Prognostic Factors and a Nomogram Predicting Overall Survival in Patients with Limb Chondrosarcomas: A Population-Based Study

Introduction. We aimed to develop and validate a nomogram for predicting the overall survival of patients with limb chondrosarcomas. Methods. The Surveillance, Epidemiology, and End Results (SEER) program database was used to identify patients diagnosed with chondrosarcomas, from which data was extr...

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Veröffentlicht in:BioMed research international 2021, Vol.2021 (1), p.4510423-4510423
Hauptverfasser: Wu, Xinjie, Wang, Yanlei, Sun, Wei, Tan, Mingsheng
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Tan, Mingsheng
description Introduction. We aimed to develop and validate a nomogram for predicting the overall survival of patients with limb chondrosarcomas. Methods. The Surveillance, Epidemiology, and End Results (SEER) program database was used to identify patients diagnosed with chondrosarcomas, from which data was extracted from 18 registries in the United States between 1973 and 2016. A total of 813 patients were selected from the database. Univariate and multivariate analyses were performed using Cox proportional hazards regression models on the training group to identify independent prognostic factors and construct a nomogram to predict the 3- and 5-year survival probability of patients with limb chondrosarcomas. The predictive values were compared using concordance indexes (C-indexes) and calibration plots. Results. All 813 patients were randomly divided into a training group (n=572) and a validation group (n=241). After univariate and multivariate Cox regression, a nomogram was constructed based on a new model containing the predictive variables of age, site, grade, tumor size, histology, stage, and use of surgery, radiotherapy, or chemotherapy. The prediction model provided excellent C-indexes (0.86 and 0.77 in the training and validation groups, respectively). The good discrimination and calibration of the nomograms were demonstrated for both the training and validation groups. Conclusions. The nomograms precisely and individually predict the overall survival of patients with limb chondrosarcomas and could assist personalized prognostic evaluation and individualized clinical decision-making.
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We aimed to develop and validate a nomogram for predicting the overall survival of patients with limb chondrosarcomas. Methods. The Surveillance, Epidemiology, and End Results (SEER) program database was used to identify patients diagnosed with chondrosarcomas, from which data was extracted from 18 registries in the United States between 1973 and 2016. A total of 813 patients were selected from the database. Univariate and multivariate analyses were performed using Cox proportional hazards regression models on the training group to identify independent prognostic factors and construct a nomogram to predict the 3- and 5-year survival probability of patients with limb chondrosarcomas. The predictive values were compared using concordance indexes (C-indexes) and calibration plots. Results. All 813 patients were randomly divided into a training group (n=572) and a validation group (n=241). After univariate and multivariate Cox regression, a nomogram was constructed based on a new model containing the predictive variables of age, site, grade, tumor size, histology, stage, and use of surgery, radiotherapy, or chemotherapy. The prediction model provided excellent C-indexes (0.86 and 0.77 in the training and validation groups, respectively). The good discrimination and calibration of the nomograms were demonstrated for both the training and validation groups. Conclusions. The nomograms precisely and individually predict the overall survival of patients with limb chondrosarcomas and could assist personalized prognostic evaluation and individualized clinical decision-making.</description><identifier>ISSN: 2314-6133</identifier><identifier>EISSN: 2314-6141</identifier><identifier>DOI: 10.1155/2021/4510423</identifier><identifier>PMID: 34055971</identifier><language>eng</language><publisher>United States: Hindawi</publisher><subject>Adult ; Age ; Bone Neoplasms - mortality ; Bones ; Calibration ; Cancer therapies ; Chemotherapy ; Chondrosarcoma ; Chondrosarcoma - mortality ; Chondrosarcoma - pathology ; Clinical Decision-Making ; Decision making ; Disease ; Epidemiology ; Extremities - pathology ; Female ; Hazard identification ; Histology ; Humans ; Kaplan-Meier Estimate ; Male ; Medical prognosis ; Middle Aged ; Multivariate Analysis ; Nomograms ; Nomography (Mathematics) ; Patients ; Population studies ; Population-based studies ; Prediction models ; Prognosis ; Radiation therapy ; Regression analysis ; Regression models ; Software ; Statistical analysis ; Statistical models ; Surgery ; Survival ; Survival Analysis ; Training ; Tumors ; United States - epidemiology ; Variables</subject><ispartof>BioMed research international, 2021, Vol.2021 (1), p.4510423-4510423</ispartof><rights>Copyright © 2021 Xinjie Wu et al.</rights><rights>COPYRIGHT 2021 John Wiley &amp; Sons, Inc.</rights><rights>Copyright © 2021 Xinjie Wu et al. 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We aimed to develop and validate a nomogram for predicting the overall survival of patients with limb chondrosarcomas. Methods. The Surveillance, Epidemiology, and End Results (SEER) program database was used to identify patients diagnosed with chondrosarcomas, from which data was extracted from 18 registries in the United States between 1973 and 2016. A total of 813 patients were selected from the database. Univariate and multivariate analyses were performed using Cox proportional hazards regression models on the training group to identify independent prognostic factors and construct a nomogram to predict the 3- and 5-year survival probability of patients with limb chondrosarcomas. The predictive values were compared using concordance indexes (C-indexes) and calibration plots. Results. All 813 patients were randomly divided into a training group (n=572) and a validation group (n=241). 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We aimed to develop and validate a nomogram for predicting the overall survival of patients with limb chondrosarcomas. Methods. The Surveillance, Epidemiology, and End Results (SEER) program database was used to identify patients diagnosed with chondrosarcomas, from which data was extracted from 18 registries in the United States between 1973 and 2016. A total of 813 patients were selected from the database. Univariate and multivariate analyses were performed using Cox proportional hazards regression models on the training group to identify independent prognostic factors and construct a nomogram to predict the 3- and 5-year survival probability of patients with limb chondrosarcomas. The predictive values were compared using concordance indexes (C-indexes) and calibration plots. Results. All 813 patients were randomly divided into a training group (n=572) and a validation group (n=241). After univariate and multivariate Cox regression, a nomogram was constructed based on a new model containing the predictive variables of age, site, grade, tumor size, histology, stage, and use of surgery, radiotherapy, or chemotherapy. The prediction model provided excellent C-indexes (0.86 and 0.77 in the training and validation groups, respectively). The good discrimination and calibration of the nomograms were demonstrated for both the training and validation groups. Conclusions. The nomograms precisely and individually predict the overall survival of patients with limb chondrosarcomas and could assist personalized prognostic evaluation and individualized clinical decision-making.</abstract><cop>United States</cop><pub>Hindawi</pub><pmid>34055971</pmid><doi>10.1155/2021/4510423</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0001-8308-5676</orcidid><orcidid>https://orcid.org/0000-0001-5020-718X</orcidid><orcidid>https://orcid.org/0000-0002-9605-8658</orcidid><orcidid>https://orcid.org/0000-0002-6483-1642</orcidid><oa>free_for_read</oa></addata></record>
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subjects Adult
Age
Bone Neoplasms - mortality
Bones
Calibration
Cancer therapies
Chemotherapy
Chondrosarcoma
Chondrosarcoma - mortality
Chondrosarcoma - pathology
Clinical Decision-Making
Decision making
Disease
Epidemiology
Extremities - pathology
Female
Hazard identification
Histology
Humans
Kaplan-Meier Estimate
Male
Medical prognosis
Middle Aged
Multivariate Analysis
Nomograms
Nomography (Mathematics)
Patients
Population studies
Population-based studies
Prediction models
Prognosis
Radiation therapy
Regression analysis
Regression models
Software
Statistical analysis
Statistical models
Surgery
Survival
Survival Analysis
Training
Tumors
United States - epidemiology
Variables
title Prognostic Factors and a Nomogram Predicting Overall Survival in Patients with Limb Chondrosarcomas: A Population-Based Study
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