Risk Factor Analysis of Pulmonary Metastasis in Middle-Aged and Elderly Patients with Chondrosarcoma and Establishment and Validation of a Nomogram

Risk Factor Analysis of Pulmonary Metastasis in Middle-Aged and Elderly Patients with Chondrosarcoma and Establishment and Validation of a Nomogram | 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 Risk Factor Analysis of Pulmonary Metastasis in Middle-Aged and Elderly Patients with Chondrosarcoma and Establishment and Validation of a Nomogram yunkun wang, mingxin liao, fengchun wu, qiaoli zhang, tianfeng li, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7799026/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background and Objectives Pulmonary metastasis in middle-aged and elderly patients with chondrosarcoma often leads to poor prognosis. This study aimed to identify the independent risk factors for pulmonary metastasis in this population and to develop and validate a clinical prediction model (nomogram) for accurately estimating the probability of pulmonary metastasis. Methods A total of 659 eligible chondrosarcoma patients (aged 40 years or older) were identified retrospectively from the Surveillance, Epidemiology, and End Results (SEER) database, covering the period from 2004 to 2015. Univariate logistic regression, least absolute shrinkage and selection operator (LASSO) regression, and multivariate logistic regression were used to identify the risk factors for pulmonary metastasis. The selected risk factors, together with their respective weights, were visually represented in a nomogram. The predictive performance and clinical utility of the nomogram were evaluated using the receiver operating characteristic (ROC) curve, calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC). Results Tumor grade, T stage, N stage, and surgical status were identified as independent risk factors for pulmonary metastasis. The area under the ROC curve (AUC) was 0.914 for the training cohort and 0.849 for the validation cohort. The calibration curve demonstrated good agreement between the model’s predicted probabilities and observed outcomes, while the DCA and CIC confirmed the nomogram’s significant clinical value. Conclusion Tumor grade, T stage, N stage, and surgical status are important independent risk factors influencing pulmonary metastasis in middle-aged and elderly patients with chondrosarcoma. The nomogram constructed in this study provides clinicians with a rapid, user-friendly tool for predicting the probability of pulmonary metastasis in this patient population, and its accuracy and clinical applicability have been validated. Biological sciences/Cancer Health sciences/Diseases Health sciences/Medical research Health sciences/Oncology Health sciences/Risk factors Chondrosarcoma Pulmonary metastasis Nomogram Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Chondrosarcoma (CS) is currently the second most common primary malignant bone tumor, second only to osteosarcoma, accounting for approximately 20%–30% of all malignant bone tumors( 1 , 2 ). Unlike the other two primary bone sarcomas (osteosarcoma and Ewing’s sarcoma), CS primarily affects adults( 3 ), with over 70% of cases diagnosed in patients aged 40 years or older. Similar to other malignant bone tumors, CS mainly spreads via hematogenous dissemination. The development of pulmonary metastasis severely compromises patient prognosis and substantially shortens overall survival. Owing to reduced metabolism, impaired immunity, and increased tumor invasiveness in middle-aged and elderly patients( 4 ), their risk of pulmonary metastasis is substantially higher than that in younger individuals. Therefore, the early identification of middle-aged and elderly patients with chondrosarcoma who are at high risk of lung metastasis, along with more active monitoring and timely intervention, is essential to improving their survival outcomes. Despite clinical recognition of the adverse impact of pulmonary metastasis in chondrosarcoma, there remains a lack of systematic, targeted and in-depth studies investigating the specific risk factors for metastasis in this particular demographic. As an intuitive visual prediction tool, the nomogram quantifies and integrates complex risk factors based on results from multivariate regression models into a single graphical interface. This enables clinicians to perform rapid and intuitive probability assessments of specific outcomes—such as disease recurrence, metastasis, or survival rates—for individual patients( 5 – 7 ). In recent years, nomograms have demonstrated considerable utility in risk and prognosis prediction across various types of cancer( 5 , 6 , 8 , 9 ). Therefore, this study aims to focus specifically on middle-aged and elderly patients with chondrosarcoma. Through a retrospective analysis of a relatively large clinical sample, we will systematically explore independent risk factors influencing the development of pulmonary metastasis in this population ,and construct a clinically practical nomogram prediction model to quantitatively assess the risk probability of pulmonary metastasis in individual middle-aged and elderly chondrosarcoma patients. Meanwhile, to ensure the reliability and generalizability of the model, this study will also employ internal validation to rigorously evaluate the constructed nomogram. We anticipate that the establishment and validation of this model will provide clinicians with an objective basis for the early identification of middle-aged and elderly chondrosarcoma patients at high risk of pulmonary metastasis, thereby facilitating the development of more personalized follow-up strategies and potential proactive interventions, and ultimately optimizing treatment decisions and long-term survival management for this population. Materials and Methods Patient Cohort Patient data were obtained from the Surveillance, Epidemiology, and End Results (SEER) database, which consists of 18 cancer registries and covers approximately 30% of the total U.S. population( 10 ). Data extraction was performed using SEER*Stat software( 11 ). The SEER database includes two types of data: one derived from autopsy reports and the other from patients with complete follow-up. However, we excluded data obtained from autopsy reports and only included information from patients with complete follow-up. The inclusion criteria were as follows: ( 1 ) patients aged 40 years or older at diagnosis; ( 2 ) diagnosis between 2004 and 2015 to ensure sufficient follow-up time; ( 3 ) diagnosis of primary chondrosarcoma; ( 4 ) diagnosis obtained from living patients rather than from death certificates or autopsies; ( 5 ) site confined to bone, excluding soft tissue chondrosarcoma; and ( 6 ) complete follow-up without missing data. Variable Selection The variables analyzed to identify risk factors for pulmonary metastasis in CS patients included: age, gender, race, marital status, primary tumor site, tumor grade, histological type, T stage, N stage, tumor laterality, surgical status, lymph node dissection status, radiotherapy, radiotherapy sequence, chemotherapy, time to treatment, and tumor size. Statistical Analysis The collected data involving categorical variables were processed, and the dataset (n = 659) was randomly allocated into a training cohort (n = 462) and a validation cohort (n = 197). Chi-square tests were used to compare baseline clinicopathological characteristics between the two groups. Univariate logistic regression and LASSO regression were performed on the training set to preliminarily identify the most significant predictive features, while ensuring that the multivariate model would not be overfitted. Multivariate logistic regression was subsequently applied to determine the final independent risk factors for predicting pulmonary metastasis. Based on the final selected variables, a nomogram for predicting pulmonary metastasis was constructed and internally validated using the validation cohort. The discriminative ability of the nomogram was evaluated using the ROC curve and AUC value. The calibration curve was used to assess agreement between the nomogram’s predicted probabilities and actual observed outcomes. DCA and CIC were employed to evaluate the clinical utility of the prediction model. All statistical analyses and graph generation were performed using R software (version 4.3.3) and RStudio software. A two-tailed p-value < 0.05 was considered statistically significant. Results Patient Baseline Characteristics A total of 659 patients were selected from the SEER database according to the inclusion criteria. To simplify statistical analysis, age was converted from a continuous variable into a categorical variable, with patients aged 40 to 79 grouped by decade and those over 80 classified into a single category. Histological subtypes were broadly classified based on SEER codes into conventional chondrosarcoma (ICD-O-3 code: 9220/3) and special subtypes of chondrosarcoma, which included dedifferentiated chondrosarcoma (9243/3), mesenchymal chondrosarcoma (9240/3), clear cell chondrosarcoma (9242/3), myxoid chondrosarcoma (9231/3), and periosteal chondrosarcoma (9221/3). Tumor size was categorized as small (1–50 mm), medium (50–80 mm), or large (> 80 mm). Based on the presence or absence of pulmonary metastasis, the baseline data were split into a training cohort (n = 462) and a validation cohort (n = 197) at a ratio of 7:3. A chi-square test was used to compare differences between the two cohorts, and the results indicated successful grouping (p > 0.05) (Table 1 ). Independent risk factors for pulmonary metastasis The univariate logistic regression analysis (Table 2 ) revealed that the risk factors for pulmonary metastasis included age, marital status, tumor grade, histological type, T stage, N stage, surgery, chemotherapy, time to treatment, and tumor size. The LASSO regression analysis (Fig. 1 ) identified the following risk factors for pulmonary metastasis: age, marital status, race, primary site, tumor grade, histological type, laterality, T stage, N stage, surgery, radiotherapy, and chemotherapy. By integrating the statistical significance from the univariate logistic regression and the dimensionality reduction advantage of LASSO regression, we included age, marital status, tumor grade, histological type, T stage, N stage, and chemotherapy in the multivariate logistic regression analysis. The final results (Fig. 2 ) demonstrated that tumor grade, T stage, N stage, and surgery were significant risk factors associated with pulmonary metastasis. Table 2 Results of the univariate logistic regression analysis Variable OR CI_low CI_high P_value Age 1.327 1.006 1.757 0.045 Sex 1.197 0.609 2.415 0.606 Marriage 0.355 0.131 0.813 0.024 Race 1.029 0.465 1.870 0.935 Primary site 1.752 0.893 3.502 0.105 Grade 3.040 2.132 4.445 <0.001 Histological type 3.556 1.750 7.116 <0.001 Laterality 1.310 0.825 2.132 0.262 T 17.410 7.514 50.672 <0.001 N 17.051 3.618 89.628 <0.001 Surgical conditions 0.415 0.259 0.649 <0.001 Lymph dissection 0.645 0.116 1.792 0.501 Radiation 1.275 0.463 2.999 0.604 Radiation sequence 0.577 0.183 1.178 0.217 Chemotherapy 11.150 5.005 24.598 <0.001 Time to treatment 1.397 1.027 1.917 0.035 Tumor size 3.482 2.063 6.544 <0.001 CI, confidence interval; OR, odds ratio. ( p-value < 0.05 was considered statistically significant). Nomogram Results for Pulmonary Metastasis Based on the identified variables, we constructed and validated a nomogram (Fig. 3 ). For example, consider a middle-aged or elderly patient diagnosed with chondrosarcoma, with a tumor grade of 3, T stage T2, N stage N0, and having undergone partial resection of the tumor. To use the nomogram, a vertical line is drawn from each predictor axis to the points axis to obtain the corresponding score. After summing all individual scores, a total of approximately 122 points corresponds to an estimated probability of pulmonary metastasis of around 30%. Nomogram Performance and Clinical Utility This study comprehensively evaluated the performance of the nomogram model—constructed based on tumor grade, T stage, N stage, and surgical status—through a systematic multi-dimensional validation framework. The ROC curve (Fig. 4 ) demonstrated excellent discriminatory ability of the model in the training cohort (AUC = 0.914), indicating high accuracy in identifying high-risk individuals for pulmonary metastasis. The model also maintained strong reliability in the independent validation cohort (AUC = 0.849), exceeding the conventional diagnostic threshold (AUC above 0.7) and confirming its robust generalizability. The calibration curve (Fig. 5 ), which compares predicted probabilities with observed outcomes, showed close agreement between model predictions and actual results. The calibration curve nearly coincided with the ideal diagonal in the training cohort, and although minor fluctuations occurred in the validation cohort due to its smaller sample size, overall consistency remained good, supporting the clinical credibility of the predicted probabilities. Clinical utility was further reinforced using decision analysis tools. Decision curve analysis (Fig. 6) indicated that applying this nomogram to guide clinical decisions yielded significant net clinical benefits across a wide threshold probability range of 0.1 to 0.8. Additionally, the clinical impact curve (Fig. 7) revealed that among patients identified as high-risk by the model, over 80% were true positives (more than 85% in the training cohort), demonstrating its effectiveness in accurately targeting individuals requiring intervention. This facilitates optimized allocation of medical resources and helps avoid unnecessary screening for low-risk patients. Discussion Chondrosarcoma is a common malignant bone tumor, and pulmonary metastasis is a critical factor affecting patient prognosis( 12 ). Currently, there is extensive research on chondrosarcoma at the molecular level. For example, aspartate β-hydroxylase (ASPH) has been identified as a biomarker for chondrosarcoma. Sun et al. found that a higher ASPH expression score may be associated with metastatic risk and prognosis, suggesting its potential as a therapeutic target( 13 ). Nemecek et al. conducted a retrospective analysis of 33 patients with dedifferentiated chondrosarcoma and proposed that C-reactive protein (CRP) serves as an independent prognostic predictor for these patients, making it a potential clinical indicator( 14 ). Studies by Yang et al. indicated that PD-L1 and PD-L2 play roles in the clinical progression of chondrosarcoma( 15 ), and blocking the PD-1/PD-L1 signaling pathway can activate T-cell-mediated anti-tumor immune responses, thereby inhibiting tumor growth and metastasis( 16 , 17 ). Isocitrate dehydrogenase 1/2 (IDH1/2), metabolic enzymes frequently mutated in various tumors, may influence the development and prognosis of chondrosarcoma( 18 – 20 ). Researchers such as Lugowska suggest that IDH1/2 mutations are important predictors of prognosis in chondrosarcoma( 21 ). While molecular studies provide valuable insights into the pathological mechanisms and potential treatment strategies of chondrosarcoma, translating these findings into clinical applications remains challenging. Therefore, research on clinical characteristics remains essential. Studies focusing on clinical manifestations—such as age, gender, ethnicity, tumor location, histological type, stage, and treatment modalities—can elucidate the clinical features and prognostic factors of chondrosarcoma, offering crucial evidence to support clinical decision-making. This study represents the first systematic exploration of risk factors for pulmonary metastasis specifically in middle-aged and elderly patients with chondrosarcoma. We successfully developed and validated a nomogram prediction model utilizing large-sample data from the SEER database. Through rigorous univariate, LASSO, and multivariate logistic regression analyses, we identified tumor grade, T stage, N stage, and surgical status as independent risk factors for pulmonary metastasis in this patient population. Based on these key factors, we constructed an intuitive and clinically applicable nomogram. The model demonstrated excellent discriminatory ability in both the training and validation cohorts, with AUC values of 0.914 and 0.849, respectively, indicating its effectiveness in distinguishing between high- and low-risk patients for pulmonary metastasis. The calibration curve showed strong agreement between predicted probabilities and actual observations. DCA and CIC confirmed that the nomogram provides significant net clinical benefit and practical utility. Specifically, across a broad threshold probability range of 0.1 to 0.8, using this model to guide clinical decisions—such as determining whether more intensive pulmonary imaging surveillance is needed—yielded higher net benefit compared to strategies of “intervening for all patients” or “intervening for no patients,” underscoring its clear clinical application value. While pulmonary metastasis is typically detected via chest CT scans, previous studies have reported that up to 28–32% of metastases may be missed by CT imaging( 22 , 23 ). Therefore, our prediction model demonstrates considerable clinical utility by facilitating early detection of metastatic disease and enabling timely, targeted interventions. The findings of this study are largely consistent with existing literature on risk factors for metastasis in chondrosarcoma. Previous studies have indicated that higher tumor grade is associated with an increased risk of distant metastasis. High-grade chondrosarcomas, due to their inherently aggressive phenotype—characterized by marked cellular atypia and active mitotic activity—are more likely to breach local tissue boundaries, thereby driving distant dissemination of tumor cells( 24 , 25 ). This invasive behavior is closely linked to complex interactions within the tumor microenvironment, including alterations in the extracellular matrix and suppression of immune responses( 26 , 27 ). Recent research has shown that interactions between tumor cells and the surrounding stroma not only influence tumor growth but also significantly affect metastatic potential( 27 ). High-grade chondrosarcomas often exhibit abnormal degradation of the extracellular matrix, which provides a physical pathway for tumor invasion and metastasis. Specifically, tumor cells secrete enzymes such as matrix metalloproteinases (MMPs) to break down the protective barriers of the extracellular matrix, facilitating their release from the primary site and entry into the bloodstream( 28 ). Concurrently, enhanced vascular invasion capability allows tumor cells to disseminate more readily through blood vessels to distant organs such as the lungs, forming metastatic lesions( 27 ). Furthermore, the immune microenvironment plays a crucial role in the metastatic process of chondrosarcoma. Tumor cells can evade immune surveillance and attack through multiple mechanisms, such as expressing immune checkpoint molecules like PD-L1 to inhibit T-cell activity( 29 – 31 ). The formation of an immunosuppressive microenvironment further promotes tumor cell survival and growth, accelerating the development of pulmonary metastases. Emerging evidence also suggests that tumor-associated collagen signatures are closely related to metastatic potential( 24 ). For example, in lung cancer, the alignment of collagen fibers is strongly correlated with the invasive capacity of tumor cells. A collagen structure with high fiber length and low density may promote the infiltration of immune cells, thereby influencing responses to immunotherapy( 24 , 29 , 31 ). As a cornerstone in the prognostic assessment of chondrosarcoma, tumor grade—and its critical role in predicting pulmonary metastasis—has been confirmed in multiple studies and is further emphasized in the present research. T stage is a critical factor in the prognosis and treatment decision-making for chondrosarcoma. Particular attention should be paid to its association with the risk of pulmonary metastasis when the tumor progresses to T3 stage, characterized by breakthrough beyond the cortex and invasion into adjacent structures(32,33) .This correlation is closely linked to the biological behavior of chondrosarcoma, as the development of pulmonary metastasis typically follows a multi-step process: initial local invasion, followed by dissemination of tumor cells through the circulatory system, and eventual colonization in distant organs such as the lungs( 34 – 36 ).In the early stages of chondrosarcoma (T1–T2), the tumor is generally confined within the bone. The surrounding cortical bone and soft tissues serve as a natural barrier, effectively preventing tumor cells from entering the bloodstream( 34 , 36 ). However, once the tumor advances to T3 stage and breaches the cortical barrier, tumor cells gain direct access to adjacent blood vessels (such as the nutrient artery of the bone) or lymphatic channels( 35 ). This invasive behavior provides a direct pathway for tumor cells to enter the circulation, thereby increasing the risk of distant metastasis. After entering the bloodstream, tumor cells can migrate via blood flow to distant organs including the lungs, where they may colonize and form metastatic lesions under permissive microenvironmental conditions( 37 ). Therefore, the T stage not only reflects the extent of local progression but, more importantly, indicates the potential risk of systemic spread( 38 ).The T3 stage can be regarded as an “early warning,” signaling clinicians to conduct more comprehensive evaluation and implement aggressive interventions. This implies that patients diagnosed with T3 chondrosarcoma require intensified examination strategies, such as dynamic contrast-enhanced MRI to assess the extent of local invasion, and chest CT scans to detect potential pulmonary metastases at the earliest possible stage( 39 , 40 ). In certain cases, whole-body PET-CT may also be valuable for identifying occult distant metastases( 41 ). N stage, particularly N1 (indicating regional lymph node metastasis), plays a significant role in pulmonary metastasis of chondrosarcoma( 42 ). Although conventional belief holds that lymph node metastasis is rare in chondrosarcoma, relevant studies have shown that the proportion of patients with N1 stage who develop pulmonary metastasis is considerably higher than that of N0 patients( 39 ), highlighting the importance of lymph node involvement in the metastatic process. Pulmonary metastasis is the most common form of distant spread in chondrosarcoma and represents one of the leading causes of mortality. While hematogenous spread is considered the primary route for pulmonary metastasis, lymph node metastasis may serve as a bridging mechanism facilitating tumor cell entry into the bloodstream( 43 ). Specifically, tumor cells may initially spread via lymphatic vessels to regional lymph nodes, proliferate within the nodes, and eventually break through the nodal capsule into the circulation, leading to metastasis in distant organs such as the lungs. Furthermore, as lymph nodes are crucial components of the immune system, the growth and proliferation of tumor cells within them may disrupt immune function, enabling tumor cells to evade immune surveillance and thereby promoting metastasis( 43 , 44 ).In conclusion, N stage is an important factor in determining metastatic spread in chondrosarcoma. Future research should integrate N stage with other clinicopathological factors to develop more accurate predictive models, assisting clinicians in better evaluating individual patient risk and formulating personalized treatment strategies. Since chondrosarcoma is generally resistant to chemotherapy and radiotherapy, surgical resection remains the primary treatment modality( 45 – 49 ). Studies have shown a close relationship between tumor metastasis and surgical margins, with more extensive resection associated with a reduced risk of pulmonary metastasis, underscoring the critical impact of surgical quality on both metastasis and prognosis( 2 , 50 ). Wide resection can decrease the risk of metastasis, whereas incomplete removal or residual tumor after surgery may increase the likelihood of recurrence and metastasis, highlighting the importance of achieving thorough local tumor control( 51 – 54 ). Although extended resection helps reduce the risk of pulmonary metastasis, an overly aggressive surgical approach may adversely affect the patient’s quality of life. Wide resections can lead to impaired limb function, reduced mobility, and amputation may cause significant psychological and social challenges. Therefore, when planning surgical intervention, it is essential to balance tumor control with the preservation of the patient’s functional and life quality. The nomogram developed in this study provides clinicians with a practical tool for quantitatively assessing the risk of pulmonary metastasis in middle-aged and elderly patients with chondrosarcoma. It facilitates the early identification of high-risk patients, prompting healthcare providers to establish more intensive and sensitive surveillance protocols. Such protocols may include shortened intervals for CT reevaluation, or the use of high-resolution CT or PET-CT. Furthermore, the tool offers an objective basis for risk assessment when formulating initial treatment plans, particularly surgical strategies, and supports considerations regarding adjuvant therapies. Although current options show limited efficacy, high-risk patients may be considered for enrollment in clinical trials of novel therapies. Additionally, the model supports clear and intuitive communication with patients and their families regarding the risk of disease progression. Limitations and Prospects However, our study has several limitations. First, as a retrospective study, it may carry potential risks of bias. The data were primarily derived from the U.S. population, which may not fully represent patient profiles in other countries or regions. Second, the nomogram was validated only internally, which could lead to model overfitting. External validation would improve its reliability. Third, the grading and staging systems in the SEER database may be outdated, which could introduce discrepancies when comparing our findings with newer studies. Additionally, molecular markers, genetic and epigenetic factors were not available in the SEER database and thus were not included in this study. Future research could build upon the framework of this model by incorporating additional patient characteristics and molecular biomarkers to further enhance its predictive accuracy and generalizability. Furthermore, expanding the model’s application may help optimize early diagnosis and intervention strategies for pulmonary metastasis associated with other malignancies. Through subsequent research and external validation, this predictive model has the potential to become an integral part of clinical decision-support tools, significantly improving treatment outcomes and quality of life for patients with chondrosarcoma. Conclusion In summary, tumor grade, T stage, N stage, and surgical status were identified as risk factors for pulmonary metastasis in chondrosarcoma patients. This study developed and validated a nomogram for predicting the probability of pulmonary metastasis in middle-aged and elderly chondrosarcoma patients, providing clinicians with a rapid and user-friendly assessment tool. This nomogram may guide surgeons and oncologists in optimizing individualized treatment strategies and facilitating improved clinical decision-making. Declarations Funding statement Joint Funding Program for Science and Technology Innovation in Healthcare（Grant number：N2024LH016） Joint Funding Program for Science and Technology Innovation in Healthcare（Grant number：N2024LH010） Supported by Fujian Provincial Natural Science Foundation of China（Grant number：2024J011595） Data availability The SEER datasets used during the current study can be found here: https:// seer. cancer. gov/. The ZJCH datasets are available from the corresponding author on reasonable request. Conflict of Interest The authors declare that they have no conflict of interest. Acknowledgements: The authors are grateful to all the staff in the National Cancer Institute (USA) for their contribution to the SEER program. Ethical review statement As this study is based on a publicly available database, it is exempt from ethical review requirements. References Landuzzi, L., Ruzzi, F., Lollini, P-L. & Scotlandi, K. Chondrosarcoma: new molecular insights, challenges in near-patient preclinical modeling, and therapeutic approaches. Int. J. Mol. Sci. 26 , 1542. 10.3390/ijms26041542 (2025). Chen, Y-C. et al. Prognostic factors and survival in conventional chondrosarcoma: a single institution review. J. Chin. Med. Assoc. JCMA . 83 , 669–673. 10.1097/JCMA.0000000000000315 (2020). Li, Z., Liu, W., Hao, L., Zhang, Q. & Niu, X. 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Case Rep. 111 , 108773. 10.1016/j.ijscr.2023.108773 (2023). van Maldegem, A. M., Bovée, J. V. & Gelderblom, H. Comprehensive analysis of published studies involving systemic treatment for chondrosarcoma of bone between 2000 and 2013. Clin. Sarcoma Res. 4 , 11. 10.1186/2045-3329-4-11 (2014). Liu, F. et al. Comparation of tumor-free margin or intralesional spondylectomy for chondrosarcoma in mobile spine: a retrospective study of surgery management, complications and prognosis. J. Orthop. Surg. 20 , 307. 10.1186/s13018-025-05712-4 (2025). Nakagawa, M. et al. Clinical, radiological, and histopathological characteristics of periosteal chondrosarcoma with a focus on the frequency of medullary invasion. J Clin Med 11:2062. (2022). 10.3390/jcm11072062 Sampo, M. et al. Impact of the smallest surgical margin on local control in soft tissue sarcoma. Br. J. Surg. 95 , 237–243. 10.1002/bjs.5906 (2008). Tsagozis, P., Brosjö, O. & Skorpil, M. Preoperative radiotherapy of soft-tissue sarcomas: surgical and radiologic parameters associated with local control and survival. Clin. Sarcoma Res. 8 , 19. 10.1186/s13569-018-0106-x (2018). Wang, S. et al. Effect of radiotherapy on local control and overall survival in spinal metastasis of non-small-cell lung cancer after surgery and systemic therapy. Bone Jt. Open. 5 , 350–360. 10.1302/2633-1462.54.BJO-2024-0037.R1 (2024). Kanakarajan, H. et al. Factors associated with the local control of brain metastases: a systematic search and machine learning application. BMC Med. Inf. Decis. Mak. 24 , 177. 10.1186/s12911-024-02579-z (2024). Gwin, J. L. & Bell, J. L. Optimizing local control in soft tissue sarcoma of the extremity. Oncol Williston Park N 8:25–31; discussion 32, 37–38, 41. (1994). Table 1 Table 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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1","display":"","copyAsset":false,"role":"figure","size":103652,"visible":true,"origin":"","legend":"\u003cp\u003eUsing Lasso regression for risk factor selection. Ultimately, age, marital status, race, primary site, grade, histological type, laterality, T stage, N stage, surgical condition, radiotherapy, and chemotherapy were selected. (A) Coefficient path plot; (B) Cross-validation plot.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7799026/v1/37835b4547ecc5cd7453ea66.png"},{"id":94781452,"identity":"81472ee5-1468-4052-b905-5195cb0c2c00","added_by":"auto","created_at":"2025-10-30 15:46:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":112664,"visible":true,"origin":"","legend":"\u003cp\u003eThe multivariable logistic regression analysis identified tumor grade, T stage, N stage, and conditions as independent risk factors for lung metastasis. CI, confidence interval; OR, odds ratio.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7799026/v1/dffc4763c114fa75ce3991ae.png"},{"id":94825227,"identity":"7d627080-b211-4e50-b1a9-1043ef33034f","added_by":"auto","created_at":"2025-10-31 06:50:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":101026,"visible":true,"origin":"","legend":"\u003cp\u003eThe figure shows a nomogram for predicting the probability of lung metastasis in middle-aged and elderly patients with chondrosarcoma. To use the nomogram, first obtain individualized information that includes all predictors listed. Then, draw a vertical line from each variable to the point scale to determine the score for each predictor. Next, sum the points of all predictors to obtain the total points. Finally, draw a vertical line from the total point scale to the risk scale to determine the predicted probability of metastasis.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7799026/v1/9e8952d90639a1eb64434613.png"},{"id":94781455,"identity":"346363d2-7a67-4e8a-8f48-752484b329c9","added_by":"auto","created_at":"2025-10-30 15:46:48","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":111821,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves were used to validate the nomogram. Figure (A) shows the ROC curve for the training cohort, while Figure (B) displays the ROC curve for the validation cohort. ROC, Receiver Operating Characteristic; AUC, Area Under the Curve.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7799026/v1/5ff26b6f194ca546676fa648.png"},{"id":94825462,"identity":"0623e1e4-4899-4ed3-bb80-cb3d21eac04d","added_by":"auto","created_at":"2025-10-31 06:50:20","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":92515,"visible":true,"origin":"","legend":"\u003cp\u003eThe calibration curve of the nomogram predicts the probability of lung metastasis. (A) shows the calibration curve for the training cohort, and (B) displays the calibration curve for the validation cohort. The red line represents the ideal prediction, while the black line indicates the actual performance of the model on the dataset.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7799026/v1/3077bc1c2d57b64329053305.png"},{"id":94781469,"identity":"e9c5dd4b-c3ee-41e7-bdfd-86dcffa86d14","added_by":"auto","created_at":"2025-10-30 15:46:48","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":256673,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7799026/v1/b3973f6bacc72550f266c0bc.png"},{"id":94781460,"identity":"5c824876-a4f0-4082-967e-f4f9b55fdf48","added_by":"auto","created_at":"2025-10-30 15:46:48","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":395448,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7799026/v1/f27c4ed450ff1df7d825ab34.png"},{"id":96437652,"identity":"25614592-efb6-4a7b-be0f-427fbf6c454f","added_by":"auto","created_at":"2025-11-21 06:08:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1969549,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7799026/v1/7077a306-13b1-4eb3-9156-d8266f877206.pdf"},{"id":94781449,"identity":"7b2a03f5-994d-41e3-9293-e360a78c55a2","added_by":"auto","created_at":"2025-10-30 15:46:48","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":21150,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7799026/v1/339d5e44768c7893cbccd133.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Risk Factor Analysis of Pulmonary Metastasis in Middle-Aged and Elderly Patients with Chondrosarcoma and Establishment and Validation of a Nomogram","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChondrosarcoma (CS) is currently the second most common primary malignant bone tumor, second only to osteosarcoma, accounting for approximately 20%\u0026ndash;30% of all malignant bone tumors(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Unlike the other two primary bone sarcomas (osteosarcoma and Ewing\u0026rsquo;s sarcoma), CS primarily affects adults(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), with over 70% of cases diagnosed in patients aged 40 years or older. Similar to other malignant bone tumors, CS mainly spreads via hematogenous dissemination. The development of pulmonary metastasis severely compromises patient prognosis and substantially shortens overall survival. Owing to reduced metabolism, impaired immunity, and increased tumor invasiveness in middle-aged and elderly patients(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), their risk of pulmonary metastasis is substantially higher than that in younger individuals. Therefore, the early identification of middle-aged and elderly patients with chondrosarcoma who are at high risk of lung metastasis, along with more active monitoring and timely intervention, is essential to improving their survival outcomes. Despite clinical recognition of the adverse impact of pulmonary metastasis in chondrosarcoma, there remains a lack of systematic, targeted and in-depth studies investigating the specific risk factors for metastasis in this particular demographic.\u003c/p\u003e\u003cp\u003eAs an intuitive visual prediction tool, the nomogram quantifies and integrates complex risk factors based on results from multivariate regression models into a single graphical interface. This enables clinicians to perform rapid and intuitive probability assessments of specific outcomes\u0026mdash;such as disease recurrence, metastasis, or survival rates\u0026mdash;for individual patients(\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). In recent years, nomograms have demonstrated considerable utility in risk and prognosis prediction across various types of cancer(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Therefore, this study aims to focus specifically on middle-aged and elderly patients with chondrosarcoma. Through a retrospective analysis of a relatively large clinical sample, we will systematically explore independent risk factors influencing the development of pulmonary metastasis in this population ,and construct a clinically practical nomogram prediction model to quantitatively assess the risk probability of pulmonary metastasis in individual middle-aged and elderly chondrosarcoma patients. Meanwhile, to ensure the reliability and generalizability of the model, this study will also employ internal validation to rigorously evaluate the constructed nomogram. We anticipate that the establishment and validation of this model will provide clinicians with an objective basis for the early identification of middle-aged and elderly chondrosarcoma patients at high risk of pulmonary metastasis, thereby facilitating the development of more personalized follow-up strategies and potential proactive interventions, and ultimately optimizing treatment decisions and long-term survival management for this population.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003ePatient Cohort\u003c/h2\u003e\u003cp\u003ePatient data were obtained from the Surveillance, Epidemiology, and End Results (SEER) database, which consists of 18 cancer registries and covers approximately 30% of the total U.S. population(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Data extraction was performed using SEER*Stat software(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). The SEER database includes two types of data: one derived from autopsy reports and the other from patients with complete follow-up. However, we excluded data obtained from autopsy reports and only included information from patients with complete follow-up.\u003c/p\u003e\u003cp\u003eThe inclusion criteria were as follows: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) patients aged 40 years or older at diagnosis; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) diagnosis between 2004 and 2015 to ensure sufficient follow-up time; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) diagnosis of primary chondrosarcoma; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) diagnosis obtained from living patients rather than from death certificates or autopsies; (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) site confined to bone, excluding soft tissue chondrosarcoma; and (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) complete follow-up without missing data.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eVariable Selection\u003c/h3\u003e\n\u003cp\u003eThe variables analyzed to identify risk factors for pulmonary metastasis in CS patients included: age, gender, race, marital status, primary tumor site, tumor grade, histological type, T stage, N stage, tumor laterality, surgical status, lymph node dissection status, radiotherapy, radiotherapy sequence, chemotherapy, time to treatment, and tumor size.\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eThe collected data involving categorical variables were processed, and the dataset (n\u0026thinsp;=\u0026thinsp;659) was randomly allocated into a training cohort (n\u0026thinsp;=\u0026thinsp;462) and a validation cohort (n\u0026thinsp;=\u0026thinsp;197). Chi-square tests were used to compare baseline clinicopathological characteristics between the two groups. Univariate logistic regression and LASSO regression were performed on the training set to preliminarily identify the most significant predictive features, while ensuring that the multivariate model would not be overfitted. Multivariate logistic regression was subsequently applied to determine the final independent risk factors for predicting pulmonary metastasis.\u003c/p\u003e\u003cp\u003eBased on the final selected variables, a nomogram for predicting pulmonary metastasis was constructed and internally validated using the validation cohort. The discriminative ability of the nomogram was evaluated using the ROC curve and AUC value. The calibration curve was used to assess agreement between the nomogram\u0026rsquo;s predicted probabilities and actual observed outcomes. DCA and CIC were employed to evaluate the clinical utility of the prediction model. All statistical analyses and graph generation were performed using R software (version 4.3.3) and RStudio software. A two-tailed p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003ePatient Baseline Characteristics\u003c/h2\u003e\n \u003cp\u003eA total of 659 patients were selected from the SEER database according to the inclusion criteria. To simplify statistical analysis, age was converted from a continuous variable into a categorical variable, with patients aged 40 to 79 grouped by decade and those over 80 classified into a single category. Histological subtypes were broadly classified based on SEER codes into conventional chondrosarcoma (ICD-O-3 code: 9220/3) and special subtypes of chondrosarcoma, which included dedifferentiated chondrosarcoma (9243/3), mesenchymal chondrosarcoma (9240/3), clear cell chondrosarcoma (9242/3), myxoid chondrosarcoma (9231/3), and periosteal chondrosarcoma (9221/3). Tumor size was categorized as small (1\u0026ndash;50 mm), medium (50\u0026ndash;80 mm), or large (\u0026gt;\u0026thinsp;80 mm). Based on the presence or absence of pulmonary metastasis, the baseline data were split into a training cohort (n\u0026thinsp;=\u0026thinsp;462) and a validation cohort (n\u0026thinsp;=\u0026thinsp;197) at a ratio of 7:3. A chi-square test was used to compare differences between the two cohorts, and the results indicated successful grouping (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eIndependent risk factors for pulmonary metastasis\u003c/h2\u003e\n \u003cp\u003eThe univariate logistic regression analysis (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) revealed that the risk factors for pulmonary metastasis included age, marital status, tumor grade, histological type, T stage, N stage, surgery, chemotherapy, time to treatment, and tumor size. The LASSO regression analysis (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) identified the following risk factors for pulmonary metastasis: age, marital status, race, primary site, tumor grade, histological type, laterality, T stage, N stage, surgery, radiotherapy, and chemotherapy. By integrating the statistical significance from the univariate logistic regression and the dimensionality reduction advantage of LASSO regression, we included age, marital status, tumor grade, histological type, T stage, N stage, and chemotherapy in the multivariate logistic regression analysis. The final results (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) demonstrated that tumor grade, T stage, N stage, and surgery were significant risk factors associated with pulmonary metastasis.\u003c/p\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eResults of the univariate logistic regression analysis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCI_low\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCI_high\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP_value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.415\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.606\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarriage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.813\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.870\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.935\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrimary site\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.893\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGrade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHistological type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLaterality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.825\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.262\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.672\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e89.628\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSurgical conditions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.415\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLymph dissection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.645\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.792\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.501\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRadiation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.604\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRadiation sequence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.577\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.217\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChemotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.598\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTime to treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.917\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTumor size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eCI, confidence interval; OR, odds ratio. ( p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant).\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eNomogram Results for Pulmonary Metastasis\u003c/h3\u003e\n\u003cp\u003eBased on the identified variables, we constructed and validated a nomogram (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). For example, consider a middle-aged or elderly patient diagnosed with chondrosarcoma, with a tumor grade of 3, T stage T2, N stage N0, and having undergone partial resection of the tumor. To use the nomogram, a vertical line is drawn from each predictor axis to the points axis to obtain the corresponding score. After summing all individual scores, a total of approximately 122 points corresponds to an estimated probability of pulmonary metastasis of around 30%.\u003c/p\u003e\n\u003ch3\u003eNomogram Performance and Clinical Utility\u003c/h3\u003e\n\u003cp\u003eThis study comprehensively evaluated the performance of the nomogram model\u0026mdash;constructed based on tumor grade, T stage, N stage, and surgical status\u0026mdash;through a systematic multi-dimensional validation framework. The ROC curve (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e) demonstrated excellent discriminatory ability of the model in the training cohort (AUC\u0026thinsp;=\u0026thinsp;0.914), indicating high accuracy in identifying high-risk individuals for pulmonary metastasis. The model also maintained strong reliability in the independent validation cohort (AUC\u0026thinsp;=\u0026thinsp;0.849), exceeding the conventional diagnostic threshold (AUC above 0.7) and confirming its robust generalizability. The calibration curve (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e), which compares predicted probabilities with observed outcomes, showed close agreement between model predictions and actual results. The calibration curve nearly coincided with the ideal diagonal in the training cohort, and although minor fluctuations occurred in the validation cohort due to its smaller sample size, overall consistency remained good, supporting the clinical credibility of the predicted probabilities. Clinical utility was further reinforced using decision analysis tools. Decision curve analysis (Fig.\u0026nbsp;6) indicated that applying this nomogram to guide clinical decisions yielded significant net clinical benefits across a wide threshold probability range of 0.1 to 0.8. Additionally, the clinical impact curve (Fig.\u0026nbsp;7) revealed that among patients identified as high-risk by the model, over 80% were true positives (more than 85% in the training cohort), demonstrating its effectiveness in accurately targeting individuals requiring intervention. This facilitates optimized allocation of medical resources and helps avoid unnecessary screening for low-risk patients.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eChondrosarcoma is a common malignant bone tumor, and pulmonary metastasis is a critical factor affecting patient prognosis(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Currently, there is extensive research on chondrosarcoma at the molecular level. For example, aspartate β-hydroxylase (ASPH) has been identified as a biomarker for chondrosarcoma. Sun et al. found that a higher ASPH expression score may be associated with metastatic risk and prognosis, suggesting its potential as a therapeutic target(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Nemecek et al. conducted a retrospective analysis of 33 patients with dedifferentiated chondrosarcoma and proposed that C-reactive protein (CRP) serves as an independent prognostic predictor for these patients, making it a potential clinical indicator(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Studies by Yang et al. indicated that PD-L1 and PD-L2 play roles in the clinical progression of chondrosarcoma(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), and blocking the PD-1/PD-L1 signaling pathway can activate T-cell-mediated anti-tumor immune responses, thereby inhibiting tumor growth and metastasis(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Isocitrate dehydrogenase 1/2 (IDH1/2), metabolic enzymes frequently mutated in various tumors, may influence the development and prognosis of chondrosarcoma(\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Researchers such as Lugowska suggest that IDH1/2 mutations are important predictors of prognosis in chondrosarcoma(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). While molecular studies provide valuable insights into the pathological mechanisms and potential treatment strategies of chondrosarcoma, translating these findings into clinical applications remains challenging. Therefore, research on clinical characteristics remains essential. Studies focusing on clinical manifestations\u0026mdash;such as age, gender, ethnicity, tumor location, histological type, stage, and treatment modalities\u0026mdash;can elucidate the clinical features and prognostic factors of chondrosarcoma, offering crucial evidence to support clinical decision-making.\u003c/p\u003e\u003cp\u003eThis study represents the first systematic exploration of risk factors for pulmonary metastasis specifically in middle-aged and elderly patients with chondrosarcoma. We successfully developed and validated a nomogram prediction model utilizing large-sample data from the SEER database. Through rigorous univariate, LASSO, and multivariate logistic regression analyses, we identified tumor grade, T stage, N stage, and surgical status as independent risk factors for pulmonary metastasis in this patient population.\u003c/p\u003e\u003cp\u003eBased on these key factors, we constructed an intuitive and clinically applicable nomogram. The model demonstrated excellent discriminatory ability in both the training and validation cohorts, with AUC values of 0.914 and 0.849, respectively, indicating its effectiveness in distinguishing between high- and low-risk patients for pulmonary metastasis. The calibration curve showed strong agreement between predicted probabilities and actual observations. DCA and CIC confirmed that the nomogram provides significant net clinical benefit and practical utility. Specifically, across a broad threshold probability range of 0.1 to 0.8, using this model to guide clinical decisions\u0026mdash;such as determining whether more intensive pulmonary imaging surveillance is needed\u0026mdash;yielded higher net benefit compared to strategies of \u0026ldquo;intervening for all patients\u0026rdquo; or \u0026ldquo;intervening for no patients,\u0026rdquo; underscoring its clear clinical application value. While pulmonary metastasis is typically detected via chest CT scans, previous studies have reported that up to 28\u0026ndash;32% of metastases may be missed by CT imaging(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Therefore, our prediction model demonstrates considerable clinical utility by facilitating early detection of metastatic disease and enabling timely, targeted interventions.\u003c/p\u003e\u003cp\u003eThe findings of this study are largely consistent with existing literature on risk factors for metastasis in chondrosarcoma. Previous studies have indicated that higher tumor grade is associated with an increased risk of distant metastasis. High-grade chondrosarcomas, due to their inherently aggressive phenotype\u0026mdash;characterized by marked cellular atypia and active mitotic activity\u0026mdash;are more likely to breach local tissue boundaries, thereby driving distant dissemination of tumor cells(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). This invasive behavior is closely linked to complex interactions within the tumor microenvironment, including alterations in the extracellular matrix and suppression of immune responses(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Recent research has shown that interactions between tumor cells and the surrounding stroma not only influence tumor growth but also significantly affect metastatic potential(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). High-grade chondrosarcomas often exhibit abnormal degradation of the extracellular matrix, which provides a physical pathway for tumor invasion and metastasis. Specifically, tumor cells secrete enzymes such as matrix metalloproteinases (MMPs) to break down the protective barriers of the extracellular matrix, facilitating their release from the primary site and entry into the bloodstream(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Concurrently, enhanced vascular invasion capability allows tumor cells to disseminate more readily through blood vessels to distant organs such as the lungs, forming metastatic lesions(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Furthermore, the immune microenvironment plays a crucial role in the metastatic process of chondrosarcoma. Tumor cells can evade immune surveillance and attack through multiple mechanisms, such as expressing immune checkpoint molecules like PD-L1 to inhibit T-cell activity(\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). The formation of an immunosuppressive microenvironment further promotes tumor cell survival and growth, accelerating the development of pulmonary metastases. Emerging evidence also suggests that tumor-associated collagen signatures are closely related to metastatic potential(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). For example, in lung cancer, the alignment of collagen fibers is strongly correlated with the invasive capacity of tumor cells. A collagen structure with high fiber length and low density may promote the infiltration of immune cells, thereby influencing responses to immunotherapy(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). As a cornerstone in the prognostic assessment of chondrosarcoma, tumor grade\u0026mdash;and its critical role in predicting pulmonary metastasis\u0026mdash;has been confirmed in multiple studies and is further emphasized in the present research.\u003c/p\u003e\u003cp\u003eT stage is a critical factor in the prognosis and treatment decision-making for chondrosarcoma. Particular attention should be paid to its association with the risk of pulmonary metastasis when the tumor progresses to T3 stage, characterized by breakthrough beyond the cortex and invasion into adjacent structures(32,33) .This correlation is closely linked to the biological behavior of chondrosarcoma, as the development of pulmonary metastasis typically follows a multi-step process: initial local invasion, followed by dissemination of tumor cells through the circulatory system, and eventual colonization in distant organs such as the lungs(\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e).In the early stages of chondrosarcoma (T1\u0026ndash;T2), the tumor is generally confined within the bone. The surrounding cortical bone and soft tissues serve as a natural barrier, effectively preventing tumor cells from entering the bloodstream(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). However, once the tumor advances to T3 stage and breaches the cortical barrier, tumor cells gain direct access to adjacent blood vessels (such as the nutrient artery of the bone) or lymphatic channels(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). This invasive behavior provides a direct pathway for tumor cells to enter the circulation, thereby increasing the risk of distant metastasis. After entering the bloodstream, tumor cells can migrate via blood flow to distant organs including the lungs, where they may colonize and form metastatic lesions under permissive microenvironmental conditions(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Therefore, the T stage not only reflects the extent of local progression but, more importantly, indicates the potential risk of systemic spread(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e).The T3 stage can be regarded as an \u0026ldquo;early warning,\u0026rdquo; signaling clinicians to conduct more comprehensive evaluation and implement aggressive interventions. This implies that patients diagnosed with T3 chondrosarcoma require intensified examination strategies, such as dynamic contrast-enhanced MRI to assess the extent of local invasion, and chest CT scans to detect potential pulmonary metastases at the earliest possible stage(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). In certain cases, whole-body PET-CT may also be valuable for identifying occult distant metastases(\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eN stage, particularly N1 (indicating regional lymph node metastasis), plays a significant role in pulmonary metastasis of chondrosarcoma(\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Although conventional belief holds that lymph node metastasis is rare in chondrosarcoma, relevant studies have shown that the proportion of patients with N1 stage who develop pulmonary metastasis is considerably higher than that of N0 patients(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e), highlighting the importance of lymph node involvement in the metastatic process. Pulmonary metastasis is the most common form of distant spread in chondrosarcoma and represents one of the leading causes of mortality. While hematogenous spread is considered the primary route for pulmonary metastasis, lymph node metastasis may serve as a bridging mechanism facilitating tumor cell entry into the bloodstream(\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). Specifically, tumor cells may initially spread via lymphatic vessels to regional lymph nodes, proliferate within the nodes, and eventually break through the nodal capsule into the circulation, leading to metastasis in distant organs such as the lungs. Furthermore, as lymph nodes are crucial components of the immune system, the growth and proliferation of tumor cells within them may disrupt immune function, enabling tumor cells to evade immune surveillance and thereby promoting metastasis(\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e).In conclusion, N stage is an important factor in determining metastatic spread in chondrosarcoma. Future research should integrate N stage with other clinicopathological factors to develop more accurate predictive models, assisting clinicians in better evaluating individual patient risk and formulating personalized treatment strategies.\u003c/p\u003e\u003cp\u003eSince chondrosarcoma is generally resistant to chemotherapy and radiotherapy, surgical resection remains the primary treatment modality(\u003cspan additionalcitationids=\"CR46 CR47 CR48\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). Studies have shown a close relationship between tumor metastasis and surgical margins, with more extensive resection associated with a reduced risk of pulmonary metastasis, underscoring the critical impact of surgical quality on both metastasis and prognosis(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). Wide resection can decrease the risk of metastasis, whereas incomplete removal or residual tumor after surgery may increase the likelihood of recurrence and metastasis, highlighting the importance of achieving thorough local tumor control(\u003cspan additionalcitationids=\"CR52 CR53\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). Although extended resection helps reduce the risk of pulmonary metastasis, an overly aggressive surgical approach may adversely affect the patient\u0026rsquo;s quality of life. Wide resections can lead to impaired limb function, reduced mobility, and amputation may cause significant psychological and social challenges. Therefore, when planning surgical intervention, it is essential to balance tumor control with the preservation of the patient\u0026rsquo;s functional and life quality.\u003c/p\u003e\u003cp\u003eThe nomogram developed in this study provides clinicians with a practical tool for quantitatively assessing the risk of pulmonary metastasis in middle-aged and elderly patients with chondrosarcoma. It facilitates the early identification of high-risk patients, prompting healthcare providers to establish more intensive and sensitive surveillance protocols. Such protocols may include shortened intervals for CT reevaluation, or the use of high-resolution CT or PET-CT. Furthermore, the tool offers an objective basis for risk assessment when formulating initial treatment plans, particularly surgical strategies, and supports considerations regarding adjuvant therapies. Although current options show limited efficacy, high-risk patients may be considered for enrollment in clinical trials of novel therapies. Additionally, the model supports clear and intuitive communication with patients and their families regarding the risk of disease progression.\u003c/p\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eLimitations and Prospects\u003c/h2\u003e\u003cp\u003eHowever, our study has several limitations. First, as a retrospective study, it may carry potential risks of bias. The data were primarily derived from the U.S. population, which may not fully represent patient profiles in other countries or regions. Second, the nomogram was validated only internally, which could lead to model overfitting. External validation would improve its reliability. Third, the grading and staging systems in the SEER database may be outdated, which could introduce discrepancies when comparing our findings with newer studies. Additionally, molecular markers, genetic and epigenetic factors were not available in the SEER database and thus were not included in this study.\u003c/p\u003e\u003cp\u003eFuture research could build upon the framework of this model by incorporating additional patient characteristics and molecular biomarkers to further enhance its predictive accuracy and generalizability. Furthermore, expanding the model\u0026rsquo;s application may help optimize early diagnosis and intervention strategies for pulmonary metastasis associated with other malignancies. Through subsequent research and external validation, this predictive model has the potential to become an integral part of clinical decision-support tools, significantly improving treatment outcomes and quality of life for patients with chondrosarcoma.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, tumor grade, T stage, N stage, and surgical status were identified as risk factors for pulmonary metastasis in chondrosarcoma patients. This study developed and validated a nomogram for predicting the probability of pulmonary metastasis in middle-aged and elderly chondrosarcoma patients, providing clinicians with a rapid and user-friendly assessment tool. This nomogram may guide surgeons and oncologists in optimizing individualized treatment strategies and facilitating improved clinical decision-making.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJoint Funding Program for Science and Technology Innovation in Healthcare（Grant number：N2024LH016）\u003c/p\u003e\n\u003cp\u003eJoint Funding Program for Science and Technology Innovation in Healthcare（Grant number：N2024LH010）\u003c/p\u003e\n\u003cp\u003eSupported by Fujian Provincial Natural Science Foundation of China（Grant number：2024J011595）\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe SEER datasets used during the current study can be found here: https:// seer. cancer. gov/. The ZJCH datasets are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are grateful to all the staff in the National Cancer Institute (USA) for their contribution to the SEER program.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical review statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs this study is based on a publicly available database, it is exempt from ethical review requirements.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLanduzzi, L., Ruzzi, F., Lollini, P-L. \u0026amp; Scotlandi, K. 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(1994).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Chondrosarcoma, Pulmonary metastasis, Nomogram","lastPublishedDoi":"10.21203/rs.3.rs-7799026/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7799026/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground and Objectives\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePulmonary metastasis in middle-aged and elderly patients with chondrosarcoma often leads to poor prognosis. This study aimed to identify the independent risk factors for pulmonary metastasis in this population and to develop and validate a clinical prediction model (nomogram) for accurately estimating the probability of pulmonary metastasis.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA total of 659 eligible chondrosarcoma patients (aged 40 years or older) were identified retrospectively from the Surveillance, Epidemiology, and End Results (SEER) database, covering the period from 2004 to 2015. Univariate logistic regression, least absolute shrinkage and selection operator (LASSO) regression, and multivariate logistic regression were used to identify the risk factors for pulmonary metastasis. The selected risk factors, together with their respective weights, were visually represented in a nomogram. The predictive performance and clinical utility of the nomogram were evaluated using the receiver operating characteristic (ROC) curve, calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC).\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTumor grade, T stage, N stage, and surgical status were identified as independent risk factors for pulmonary metastasis. The area under the ROC curve (AUC) was 0.914 for the training cohort and 0.849 for the validation cohort. The calibration curve demonstrated good agreement between the model\u0026rsquo;s predicted probabilities and observed outcomes, while the DCA and CIC confirmed the nomogram\u0026rsquo;s significant clinical value.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTumor grade, T stage, N stage, and surgical status are important independent risk factors influencing pulmonary metastasis in middle-aged and elderly patients with chondrosarcoma. The nomogram constructed in this study provides clinicians with a rapid, user-friendly tool for predicting the probability of pulmonary metastasis in this patient population, and its accuracy and clinical applicability have been validated.\u003c/p\u003e","manuscriptTitle":"Risk Factor Analysis of Pulmonary Metastasis in Middle-Aged and Elderly Patients with Chondrosarcoma and Establishment and Validation of a Nomogram","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-30 15:46:43","doi":"10.21203/rs.3.rs-7799026/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"222b31e4-2354-4d0c-a3be-414eb61ebe36","owner":[],"postedDate":"October 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":56962199,"name":"Biological sciences/Cancer"},{"id":56962200,"name":"Health sciences/Diseases"},{"id":56962201,"name":"Health sciences/Medical research"},{"id":56962202,"name":"Health sciences/Oncology"},{"id":56962203,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-11-21T06:08:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-30 15:46:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7799026","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7799026","identity":"rs-7799026","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}