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A New Online Dynamic Nomogram: Construction and Validation of an Assistant Decision-Making Model for Laryngeal Squamous Cell Carcinoma

BACKGROUND: Laryngeal squamous cell carcinoma (LSCC) is the most common type of head and neck squamous cell carcinoma. However, there are currently no reliable biomarkers for the diagnosis and prognosis of LSCC. Thus, this study aimed to identify the independent risk factors and develop and validate...

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Autores principales: Liu, Yuchen, Han, Yanxun, Chen, Bangjie, Zhang, Jian, Yin, Siyue, Li, Dapeng, Wu, Yu, Jiang, Yuan, Wang, Xinyi, Wang, Jianpeng, Fu, Ziyue, Shen, Hailong, Ding, Zhao, Yao, Kun, Tao, Ye, Wu, Jing, Liu, Yehai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9204277/
https://www.ncbi.nlm.nih.gov/pubmed/35719922
http://dx.doi.org/10.3389/fonc.2022.829761
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author Liu, Yuchen
Han, Yanxun
Chen, Bangjie
Zhang, Jian
Yin, Siyue
Li, Dapeng
Wu, Yu
Jiang, Yuan
Wang, Xinyi
Wang, Jianpeng
Fu, Ziyue
Shen, Hailong
Ding, Zhao
Yao, Kun
Tao, Ye
Wu, Jing
Liu, Yehai
author_facet Liu, Yuchen
Han, Yanxun
Chen, Bangjie
Zhang, Jian
Yin, Siyue
Li, Dapeng
Wu, Yu
Jiang, Yuan
Wang, Xinyi
Wang, Jianpeng
Fu, Ziyue
Shen, Hailong
Ding, Zhao
Yao, Kun
Tao, Ye
Wu, Jing
Liu, Yehai
author_sort Liu, Yuchen
collection PubMed
description BACKGROUND: Laryngeal squamous cell carcinoma (LSCC) is the most common type of head and neck squamous cell carcinoma. However, there are currently no reliable biomarkers for the diagnosis and prognosis of LSCC. Thus, this study aimed to identify the independent risk factors and develop and validate a new dynamic web-based nomogram that can predict auxiliary laryngeal carcinogenesis. METHODS: Data on the medical history of 221 patients who were recently diagnosed with LSCC and 359 who were recently diagnosed with benign laryngeal lesions (BLLs) at the First Affiliated Hospital of Anhui Medical University were retrospectively reviewed. Using the bootstrap method, 580 patients were divided in a 7:3 ratio into a training cohort (LSCC, 158 patients; BLL, 250 patients) and an internal validation cohort (LSCC, 63 patients; BLL, 109 patients). In addition, a retrospective analysis of 31 patients with LSCC and 54 patients with BLL from Fuyang Hospital affiliated with Anhui Medical University was performed as an external validation cohort. In the training cohort, the relevant indices were initially screened using univariate analysis. Then, least absolute shrinkage and selection operator logistic analysis was used to evaluate the significant potential independent risk factors (P<0.05); a dynamic online diagnostic nomogram, whose discrimination was evaluated using the area under the ROC curve (AUC), was constructed, while the consistency was evaluated using calibration plots. Its clinical application was evaluated by performing a decision curve analysis (DCA) and validated by internal validation of the training set and external validation of the validation set. RESULTS: Five independent risk factors, sex (odds ratio [OR]: 6.779, P<0.001), age (OR: 9.257, P<0.001), smoking (OR: 2.321, P=0.005), red blood cell width distribution (OR: 2.698, P=0.001), albumin (OR: 0.487, P=0.012), were screened from the results of the multivariate logistic analysis of the training cohort and included in the LSCC diagnostic nomogram. The nomogram predicted LSCC with AUC values of 0.894 in the training cohort, 0.907 in the internal testing cohort, and 0.966 in the external validation cohort. The calibration curve also proved that the nomogram predicted outcomes were close to the ideal curve, the predicted outcomes were consistent with the real outcomes, and the DCA curve showed that all patients could benefit. This finding was also confirmed in the validation cohort. CONCLUSION: An online nomogram for LSCC was constructed with good predictive performance, which can be used as a practical approach for the personalized early screening and auxiliary diagnosis of the potential risk factors and assist physicians in making a personalized diagnosis and treatment for patients.
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spelling pubmed-92042772022-06-18 A New Online Dynamic Nomogram: Construction and Validation of an Assistant Decision-Making Model for Laryngeal Squamous Cell Carcinoma Liu, Yuchen Han, Yanxun Chen, Bangjie Zhang, Jian Yin, Siyue Li, Dapeng Wu, Yu Jiang, Yuan Wang, Xinyi Wang, Jianpeng Fu, Ziyue Shen, Hailong Ding, Zhao Yao, Kun Tao, Ye Wu, Jing Liu, Yehai Front Oncol Oncology BACKGROUND: Laryngeal squamous cell carcinoma (LSCC) is the most common type of head and neck squamous cell carcinoma. However, there are currently no reliable biomarkers for the diagnosis and prognosis of LSCC. Thus, this study aimed to identify the independent risk factors and develop and validate a new dynamic web-based nomogram that can predict auxiliary laryngeal carcinogenesis. METHODS: Data on the medical history of 221 patients who were recently diagnosed with LSCC and 359 who were recently diagnosed with benign laryngeal lesions (BLLs) at the First Affiliated Hospital of Anhui Medical University were retrospectively reviewed. Using the bootstrap method, 580 patients were divided in a 7:3 ratio into a training cohort (LSCC, 158 patients; BLL, 250 patients) and an internal validation cohort (LSCC, 63 patients; BLL, 109 patients). In addition, a retrospective analysis of 31 patients with LSCC and 54 patients with BLL from Fuyang Hospital affiliated with Anhui Medical University was performed as an external validation cohort. In the training cohort, the relevant indices were initially screened using univariate analysis. Then, least absolute shrinkage and selection operator logistic analysis was used to evaluate the significant potential independent risk factors (P<0.05); a dynamic online diagnostic nomogram, whose discrimination was evaluated using the area under the ROC curve (AUC), was constructed, while the consistency was evaluated using calibration plots. Its clinical application was evaluated by performing a decision curve analysis (DCA) and validated by internal validation of the training set and external validation of the validation set. RESULTS: Five independent risk factors, sex (odds ratio [OR]: 6.779, P<0.001), age (OR: 9.257, P<0.001), smoking (OR: 2.321, P=0.005), red blood cell width distribution (OR: 2.698, P=0.001), albumin (OR: 0.487, P=0.012), were screened from the results of the multivariate logistic analysis of the training cohort and included in the LSCC diagnostic nomogram. The nomogram predicted LSCC with AUC values of 0.894 in the training cohort, 0.907 in the internal testing cohort, and 0.966 in the external validation cohort. The calibration curve also proved that the nomogram predicted outcomes were close to the ideal curve, the predicted outcomes were consistent with the real outcomes, and the DCA curve showed that all patients could benefit. This finding was also confirmed in the validation cohort. CONCLUSION: An online nomogram for LSCC was constructed with good predictive performance, which can be used as a practical approach for the personalized early screening and auxiliary diagnosis of the potential risk factors and assist physicians in making a personalized diagnosis and treatment for patients. Frontiers Media S.A. 2022-05-26 /pmc/articles/PMC9204277/ /pubmed/35719922 http://dx.doi.org/10.3389/fonc.2022.829761 Text en Copyright © 2022 Liu, Han, Chen, Zhang, Yin, Li, Wu, Jiang, Wang, Wang, Fu, Shen, Ding, Yao, Tao, Wu and Liu 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 Oncology
Liu, Yuchen
Han, Yanxun
Chen, Bangjie
Zhang, Jian
Yin, Siyue
Li, Dapeng
Wu, Yu
Jiang, Yuan
Wang, Xinyi
Wang, Jianpeng
Fu, Ziyue
Shen, Hailong
Ding, Zhao
Yao, Kun
Tao, Ye
Wu, Jing
Liu, Yehai
A New Online Dynamic Nomogram: Construction and Validation of an Assistant Decision-Making Model for Laryngeal Squamous Cell Carcinoma
title A New Online Dynamic Nomogram: Construction and Validation of an Assistant Decision-Making Model for Laryngeal Squamous Cell Carcinoma
title_full A New Online Dynamic Nomogram: Construction and Validation of an Assistant Decision-Making Model for Laryngeal Squamous Cell Carcinoma
title_fullStr A New Online Dynamic Nomogram: Construction and Validation of an Assistant Decision-Making Model for Laryngeal Squamous Cell Carcinoma
title_full_unstemmed A New Online Dynamic Nomogram: Construction and Validation of an Assistant Decision-Making Model for Laryngeal Squamous Cell Carcinoma
title_short A New Online Dynamic Nomogram: Construction and Validation of an Assistant Decision-Making Model for Laryngeal Squamous Cell Carcinoma
title_sort new online dynamic nomogram: construction and validation of an assistant decision-making model for laryngeal squamous cell carcinoma
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9204277/
https://www.ncbi.nlm.nih.gov/pubmed/35719922
http://dx.doi.org/10.3389/fonc.2022.829761
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