Risk factors for metastasis at presentation with conventional chondrosarcoma: a population-based study

Song, Kunpeng; Shi, Xiao; Liang, Xueying; Wang, Hongli; Zou, Fei; Lu, Feizhou; Ma, Xin; Jiang, Jianyuan

doi:10.1007/s00264-018-3942-7

Cited by 26 publications

(37 citation statements)

References 26 publications

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“…In our series, we discovered that patients ≥65 years had poorer OS and CSS. Prior to our series, many studies drew the same conclusion. The subgroup analysis implied that elder patients were prone to reject surgery (Table S4, P = 0.005), probably because they could not tolerate surgery well.…”

Section: Discussionmentioning

confidence: 82%

“…Chondrosarcoma is the second most frequent primary malignant tumor of the bone. Metastatic chondrosarcoma is an advanced stage of this disease and has been demonstrated as a predictor of poor prognosis. Thus, evaluating the pre‐metastatic stage is meaningful to improve prognosis and assist clinicians in making therapeutic choices.…”

Section: Discussionmentioning

confidence: 99%

“…Previously, primary sites were often divided into two groups: axial and appendicular groups. Chondrosarcomas in the pelvis and spine had a worse prognosis due to anatomic constraints hindering the efforts of complete tumor resection, which resulted in a high rate of tumor recurrence. However, the subgroup analysis showed that surgery did not have an close connection with the primary site, because most of the patients underwent surgery no matter where tumors occurred (Table S9).…”

Section: Discussionmentioning

confidence: 99%

“…Identifying the prognostic factors of pre‐metastatic chondrosarcoma may help oncologists treat patients individually. To improve the predictive capability of survival models, previous studies constructed several nomograms, but most nomograms were derived from small samples in different research centers, which might diminish the accuracy of the predictors.…”

Section: Introductionmentioning

confidence: 99%

“…Metastasis in advanced stage cancer suggests a poor prognosis; therefore, early diagnosis in the pre‐metastatic stage is significant and evaluating this stage is helpful for improving prognoses. However, a multi‐center study based on a large sample size exploring the prognostic factors of non‐metastatic chondrosarcomas has not been reported.…”

Section: Introductionmentioning

confidence: 99%

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Identifying the Prognosis Factors and Predicting the Survival Probability in Patients with Non‐Metastatic Chondrosarcoma from the SEER Database

Huang

Sun

Zheng

et al. 2019

Orthopaedic Surgery

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ObjectiveTo identify prognostic factors and establish nomograms for predicting overall survival (OS) and cause specific survival (CSS) of patients with non‐metastatic chondrosarcoma.MethodsWe collected information on patients with non‐metastatic chondrosarcoma from the Surveillance, Epidemiology, and End Results (SEER) database between 2005 and 2014, together with data from the First Affiliated Hospital of Zhengzhou University from 2011 to 2016. Variables including patients’ baseline demographics (age, race, and gender), tumor characteristics (tumor size and extension, histology subtype, primary site, and American Joint Committee on Cancer [AJCC] stage), therapy (surgery, chemotherapy, and radiotherapy), and socioeconomic status (SES) were extracted for further analysis. OS and CSS were retrieved as our researching endpoints. Patients from the database were regarded as the training set, and univariate analysis, Lasso regression and multivariate analysis as well as the random forest were used to explore the predictors and establish nomograms. To validate nomograms internally and externally, we applied bootstrapped validation internally with the training dataset, while the dataset for external validation was obtained from the First Affiliated Hospital of Zhengzhou University. We estimated the discriminative ability of nomograms based on Cox proportional hazard regression models by means of calibration curves and the concordance index (C‐index) of internal and external validation.ResultsAfter the implementation of exclusion criteria, there were 1267 patients in the training set and 72 patients in the testing set with non‐metastatic chondrosarcomas. Age, gender, grade, histological subtype, primary site, surgery, radiation, chemotherapy, being employed/unemployed, tumor size, and tumor extension were significantly associated with prognosis in the univariate analysis. Age, gender, tumor size and extension, primary site, surgery, radiotherapy, chemotherapy, histological grade, and subtype were independent prognostic factors in the Cox models. The C‐index of nomograms (internal: OS, 0.787; CSS, 0.821; external: OS, 0.777; CSS, 0.821) were higher than following conventional systems: AJCC sixth (OS, 0.640; CSS, 0.673) and seventh edition (OS, 0.675; CSS, 0.711).ConclusionsAge, gender, tumor size and extension, surgery, histological grade, and subtype were independent prognostic factors for both OS and CSS. In addition, we revealed that chondrosarcomas in the trunk, radiotherapy, and chemotherapy were correlated with poor prognosis. Our nomograms based on significant clinicopathologic features can well predict the 3‐year and 5‐year survival probability of patients with non‐metastatic chondrosarcoma and assist oncologists in making accurate survival evaluation.

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Section: Discussionmentioning

confidence: 82%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Identifying the Prognosis Factors and Predicting the Survival Probability in Patients with Non‐Metastatic Chondrosarcoma from the SEER Database

Huang

Sun

Zheng

et al. 2019

Orthopaedic Surgery

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Novel machine‐learning prediction tools for overall survival of patients with chondrosarcoma: Based on recursive partitioning analysis

Yang,

Bao

et al. 2024

Cancer Medicine

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BackgroundChondrosarcoma (CHS), a bone malignancy, poses a significant challenge due to its heterogeneous nature and resistance to conventional treatments. There is a clear need for advanced prognostic instruments that can integrate multiple prognostic factors to deliver personalized survival predictions for individual patients. This study aimed to develop a novel prediction tool based on recursive partitioning analysis (RPA) to improve the estimation of overall survival for patients with CHS.MethodsData from the Surveillance, Epidemiology, and End Results (SEER) database were analyzed, including demographic, clinical, and treatment details of patients diagnosed between 2000 and 2018. Using C5.0 algorithm, decision trees were created to predict survival probabilities at 12, 24, 60, and 120 months. The performance of the models was assessed through confusion scatter plot, accuracy rate, receiver operator characteristic (ROC) curve, and area under ROC curve (AUC).ResultsThe study identified tumor histology, surgery, age, visceral (brain/liver/lung) metastasis, chemotherapy, tumor grade, and sex as critical predictors. Decision trees revealed distinct patterns for survival prediction at each time point. The models showed high accuracy (82.40%–89.09% in training group, and 82.16%–88.74% in test group) and discriminatory power (AUC: 0.806–0.894 in training group, and 0.808–0.882 in test group) in both training and testing datasets. An interactive web‐based shiny APP (URL: https://yangxg1209.shinyapps.io/chondrosarcoma_survival_prediction/) was developed, simplifying the survival prediction process for clinicians.ConclusionsThis study successfully employed RPA to develop a user‐friendly tool for personalized survival predictions in CHS. The decision tree models demonstrated robust predictive capabilities, with the interactive application facilitating clinical decision‐making. Future prospective studies are recommended to validate these findings and further refine the predictive model.

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The novel dynamic nomogram and risk classification system constructed for predicting post-surgical overall survival and mortality risk in primary chondrosarcoma: a population study based on SEER database

Jiang

Gong

Jiang

et al. 2023

J Cancer Res Clin Oncol

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Risk factors for metastasis at presentation with conventional chondrosarcoma: a population-based study

Abstract: Conventional chondrosarcoma patients with greater age, axial or craniofacial tumour location, higher grade, and larger tumor size were more likely to have metastasis at diagnosis.

Cited by 26 publications

References 26 publications

Identifying the Prognosis Factors and Predicting the Survival Probability in Patients with Non‐Metastatic Chondrosarcoma from the SEER Database

Identifying the Prognosis Factors and Predicting the Survival Probability in Patients with Non‐Metastatic Chondrosarcoma from the SEER Database

Novel machine‐learning prediction tools for overall survival of patients with chondrosarcoma: Based on recursive partitioning analysis

The novel dynamic nomogram and risk classification system constructed for predicting post-surgical overall survival and mortality risk in primary chondrosarcoma: a population study based on SEER database

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