Zhigong Wei1, Zhuang Zhang2, Lianlian Yang1, Ling He1, Zheran Liu1, Yan He1, Jingjing Wang1, Xiaoli Mu1, Ruidan Li1, Yan Huang1, Xingchen Peng1. 1. Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China. 2. State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, Department of Head and Neck Oncology, West China Hospital of Stomatology, Sichuan University, Chengdu, China.
Abstract
BACKGROUND: To develop nomograms for predicting recurrence risk and long-term survival in patients with parotid gland cancer (PGC). METHODS: A total of 301 consecutive patients with PGC who underwent surgery were enrolled and randomly divided into a training cohort (n=210) and a validation cohort (n=91). Predictive nomograms were constructed based on the independent indicators of overall survival (OS) and disease-free survival (DFS) as determined by multivariate Cox regression analysis. The discrimination and calibration of nomograms were evaluated using C-indices and calibration curves. RESULTS: Six independent predictors of OS were identified. Incorporating these factors, the nomogram showed good concordance statistics of 0.84 and 0.78 in predicting the 5-year OS in the training and validation cohorts. Five independent predictors of DFS were identified and integrated into the nomogram. The concordance statistics were 0.84 and 0.74 in predicting the 5-year DFS in the training and validation cohorts. The predictive performance of the nomograms outperformed the TNM model. Additionally, the patients were divided into two groups according to the nomogram score, and significant differences in OS and DFS were observed between the high risk and low risk groups. Finally, the role of postoperative treatments was evaluated based on the risk stratification; patients at high risk of disease recurrence showed an improvement in DFS after receiving postoperative treatments. CONCLUSIONS: The nomogram showed good performance in predicting both OS and DFS in patients with PGC. It might be useful for selecting patients for postoperative treatments. 2021 Gland Surgery. All rights reserved.
BACKGROUND: To develop nomograms for predicting recurrence risk and long-term survival in patients with parotid gland cancer (PGC). METHODS: A total of 301 consecutive patients with PGC who underwent surgery were enrolled and randomly divided into a training cohort (n=210) and a validation cohort (n=91). Predictive nomograms were constructed based on the independent indicators of overall survival (OS) and disease-free survival (DFS) as determined by multivariate Cox regression analysis. The discrimination and calibration of nomograms were evaluated using C-indices and calibration curves. RESULTS: Six independent predictors of OS were identified. Incorporating these factors, the nomogram showed good concordance statistics of 0.84 and 0.78 in predicting the 5-year OS in the training and validation cohorts. Five independent predictors of DFS were identified and integrated into the nomogram. The concordance statistics were 0.84 and 0.74 in predicting the 5-year DFS in the training and validation cohorts. The predictive performance of the nomograms outperformed the TNM model. Additionally, the patients were divided into two groups according to the nomogram score, and significant differences in OS and DFS were observed between the high risk and low risk groups. Finally, the role of postoperative treatments was evaluated based on the risk stratification; patients at high risk of disease recurrence showed an improvement in DFS after receiving postoperative treatments. CONCLUSIONS: The nomogram showed good performance in predicting both OS and DFS in patients with PGC. It might be useful for selecting patients for postoperative treatments. 2021 Gland Surgery. All rights reserved.
Entities:
Keywords:
Parotid gland cancer (PGC); nomogram; prognosis; survival
Authors: Chris H J Terhaard; Herman Lubsen; Coen R N Rasch; Peter C Levendag; Hans H A M Kaanders; Reineke E Tjho-Heslinga; Piet L A van Den Ende; Fred Burlage Journal: Int J Radiat Oncol Biol Phys Date: 2005-01-01 Impact factor: 7.038
Authors: Peter Zbären; Jonas Schüpbach; Michel Nuyens; Edouard Stauffer; Richard Greiner; Rudolf Häusler Journal: Am J Surg Date: 2003-07 Impact factor: 2.565
Authors: Vincent Vander Poorten; Augustinus Hart; Tom Vauterin; Gert Jeunen; Joseph Schoenaers; Marc Hamoir; Alphonsus Balm; Eberhard Stennert; Orlando Guntinas-Lichius; Pierre Delaere Journal: Cancer Date: 2009-02-01 Impact factor: 6.860
Authors: Safina Ali; Frank L Palmer; Changhong Yu; Monica DiLorenzo; Jatin P Shah; Michael W Kattan; Snehal G Patel; Ian Ganly Journal: Ann Surg Oncol Date: 2013-10-17 Impact factor: 5.344
Authors: Breanne E Terakedis; Jason P Hunt; Luke O Buchmann; Vilija N Avizonis; Christopher J Anker; Ying J Hitchcock Journal: Am J Clin Oncol Date: 2017-06 Impact factor: 2.339