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, Liming; Gong, Yan; Jiang, Jiajia; Zhao, Dongxu

doi:10.1007/s00432-023-05143-w

Cited by 2 publications

(1 citation statement)

References 52 publications

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“…10 Jiang et al used three types of surgery to construct a survival nomogram. 11 However, these studies did not include the complete type of surgery as a study variable, nor did they construct predictive models for postoperative OS. 11 Therefore, it is extremely important to construct a reliable and accurate OS prediction model for patients with chondrosarcoma after surgery.…”

Section: Introductionmentioning

confidence: 99%

Prognostic Factors and Surgical Impact of Non‐metastatic Conventional Chondrosarcoma of the Extremities

Wang,

Zhen

2023

Orthopaedic Surgery

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ObjectiveChondrosarcoma is a common bone malignancy, and the main treatment method is surgery. Different surgeries lead to different survival outcomes. The aim of this study was to construct a new clinical predictive tool to accurately predict the overall survival (OS) and cause specific survival (CSS) of patients with chondrosarcoma receiving different treatments.MethodsA total of 620 patients with chondrosarcoma registered between January 1, 2000 and December 31, 2016 were recruited as study targets. The missing values are filled by multiple imputation. Two continuous variables, age and tumor size, were divided into binary variables based on Kaplan–Meier curve. Univariate and multivariate analyses were used to explore predictors and establish nomograms. Propensity score matching (PSM) analysis was used to reduce the impact of potential confounders to determine whether different surgical modalities had any survival benefits in subgroups.ResultsIn a multivariate cox regression, age, grade, tumor size, radiotherapy, chemotherapy, and surgical methods were identified as independent prognostic factors for chondrosarcoma. To construct 1‐, 3‐, and 5‐year nomogram maps of OS and CSS with prognostic factors and verify the c index internally (OS, 0.807; CSS, 0.847) above American Joint Committee on Cancer (AJCC) (OS, 0.685; CSS, 0.732).ConclusionThis study found that the 5 year overall survival rate of patients with non‐metastatic chondrosarcoma of the extremities was about 80%. Age, high malignancy, large tumor, prior chemoradiotherapy, and poor surgical selection were independent risk factors. Therefore, the nomogram established in this study will help to optimize clinicians' personalized decision making for patients.

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Section: Introductionmentioning

confidence: 99%

Prognostic Factors and Surgical Impact of Non‐metastatic Conventional Chondrosarcoma of the Extremities

Wang,

Zhen

2023

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

Cited by 2 publications

References 52 publications

Prognostic Factors and Surgical Impact of Non‐metastatic Conventional Chondrosarcoma of the Extremities

Prognostic Factors and Surgical Impact of Non‐metastatic Conventional Chondrosarcoma of the Extremities

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

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