IQR: Interquartile Range; K\u0026ndash;S: Kolmogorov\u0026ndash;Smirnov; MAXSTAT: Maximally Selected Rank Statistics; NSCLC: Non\u0026ndash;Small Cell Lung Cancer; OBM: Only Bone Metastasis; OS: Overall Survival; OSM: Other Sites of Metastasis; PH: Proportional Hazards; PS: Probability of Superiority; RECORD: Reporting of Studies Conducted using Observational Routinely-Collected Health Data; STROBE: Strengthening the Reporting of Observational Studies in Epidemiology; TNM: Tumor-Node-Metastasis; VIF: Variance Inflation Factor; WFNS: World Federation of Neurosurgical Societies.\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThe study is retrospective and registry-based without clinical or experimental intervention, and the data used were collected from a public database. For these reasons, informed consent was applied for exemption. The study was conducted with the support of Peking University Third Hospital. Ethical approval was granted by the Research Ethics Committee (approval number: LM2025366)\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe datasets supporting the conclusions of this study are available from the corresponding author on reasonable request or can be accessed from the cBioPortal for Cancer Genomics (https://www.cbioportal.org/).\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Peking University Third Hospital (Grant No. BYSYZD2023017) and the National Natural Science Foundation of China (Grant Nos. 82201644 and 82471505). The funding bodies had no role in the design of the study, data collection, analysis, interpretation of data, or in writing the manuscript.\u003c/p\u003e\n\u003cp\u003eAuthors’ contributions\u003c/p\u003e\n\u003cp\u003eZ.L. Yun, Y.C. Tang, and J. Sun conceived and designed the study. Z.L. Yun, J.C. Lei, and G.Q. Zhang collected the data. Z.L. Yun and Y.C. Tang performed the statistical analysis. Z.L. Yun drafted the manuscript. F. Wei and X.G. Liu supervised the study and were responsible for manuscript revision and correspondence. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eWe sincerely thank Prof. Wei and Prof. Liu for their valuable guidance on integrating this project with clinical practice. The authors gratefully acknowledge Peking University Third Hospital and the National Natural Science Foundation of China for hardware support in data processing and financial funding for the overall implementation of this project.\u003c/p\u003e\n\u003cp\u003eAuthors’ information\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eClinical trial number\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChallapalli, A. et al. Spine and Non-spine Bone Metastases - Current Controversies and Future Direction. \u003cem\u003eClin. Oncol. (R Coll. 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A machine learning-Based model to predict early death among bone metastatic breast cancer patients: A large cohort of 16,189 patients. \u003cem\u003eFront. Cell. Dev. Biol.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e, 1059597. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.3389/fcell.2022.1059597\u003c/span\u003e\u003cspan address=\"https://doi.org:10.3389/fcell.2022.1059597\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Bone Metastasis, Registry-Based, Prognosis, Cancer-Specific, Pathological Differentiation","lastPublishedDoi":"10.21203/rs.3.rs-8384769/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8384769/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAccurate survival prediction for patients with bone metastatic cancer remains challenging. Existing prognostic models frequently show poor external validity, primarily due to small sample sizes, single-center designs, and insufficient inclusion of pathological and molecular variables. Moreover, few studies have concentrated on the prognostic heterogeneity of bone metastasis (BM) across different cancers using large, standardized datasets within a cancer-specific manner. This retrospective, multicenter, registry-based cohort study was conducted to evaluate the prognostic significance of BM across multiple cancer types and to identify cancer-specific clinical factors associated with survival.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eBaseline demographic and clinical characteristics of 13,742 patients with AJCC stage IV or TNM stage M1 metastatic cancer diagnosis were collected across 42 clinical studies registered in the cBioPortal for Cancer Genomics database. Overall survival (OS) following metastatic diagnosis was set as the primary outcome. Univariate analyses were conducted to identify potential prognostic risk factors mainly using the Kaplan\u0026ndash;Meier, log-rank test, and non-parametric tests. Variables with p\u0026thinsp;\u0026lt;\u0026thinsp;0.20 were included in multivariate Cox proportional hazards models for further validation. Multiple imputation and bootstrap were applied for the missing value process and validation.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eBM was associated with favorable outcomes compared with other metastatic sites in osteotropic cancers such as breast, prostate, and thyroid cancer, whereas it indicated a worse prognosis in hepatobiliary, uterine sarcoma, and colorectal cancer with low affinity to skeletal tissue. Among prognostic variables, no single metastatic site served as a universal adverse prognostic factor across all cancers. Poorly differentiated or undifferentiated histology independently correlated with reduced survival (HR\u0026thinsp;=\u0026thinsp;1.249, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Age above 60 years was also associated with inferior survival (univariate analysis, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while the primary cancer type remained the most influential prognostic determinant (HR\u0026thinsp;=\u0026thinsp;1.422\u0026ndash;1.758, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eBM demonstrates cancer-specific and heterogeneous influences on survival. Population for survival prediction in traditional studies could be expanded within a cancer-specific framework. Among the included prognostic variables, primary cancer type, pathological differentiation, and age stratify outcomes significantly, highlighting the demand for pathology-integrated, cancer-specific prognostic models. Incorporation of standardized treatment and molecular variables is essential for improving model precision and clinical applicability in the future.\u003c/p\u003e","manuscriptTitle":"Prognostic Risk Factors for Cancer-Specific Bone Metastasis: A Registry-Based Analysis of 13,742 Patients ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-01 05:41:24","doi":"10.21203/rs.3.rs-8384769/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-29T06:15:43+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-28T06:34:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"290404835752762988236670085884141679915","date":"2026-01-18T22:33:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-10T01:26:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"142656926242100229888754050796494117656","date":"2026-01-09T11:54:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-31T22:18:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"212840818329416721083324724635048674284","date":"2025-12-31T21:52:57+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-29T16:25:03+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-23T16:15:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-18T11:23:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-18T11:19:24+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-12-17T10:39:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"11979787-93d6-4306-8caf-e67beaea5d4e","owner":[],"postedDate":"January 1st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":60367859,"name":"Health sciences/Biomarkers"},{"id":60367860,"name":"Biological sciences/Cancer"},{"id":60367861,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2026-03-23T16:01:26+00:00","versionOfRecord":{"articleIdentity":"rs-8384769","link":"https://doi.org/10.1038/s41598-026-43780-6","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-03-19 15:57:49","publishedOnDateReadable":"March 19th, 2026"},"versionCreatedAt":"2026-01-01 05:41:24","video":"","vorDoi":"10.1038/s41598-026-43780-6","vorDoiUrl":"https://doi.org/10.1038/s41598-026-43780-6","workflowStages":[]},"version":"v1","identity":"rs-8384769","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8384769","identity":"rs-8384769","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}