Cargando…
A Nomogram Predicting the Prognosis of Renal Cell Carcinoma Patients with Lung Metastases
BACKGROUND: The optimal tool for predicting the survival of renal cell carcinoma (RCC) patients with lung metastases remains controversial. METHODS: We selected patients diagnosed with RCC and lung metastases, from 2010 to 2015, from the Surveillance, Epidemiology, and End Results (SEER) database. A...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7997741/ https://www.ncbi.nlm.nih.gov/pubmed/33791367 http://dx.doi.org/10.1155/2021/6627562 |
_version_ | 1783670395149221888 |
---|---|
author | Sheng, Xinyu Lu, Xuan Wu, Jian Chen, Lu Cao, Hongcui |
author_facet | Sheng, Xinyu Lu, Xuan Wu, Jian Chen, Lu Cao, Hongcui |
author_sort | Sheng, Xinyu |
collection | PubMed |
description | BACKGROUND: The optimal tool for predicting the survival of renal cell carcinoma (RCC) patients with lung metastases remains controversial. METHODS: We selected patients diagnosed with RCC and lung metastases, from 2010 to 2015, from the Surveillance, Epidemiology, and End Results (SEER) database. After the selection of inclusion criteria and exclusion criterion, the rest of the patients were incorporated into model analysis. Least absolute shrinkage and selection operator (LASSO) regression was used to select the most important features for construction of a nomogram predicting cancer-specific survival. A calibration plot and the concordance index (C-index) were used to estimate nomogram efficacy in a validation cohort. The association between important factors selected by LASSO regression, and prognosis was assessed by the Kaplan-Meier (KM) survival curve. The receiver operating characteristic (ROC) curves were drawn to compare sensitivity and specificity between the nomogram we built and the TNM stage-based model. RESULTS: A total of 1,369 patients met the inclusion criteria, but not the exclusion criteria. The LASSO regression model reduced 15 features to seven potential predictors of survival, including tumor grade, the extent of surgery, N and T status, histological profile, and brain and bone metastasis status. Such features had good discrimination in the KM survival curves. The nomogram showed excellent discriminatory power (C-index, 0.71; 95% confidence interval: 0.70 to 0.72) and good calibration in terms of both 1- and 2-year cancer-specific survival. The nomogram showed great discriminatory power (C-index 0.68) and adequate calibration when applied to the validation cohort. The areas under the curve (AUCs) of nomogram were 0.767 and 0.780, respectively, and the AUCs of TNM stage were 0.617 and 0.618 at 1 and 2 years, respectively. CONCLUSIONS: Our nomogram might play a major role in predicting the cancer-specific survival of RCC patients with lung metastases. |
format | Online Article Text |
id | pubmed-7997741 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-79977412021-03-30 A Nomogram Predicting the Prognosis of Renal Cell Carcinoma Patients with Lung Metastases Sheng, Xinyu Lu, Xuan Wu, Jian Chen, Lu Cao, Hongcui Biomed Res Int Research Article BACKGROUND: The optimal tool for predicting the survival of renal cell carcinoma (RCC) patients with lung metastases remains controversial. METHODS: We selected patients diagnosed with RCC and lung metastases, from 2010 to 2015, from the Surveillance, Epidemiology, and End Results (SEER) database. After the selection of inclusion criteria and exclusion criterion, the rest of the patients were incorporated into model analysis. Least absolute shrinkage and selection operator (LASSO) regression was used to select the most important features for construction of a nomogram predicting cancer-specific survival. A calibration plot and the concordance index (C-index) were used to estimate nomogram efficacy in a validation cohort. The association between important factors selected by LASSO regression, and prognosis was assessed by the Kaplan-Meier (KM) survival curve. The receiver operating characteristic (ROC) curves were drawn to compare sensitivity and specificity between the nomogram we built and the TNM stage-based model. RESULTS: A total of 1,369 patients met the inclusion criteria, but not the exclusion criteria. The LASSO regression model reduced 15 features to seven potential predictors of survival, including tumor grade, the extent of surgery, N and T status, histological profile, and brain and bone metastasis status. Such features had good discrimination in the KM survival curves. The nomogram showed excellent discriminatory power (C-index, 0.71; 95% confidence interval: 0.70 to 0.72) and good calibration in terms of both 1- and 2-year cancer-specific survival. The nomogram showed great discriminatory power (C-index 0.68) and adequate calibration when applied to the validation cohort. The areas under the curve (AUCs) of nomogram were 0.767 and 0.780, respectively, and the AUCs of TNM stage were 0.617 and 0.618 at 1 and 2 years, respectively. CONCLUSIONS: Our nomogram might play a major role in predicting the cancer-specific survival of RCC patients with lung metastases. Hindawi 2021-03-18 /pmc/articles/PMC7997741/ /pubmed/33791367 http://dx.doi.org/10.1155/2021/6627562 Text en Copyright © 2021 Xinyu Sheng et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Sheng, Xinyu Lu, Xuan Wu, Jian Chen, Lu Cao, Hongcui A Nomogram Predicting the Prognosis of Renal Cell Carcinoma Patients with Lung Metastases |
title | A Nomogram Predicting the Prognosis of Renal Cell Carcinoma Patients with Lung Metastases |
title_full | A Nomogram Predicting the Prognosis of Renal Cell Carcinoma Patients with Lung Metastases |
title_fullStr | A Nomogram Predicting the Prognosis of Renal Cell Carcinoma Patients with Lung Metastases |
title_full_unstemmed | A Nomogram Predicting the Prognosis of Renal Cell Carcinoma Patients with Lung Metastases |
title_short | A Nomogram Predicting the Prognosis of Renal Cell Carcinoma Patients with Lung Metastases |
title_sort | nomogram predicting the prognosis of renal cell carcinoma patients with lung metastases |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7997741/ https://www.ncbi.nlm.nih.gov/pubmed/33791367 http://dx.doi.org/10.1155/2021/6627562 |
work_keys_str_mv | AT shengxinyu anomogrampredictingtheprognosisofrenalcellcarcinomapatientswithlungmetastases AT luxuan anomogrampredictingtheprognosisofrenalcellcarcinomapatientswithlungmetastases AT wujian anomogrampredictingtheprognosisofrenalcellcarcinomapatientswithlungmetastases AT chenlu anomogrampredictingtheprognosisofrenalcellcarcinomapatientswithlungmetastases AT caohongcui anomogrampredictingtheprognosisofrenalcellcarcinomapatientswithlungmetastases AT shengxinyu nomogrampredictingtheprognosisofrenalcellcarcinomapatientswithlungmetastases AT luxuan nomogrampredictingtheprognosisofrenalcellcarcinomapatientswithlungmetastases AT wujian nomogrampredictingtheprognosisofrenalcellcarcinomapatientswithlungmetastases AT chenlu nomogrampredictingtheprognosisofrenalcellcarcinomapatientswithlungmetastases AT caohongcui nomogrampredictingtheprognosisofrenalcellcarcinomapatientswithlungmetastases |