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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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 & Sons, Inc.</rights><rights>Copyright © 2021 Xinjie Wu et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0</rights><rights>Copyright © 2021 Xinjie Wu et al. 2021</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c476t-ad1d9e0bdff96c50b397673a27a78a9505c728502ddd70ca50bb4037cb58b7bb3</citedby><cites>FETCH-LOGICAL-c476t-ad1d9e0bdff96c50b397673a27a78a9505c728502ddd70ca50bb4037cb58b7bb3</cites><orcidid>0000-0001-8308-5676 ; 0000-0001-5020-718X ; 0000-0002-9605-8658 ; 0000-0002-6483-1642</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8147544/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8147544/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,4023,27922,27923,27924,53790,53792</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34055971$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Thai, Khac-Minh</contributor><contributor>Khac-Minh Thai</contributor><creatorcontrib>Wu, Xinjie</creatorcontrib><creatorcontrib>Wang, Yanlei</creatorcontrib><creatorcontrib>Sun, Wei</creatorcontrib><creatorcontrib>Tan, Mingsheng</creatorcontrib><title>Prognostic Factors and a Nomogram Predicting Overall Survival in Patients with Limb Chondrosarcomas: A Population-Based Study</title><title>BioMed research international</title><addtitle>Biomed Res Int</addtitle><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.</description><subject>Adult</subject><subject>Age</subject><subject>Bone Neoplasms - mortality</subject><subject>Bones</subject><subject>Calibration</subject><subject>Cancer therapies</subject><subject>Chemotherapy</subject><subject>Chondrosarcoma</subject><subject>Chondrosarcoma - mortality</subject><subject>Chondrosarcoma - pathology</subject><subject>Clinical Decision-Making</subject><subject>Decision making</subject><subject>Disease</subject><subject>Epidemiology</subject><subject>Extremities - pathology</subject><subject>Female</subject><subject>Hazard identification</subject><subject>Histology</subject><subject>Humans</subject><subject>Kaplan-Meier Estimate</subject><subject>Male</subject><subject>Medical prognosis</subject><subject>Middle Aged</subject><subject>Multivariate Analysis</subject><subject>Nomograms</subject><subject>Nomography (Mathematics)</subject><subject>Patients</subject><subject>Population studies</subject><subject>Population-based studies</subject><subject>Prediction models</subject><subject>Prognosis</subject><subject>Radiation therapy</subject><subject>Regression analysis</subject><subject>Regression models</subject><subject>Software</subject><subject>Statistical analysis</subject><subject>Statistical models</subject><subject>Surgery</subject><subject>Survival</subject><subject>Survival Analysis</subject><subject>Training</subject><subject>Tumors</subject><subject>United States - epidemiology</subject><subject>Variables</subject><issn>2314-6133</issn><issn>2314-6141</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9ks9v0zAUxyMEYtPYjTOyxAUJwuzEjmMOSKVigFSxSoOz9WI7qafE7uyk0w773-eopfw44Ist-aOP39fvZdlLgt8TwthFgQtyQRnBtCifZKdFSWheEUqeHs9leZKdx3iD06pJhUX1PDspKWZMcHKaPayD75yPo1XoEtToQ0TgNAL03Q--CzCgdTDaqtG6Dl3tTIC-R9dT2Nkd9Mg6tIbRGjdGdGfHDVrZoUHLjXc6-AhB-QHiB7RAa7-d-kR6l3-CaDS6Hid9_yJ71kIfzflhP8t-Xn7-sfyar66-fFsuVrmivBpz0EQLgxvdtqJSDDel4BUvoeDAaxAMM8WLmuFCa82xgkQ0FJdcNaxueNOUZ9nHvXc7NYPRKtWbcshtsAOEe-nByr9vnN3Izu9kTShnlCbBm4Mg-NvJxFEONirT9-CMn6IsWJmaIEQ1o6__QW_8FFyKN1OUFoUg5DfVQW-kda1P76pZKheVqGpRYzG73u0plX4zBtMeSyZYzgMg5wGQhwFI-Ks_Yx7hX-1OwNs9sLFOw539v-4R8B-4MA</recordid><startdate>2021</startdate><enddate>2021</enddate><creator>Wu, Xinjie</creator><creator>Wang, Yanlei</creator><creator>Sun, Wei</creator><creator>Tan, Mingsheng</creator><general>Hindawi</general><general>John Wiley & Sons, Inc</general><general>Hindawi Limited</general><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QL</scope><scope>7QO</scope><scope>7T7</scope><scope>7TK</scope><scope>7U7</scope><scope>7U9</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>CWDGH</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope><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></search><sort><creationdate>2021</creationdate><title>Prognostic Factors and a Nomogram Predicting Overall Survival in Patients with Limb Chondrosarcomas: A Population-Based Study</title><author>Wu, Xinjie ; Wang, Yanlei ; Sun, Wei ; Tan, Mingsheng</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c476t-ad1d9e0bdff96c50b397673a27a78a9505c728502ddd70ca50bb4037cb58b7bb3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Adult</topic><topic>Age</topic><topic>Bone Neoplasms - mortality</topic><topic>Bones</topic><topic>Calibration</topic><topic>Cancer therapies</topic><topic>Chemotherapy</topic><topic>Chondrosarcoma</topic><topic>Chondrosarcoma - mortality</topic><topic>Chondrosarcoma - pathology</topic><topic>Clinical Decision-Making</topic><topic>Decision making</topic><topic>Disease</topic><topic>Epidemiology</topic><topic>Extremities - pathology</topic><topic>Female</topic><topic>Hazard identification</topic><topic>Histology</topic><topic>Humans</topic><topic>Kaplan-Meier Estimate</topic><topic>Male</topic><topic>Medical prognosis</topic><topic>Middle Aged</topic><topic>Multivariate Analysis</topic><topic>Nomograms</topic><topic>Nomography (Mathematics)</topic><topic>Patients</topic><topic>Population studies</topic><topic>Population-based studies</topic><topic>Prediction models</topic><topic>Prognosis</topic><topic>Radiation therapy</topic><topic>Regression analysis</topic><topic>Regression models</topic><topic>Software</topic><topic>Statistical analysis</topic><topic>Statistical models</topic><topic>Surgery</topic><topic>Survival</topic><topic>Survival Analysis</topic><topic>Training</topic><topic>Tumors</topic><topic>United States - epidemiology</topic><topic>Variables</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wu, Xinjie</creatorcontrib><creatorcontrib>Wang, Yanlei</creatorcontrib><creatorcontrib>Sun, Wei</creatorcontrib><creatorcontrib>Tan, Mingsheng</creatorcontrib><collection>Hindawi Publishing Complete</collection><collection>Hindawi Publishing Subscription Journals</collection><collection>Hindawi Publishing Open Access</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Neurosciences Abstracts</collection><collection>Toxicology Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>Middle East & Africa Database</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biological Science Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>BioMed research international</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wu, Xinjie</au><au>Wang, Yanlei</au><au>Sun, Wei</au><au>Tan, Mingsheng</au><au>Thai, Khac-Minh</au><au>Khac-Minh Thai</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Prognostic Factors and a Nomogram Predicting Overall Survival in Patients with Limb Chondrosarcomas: A Population-Based Study</atitle><jtitle>BioMed research international</jtitle><addtitle>Biomed Res Int</addtitle><date>2021</date><risdate>2021</risdate><volume>2021</volume><issue>1</issue><spage>4510423</spage><epage>4510423</epage><pages>4510423-4510423</pages><issn>2314-6133</issn><eissn>2314-6141</eissn><abstract>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.</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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