Development and validation of a nomogram for predicting prognosis of high-grade chondrosarcoma: A surveillance, epidemiology, and end results-based population analysis

Sun, Yihua; Ouyang, Chenxi; Li, Yong; Liu, Yang; Jiang, Ming; Nong, Luming; Gao, Gongming

doi:10.1177/10225536231174255

Cited by 2 publications

(1 citation statement)

References 27 publications

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“…Nomograms are prognostic instruments that integrate determinant variables and yield a numerical likelihood of a clinical result [32]. Numerous nomograms have been established also for the prognosis of patients with chondrosarcoma [33], osteosarcoma, and Ewing sarcoma [34][35][36]. These nomograms also utilize such factors as the occurrence of surgery, chemotherapy, or the patient's age.…”

Section: Clinical Biomarkersmentioning

confidence: 99%

Novel biomarkers in bone sarcomas — diagnosis, treatment selection, and clinical trials

Czarnecka,

Błoński,

Chmiel

et al. 2024

Oncol Clin Pract

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Malignant bone tumors (MBT) are a rare and heterogeneous group of tumors, arising mostly in children. Localized disease is usually treated with surgery, but prognosis worsens in advanced stages. Currently, with limited biomarkers in clinical use, prognosis depends on histological grading and clinical features. However, the use of biomarkers remains inadequate, limiting treatment efficacy and increasing the risk of recurrence and disease progression for patients. Potential biomarkers based on genomics, proteomics, and clinical characteristics are currently entering clinical use in multiple cancers. Biomarker research in MBT faces additional challenges resulting from the rarity of these entities. Emerging biomarker concepts require clinical validation to create robust frameworks for precision oncology. This review of new biomarkers is based on relevant literature from Pubmed, Scopus, and clinicatrials.gov databases retrieved in November 2023. At present, the definition of prognostic markers in malignant bone tumors remains challenging. More research is needed, particularly to tailor treatments based on advanced genetic profiling and analysis of individual tumor and patient characteristics. Many newly identified biomarkers have not been clinically validated.

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Section: Clinical Biomarkersmentioning

confidence: 99%

Novel biomarkers in bone sarcomas — diagnosis, treatment selection, and clinical trials

Czarnecka,

Błoński,

Chmiel

et al. 2024

Oncol Clin Pract

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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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Development and validation of a nomogram for predicting prognosis of high-grade chondrosarcoma: A surveillance, epidemiology, and end results-based population analysis

Cited by 2 publications

References 27 publications

Novel biomarkers in bone sarcomas — diagnosis, treatment selection, and clinical trials

Novel biomarkers in bone sarcomas — diagnosis, treatment selection, and clinical trials

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

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