Prognostic nomograms for predicting overall survival and cancer-specific survival of patients with major salivary gland mucoepidermoid carcinoma

Jia Qian Hu, Peng Cheng Yu, Xiao Shi, Wan Lin Liu, Ting Ting Zhang, Bo Wen Lei, Nai Si Huang, Wei Bo Xu, Li Tao Han, Ben Ma, Tian Liao, Wen Jun Wei, Yu Wang, Zhong Wu Lu, Yu Long Wang, Qing Hai Ji

Research output: Contribution to journalArticle

Abstract

Background: The aim of this study was to develop and validate prognostic nomograms predicting overall (OS) and cancer-specific survival (CSS) of patients with major salivary gland (MaSG) mucoepidermoid carcinoma (MEC). Methods: 1398 MaSG-MEC patients were identified from the Surveillance, Epidemiology and End Results (SEER) database. They were randomly and equally divided into a training cohort (n=699) and a validation cohort (n=699). The best subsets of covariates were identified to develop nomograms predicting OS and CSS based on the smallest Akaike Information Criterion (AIC) value in the multivariate Cox models. The nomograms were internally and externally validated by the bootstrap resampling method. The predictive ability was evaluated by Harrell’s Concordance Index (C-index). Results: For the training cohort, eight (age at diagnosis, tumor grade, primary site, surgery, radiation, T, N and M classification) and seven predictors (all the above factors except primary site) were selected to create the nomograms estimating the 3- and 5- year OS and CSS, respectively. C-index indicated better predictive performance of the nomograms than the 7th AJCC staging system, which was confirmed by both internal (via the training cohort: OS: 0.888 vs 0.785, CSS: 0.938 vs 0.821) and external validation (via the validation cohort: OS: 0.844 vs 0.743, CSS: 0.882 vs 0.787). The calibration plots also revealed good agreements between the nomogram-based prediction and observed survival. Conclusions: We have proposed and validated the nomograms predicting OS and CSS of MaSG-MEC. They are proved to be of higher predictive value than the AJCC staging system and may be adopted in future clinical practice.

Original languageEnglish (US)
Pages (from-to)4380-4388
Number of pages9
JournalJournal of Cancer
Volume10
Issue number18
DOIs
StatePublished - Jan 1 2019

Fingerprint

Mucoepidermoid Carcinoma
Nomograms
Salivary Glands
Survival
Neoplasms
Proportional Hazards Models
Calibration
Epidemiology
Databases
Radiation

Keywords

  • C-index
  • Cancer-specific survival
  • Major salivary gland
  • Mucoepidermoid carcinoma
  • Nomogram
  • Overall survival

ASJC Scopus subject areas

  • Oncology

Cite this

Prognostic nomograms for predicting overall survival and cancer-specific survival of patients with major salivary gland mucoepidermoid carcinoma. / Hu, Jia Qian; Yu, Peng Cheng; Shi, Xiao; Liu, Wan Lin; Zhang, Ting Ting; Lei, Bo Wen; Huang, Nai Si; Xu, Wei Bo; Han, Li Tao; Ma, Ben; Liao, Tian; Wei, Wen Jun; Wang, Yu; Lu, Zhong Wu; Wang, Yu Long; Ji, Qing Hai.

In: Journal of Cancer, Vol. 10, No. 18, 01.01.2019, p. 4380-4388.

Research output: Contribution to journalArticle

Hu, JQ, Yu, PC, Shi, X, Liu, WL, Zhang, TT, Lei, BW, Huang, NS, Xu, WB, Han, LT, Ma, B, Liao, T, Wei, WJ, Wang, Y, Lu, ZW, Wang, YL & Ji, QH 2019, 'Prognostic nomograms for predicting overall survival and cancer-specific survival of patients with major salivary gland mucoepidermoid carcinoma', Journal of Cancer, vol. 10, no. 18, pp. 4380-4388. https://doi.org/10.7150/jca.27992
Hu, Jia Qian ; Yu, Peng Cheng ; Shi, Xiao ; Liu, Wan Lin ; Zhang, Ting Ting ; Lei, Bo Wen ; Huang, Nai Si ; Xu, Wei Bo ; Han, Li Tao ; Ma, Ben ; Liao, Tian ; Wei, Wen Jun ; Wang, Yu ; Lu, Zhong Wu ; Wang, Yu Long ; Ji, Qing Hai. / Prognostic nomograms for predicting overall survival and cancer-specific survival of patients with major salivary gland mucoepidermoid carcinoma. In: Journal of Cancer. 2019 ; Vol. 10, No. 18. pp. 4380-4388.
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abstract = "Background: The aim of this study was to develop and validate prognostic nomograms predicting overall (OS) and cancer-specific survival (CSS) of patients with major salivary gland (MaSG) mucoepidermoid carcinoma (MEC). Methods: 1398 MaSG-MEC patients were identified from the Surveillance, Epidemiology and End Results (SEER) database. They were randomly and equally divided into a training cohort (n=699) and a validation cohort (n=699). The best subsets of covariates were identified to develop nomograms predicting OS and CSS based on the smallest Akaike Information Criterion (AIC) value in the multivariate Cox models. The nomograms were internally and externally validated by the bootstrap resampling method. The predictive ability was evaluated by Harrell’s Concordance Index (C-index). Results: For the training cohort, eight (age at diagnosis, tumor grade, primary site, surgery, radiation, T, N and M classification) and seven predictors (all the above factors except primary site) were selected to create the nomograms estimating the 3- and 5- year OS and CSS, respectively. C-index indicated better predictive performance of the nomograms than the 7th AJCC staging system, which was confirmed by both internal (via the training cohort: OS: 0.888 vs 0.785, CSS: 0.938 vs 0.821) and external validation (via the validation cohort: OS: 0.844 vs 0.743, CSS: 0.882 vs 0.787). The calibration plots also revealed good agreements between the nomogram-based prediction and observed survival. Conclusions: We have proposed and validated the nomograms predicting OS and CSS of MaSG-MEC. They are proved to be of higher predictive value than the AJCC staging system and may be adopted in future clinical practice.",
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T1 - Prognostic nomograms for predicting overall survival and cancer-specific survival of patients with major salivary gland mucoepidermoid carcinoma

AU - Hu, Jia Qian

AU - Yu, Peng Cheng

AU - Shi, Xiao

AU - Liu, Wan Lin

AU - Zhang, Ting Ting

AU - Lei, Bo Wen

AU - Huang, Nai Si

AU - Xu, Wei Bo

AU - Han, Li Tao

AU - Ma, Ben

AU - Liao, Tian

AU - Wei, Wen Jun

AU - Wang, Yu

AU - Lu, Zhong Wu

AU - Wang, Yu Long

AU - Ji, Qing Hai

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N2 - Background: The aim of this study was to develop and validate prognostic nomograms predicting overall (OS) and cancer-specific survival (CSS) of patients with major salivary gland (MaSG) mucoepidermoid carcinoma (MEC). Methods: 1398 MaSG-MEC patients were identified from the Surveillance, Epidemiology and End Results (SEER) database. They were randomly and equally divided into a training cohort (n=699) and a validation cohort (n=699). The best subsets of covariates were identified to develop nomograms predicting OS and CSS based on the smallest Akaike Information Criterion (AIC) value in the multivariate Cox models. The nomograms were internally and externally validated by the bootstrap resampling method. The predictive ability was evaluated by Harrell’s Concordance Index (C-index). Results: For the training cohort, eight (age at diagnosis, tumor grade, primary site, surgery, radiation, T, N and M classification) and seven predictors (all the above factors except primary site) were selected to create the nomograms estimating the 3- and 5- year OS and CSS, respectively. C-index indicated better predictive performance of the nomograms than the 7th AJCC staging system, which was confirmed by both internal (via the training cohort: OS: 0.888 vs 0.785, CSS: 0.938 vs 0.821) and external validation (via the validation cohort: OS: 0.844 vs 0.743, CSS: 0.882 vs 0.787). The calibration plots also revealed good agreements between the nomogram-based prediction and observed survival. Conclusions: We have proposed and validated the nomograms predicting OS and CSS of MaSG-MEC. They are proved to be of higher predictive value than the AJCC staging system and may be adopted in future clinical practice.

AB - Background: The aim of this study was to develop and validate prognostic nomograms predicting overall (OS) and cancer-specific survival (CSS) of patients with major salivary gland (MaSG) mucoepidermoid carcinoma (MEC). Methods: 1398 MaSG-MEC patients were identified from the Surveillance, Epidemiology and End Results (SEER) database. They were randomly and equally divided into a training cohort (n=699) and a validation cohort (n=699). The best subsets of covariates were identified to develop nomograms predicting OS and CSS based on the smallest Akaike Information Criterion (AIC) value in the multivariate Cox models. The nomograms were internally and externally validated by the bootstrap resampling method. The predictive ability was evaluated by Harrell’s Concordance Index (C-index). Results: For the training cohort, eight (age at diagnosis, tumor grade, primary site, surgery, radiation, T, N and M classification) and seven predictors (all the above factors except primary site) were selected to create the nomograms estimating the 3- and 5- year OS and CSS, respectively. C-index indicated better predictive performance of the nomograms than the 7th AJCC staging system, which was confirmed by both internal (via the training cohort: OS: 0.888 vs 0.785, CSS: 0.938 vs 0.821) and external validation (via the validation cohort: OS: 0.844 vs 0.743, CSS: 0.882 vs 0.787). The calibration plots also revealed good agreements between the nomogram-based prediction and observed survival. Conclusions: We have proposed and validated the nomograms predicting OS and CSS of MaSG-MEC. They are proved to be of higher predictive value than the AJCC staging system and may be adopted in future clinical practice.

KW - C-index

KW - Cancer-specific survival

KW - Major salivary gland

KW - Mucoepidermoid carcinoma

KW - Nomogram

KW - Overall survival

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