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A nomogram for predicting survival in patients with skin non-keratinizing large cell squamous cell carcinoma: A study based on the Surveillance, Epidemiology, and End Results database

INTRODUCTION: This study aimed to develop and validate a nomogram for predicting cancer-specific survival (CSS) in patients with non-keratinized large cell squamous cell carcinoma (NKLCSCC) at 3, 5, and 8 years after diagnosis. METHODS: Data on SCC patients were collected from the Surveillance, Epid...

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Autores principales: Zhang, Jinrong, Yang, Wei, Lian, Chengxiang, Zhao, Qiqi, Ming, Wai-kit, Ip, Cheong Cheong, Mu, Hsin-Hua, Ching Tom, Kong, Lyu, Jun, Deng, Liehua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9983752/
https://www.ncbi.nlm.nih.gov/pubmed/36873873
http://dx.doi.org/10.3389/fmed.2023.1082402
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author Zhang, Jinrong
Yang, Wei
Lian, Chengxiang
Zhao, Qiqi
Ming, Wai-kit
Ip, Cheong Cheong
Mu, Hsin-Hua
Ching Tom, Kong
Lyu, Jun
Deng, Liehua
author_facet Zhang, Jinrong
Yang, Wei
Lian, Chengxiang
Zhao, Qiqi
Ming, Wai-kit
Ip, Cheong Cheong
Mu, Hsin-Hua
Ching Tom, Kong
Lyu, Jun
Deng, Liehua
author_sort Zhang, Jinrong
collection PubMed
description INTRODUCTION: This study aimed to develop and validate a nomogram for predicting cancer-specific survival (CSS) in patients with non-keratinized large cell squamous cell carcinoma (NKLCSCC) at 3, 5, and 8 years after diagnosis. METHODS: Data on SCC patients were collected from the Surveillance, Epidemiology, and End Results database. Training (70%) and validation (30%) cohorts were generated using random selection of patients. Independent prognostic factors were selected using the backward stepwise Cox regression model. To predict the CSS rates in patients with NKLCSCC at 3, 5, and 8 years after diagnosis, all of the factors were incorporated into the nomogram. Indicators such as the concordance index (C-index), area under the time-dependent receiver operating characteristic curve (AUC), net reclassification index (NRI), integrated discrimination improvement (IDI), calibration curve, and decision-curve analysis (DCA) were then used to validate the performance of the nomogram. RESULTS: This study included 9,811 patients with NKLCSCC. Twelve prognostic factors were identified by Cox regression analysis in the training cohort, which were age, number of regional nodes examined, number of positive regional nodes, sex, race, marital status, American Joint Committee on Cancer (AJCC) stage, surgery status, chemotherapy status, radiotherapy status, summary stage, and income. The constructed nomogram was validated both internally and externally. The nomogram had good discrimination ability, as indicated by the comparatively high C-indices and AUC values. The nomogram was properly calibrated, as indicated by the calibration curves. Our nomogram was superior to the AJCC model, as illustrated by its superior NRI and IDI values. DCA curves indicated the clinical usability of the nomogram. CONCLUSION: The first nomogram for prognosis predictions of patients with NKLCSCC has been developed and verified. Its performance and usability demonstrated that the nomogram could be utilized in clinical settings. However, additional external verification is still required.
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spelling pubmed-99837522023-03-04 A nomogram for predicting survival in patients with skin non-keratinizing large cell squamous cell carcinoma: A study based on the Surveillance, Epidemiology, and End Results database Zhang, Jinrong Yang, Wei Lian, Chengxiang Zhao, Qiqi Ming, Wai-kit Ip, Cheong Cheong Mu, Hsin-Hua Ching Tom, Kong Lyu, Jun Deng, Liehua Front Med (Lausanne) Medicine INTRODUCTION: This study aimed to develop and validate a nomogram for predicting cancer-specific survival (CSS) in patients with non-keratinized large cell squamous cell carcinoma (NKLCSCC) at 3, 5, and 8 years after diagnosis. METHODS: Data on SCC patients were collected from the Surveillance, Epidemiology, and End Results database. Training (70%) and validation (30%) cohorts were generated using random selection of patients. Independent prognostic factors were selected using the backward stepwise Cox regression model. To predict the CSS rates in patients with NKLCSCC at 3, 5, and 8 years after diagnosis, all of the factors were incorporated into the nomogram. Indicators such as the concordance index (C-index), area under the time-dependent receiver operating characteristic curve (AUC), net reclassification index (NRI), integrated discrimination improvement (IDI), calibration curve, and decision-curve analysis (DCA) were then used to validate the performance of the nomogram. RESULTS: This study included 9,811 patients with NKLCSCC. Twelve prognostic factors were identified by Cox regression analysis in the training cohort, which were age, number of regional nodes examined, number of positive regional nodes, sex, race, marital status, American Joint Committee on Cancer (AJCC) stage, surgery status, chemotherapy status, radiotherapy status, summary stage, and income. The constructed nomogram was validated both internally and externally. The nomogram had good discrimination ability, as indicated by the comparatively high C-indices and AUC values. The nomogram was properly calibrated, as indicated by the calibration curves. Our nomogram was superior to the AJCC model, as illustrated by its superior NRI and IDI values. DCA curves indicated the clinical usability of the nomogram. CONCLUSION: The first nomogram for prognosis predictions of patients with NKLCSCC has been developed and verified. Its performance and usability demonstrated that the nomogram could be utilized in clinical settings. However, additional external verification is still required. Frontiers Media S.A. 2023-02-17 /pmc/articles/PMC9983752/ /pubmed/36873873 http://dx.doi.org/10.3389/fmed.2023.1082402 Text en Copyright © 2023 Zhang, Yang, Lian, Zhao, Ming, Ip, Mu, Ching Tom, Lyu and Deng. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Medicine
Zhang, Jinrong
Yang, Wei
Lian, Chengxiang
Zhao, Qiqi
Ming, Wai-kit
Ip, Cheong Cheong
Mu, Hsin-Hua
Ching Tom, Kong
Lyu, Jun
Deng, Liehua
A nomogram for predicting survival in patients with skin non-keratinizing large cell squamous cell carcinoma: A study based on the Surveillance, Epidemiology, and End Results database
title A nomogram for predicting survival in patients with skin non-keratinizing large cell squamous cell carcinoma: A study based on the Surveillance, Epidemiology, and End Results database
title_full A nomogram for predicting survival in patients with skin non-keratinizing large cell squamous cell carcinoma: A study based on the Surveillance, Epidemiology, and End Results database
title_fullStr A nomogram for predicting survival in patients with skin non-keratinizing large cell squamous cell carcinoma: A study based on the Surveillance, Epidemiology, and End Results database
title_full_unstemmed A nomogram for predicting survival in patients with skin non-keratinizing large cell squamous cell carcinoma: A study based on the Surveillance, Epidemiology, and End Results database
title_short A nomogram for predicting survival in patients with skin non-keratinizing large cell squamous cell carcinoma: A study based on the Surveillance, Epidemiology, and End Results database
title_sort nomogram for predicting survival in patients with skin non-keratinizing large cell squamous cell carcinoma: a study based on the surveillance, epidemiology, and end results database
topic Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9983752/
https://www.ncbi.nlm.nih.gov/pubmed/36873873
http://dx.doi.org/10.3389/fmed.2023.1082402
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