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The NEAT Predictive Model for Survival in Patients with Advanced Cancer
PURPOSE: We previously developed a model to more accurately predict life expectancy for stage IV cancer patients referred to radiation oncology. The goals of this study are to validate this model and to compare competing published models. MATERIALS AND METHODS: From May 2012 to March 2015, 280 conse...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Korean Cancer Association
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6192914/ https://www.ncbi.nlm.nih.gov/pubmed/29361815 http://dx.doi.org/10.4143/crt.2017.223 |
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author | Zucker, Amanda Tsai, Chiaojung Jillian Loscalzo, John Calves, Pedro Kao, Johnny |
author_facet | Zucker, Amanda Tsai, Chiaojung Jillian Loscalzo, John Calves, Pedro Kao, Johnny |
author_sort | Zucker, Amanda |
collection | PubMed |
description | PURPOSE: We previously developed a model to more accurately predict life expectancy for stage IV cancer patients referred to radiation oncology. The goals of this study are to validate this model and to compare competing published models. MATERIALS AND METHODS: From May 2012 to March 2015, 280 consecutive patientswith stage IV cancerwere prospectively evaluated by a single radiation oncologist. Patients were separated into training, validation and combined sets. TheNEAT model evaluated number of active tumors (“N”), Eastern Cooperative Oncology Group performance status (“E”), albumin (“A”) and primary tumor site (“T”). The Odette Cancer Center model validated performance status, bone only metastases and primary tumor site. The Harvard TEACHH model investigated primary tumor type, performance status, age, prior chemotherapy courses, liver metastases, and hospitalization within 3 months. Cox multivariable analyses and logisticalregressionwere utilized to compare model performance. RESULTS: Number of active tumors, performance status, albumin, primary tumor site, prior hospitalizationwithin the last 3 months, and liver metastases predicted overall survival on uinvariate and multivariable analysis (p < 0.05 for all). The NEAT model separated patients into four prognostic groups with median survivals of 24.9, 14.8, 4.0, and 1.2 months, respectively (p < 0.001). The NEAT model had a C-index of 0.76 with a Nagelkerke’s R2 of 0.54 suggesting good discrimination, calibration and total performance compared to competing prognostic models. CONCLUSION: The NEAT model warrants further investigation as a clinically useful approach to predict survival in patients with stage IV cancer. |
format | Online Article Text |
id | pubmed-6192914 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Korean Cancer Association |
record_format | MEDLINE/PubMed |
spelling | pubmed-61929142018-10-24 The NEAT Predictive Model for Survival in Patients with Advanced Cancer Zucker, Amanda Tsai, Chiaojung Jillian Loscalzo, John Calves, Pedro Kao, Johnny Cancer Res Treat Original Article PURPOSE: We previously developed a model to more accurately predict life expectancy for stage IV cancer patients referred to radiation oncology. The goals of this study are to validate this model and to compare competing published models. MATERIALS AND METHODS: From May 2012 to March 2015, 280 consecutive patientswith stage IV cancerwere prospectively evaluated by a single radiation oncologist. Patients were separated into training, validation and combined sets. TheNEAT model evaluated number of active tumors (“N”), Eastern Cooperative Oncology Group performance status (“E”), albumin (“A”) and primary tumor site (“T”). The Odette Cancer Center model validated performance status, bone only metastases and primary tumor site. The Harvard TEACHH model investigated primary tumor type, performance status, age, prior chemotherapy courses, liver metastases, and hospitalization within 3 months. Cox multivariable analyses and logisticalregressionwere utilized to compare model performance. RESULTS: Number of active tumors, performance status, albumin, primary tumor site, prior hospitalizationwithin the last 3 months, and liver metastases predicted overall survival on uinvariate and multivariable analysis (p < 0.05 for all). The NEAT model separated patients into four prognostic groups with median survivals of 24.9, 14.8, 4.0, and 1.2 months, respectively (p < 0.001). The NEAT model had a C-index of 0.76 with a Nagelkerke’s R2 of 0.54 suggesting good discrimination, calibration and total performance compared to competing prognostic models. CONCLUSION: The NEAT model warrants further investigation as a clinically useful approach to predict survival in patients with stage IV cancer. Korean Cancer Association 2018-10 2018-01-24 /pmc/articles/PMC6192914/ /pubmed/29361815 http://dx.doi.org/10.4143/crt.2017.223 Text en Copyright © 2018 by the Korean Cancer Association This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Article Zucker, Amanda Tsai, Chiaojung Jillian Loscalzo, John Calves, Pedro Kao, Johnny The NEAT Predictive Model for Survival in Patients with Advanced Cancer |
title | The NEAT Predictive Model for Survival in Patients with Advanced Cancer |
title_full | The NEAT Predictive Model for Survival in Patients with Advanced Cancer |
title_fullStr | The NEAT Predictive Model for Survival in Patients with Advanced Cancer |
title_full_unstemmed | The NEAT Predictive Model for Survival in Patients with Advanced Cancer |
title_short | The NEAT Predictive Model for Survival in Patients with Advanced Cancer |
title_sort | neat predictive model for survival in patients with advanced cancer |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6192914/ https://www.ncbi.nlm.nih.gov/pubmed/29361815 http://dx.doi.org/10.4143/crt.2017.223 |
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