Development and validation of a nomogram for predicting long-term overall survival in nasopharyngeal carcinoma: A population-based study.
Journal
Medicine
ISSN: 1536-5964
Titre abrégé: Medicine (Baltimore)
Pays: United States
ID NLM: 2985248R
Informations de publication
Date de publication:
Jan 2020
Jan 2020
Historique:
entrez:
25
1
2020
pubmed:
25
1
2020
medline:
1
2
2020
Statut:
ppublish
Résumé
We aimed to develop a nomogram based on a population-based cohort to estimate the individualized overall survival (OS) for patients with nasopharyngeal carcinoma (NPC) and compare its predictive value with that of the traditional staging system.Data for 3693 patients with NPC were extracted from the Surveillance, Epidemiology, and End Results dataset and randomly divided into two sets: training (n = 2585) and validation (n = 1108). On the basis of multivariate Cox regression analysis, a nomogram was constructed to predict the 3-, 5-, and 10-year survival probability for a patient. The performance of the nomogram was quantified with respect to discrimination, calibration, and clinical utility.In the training set, age, sex, race, marital status, histological type, T stage, N stage, M stage, radiotherapy, and chemotherapy were selected to develop a nomogram for predicting the OS probability based on the multivariate Cox regression model. The nomogram was generally more discriminative compared with the American Joint Committee on Cancer 7th staging system. Calibration plots exhibited an excellent consistency between the observed probability and the nomogram's prediction. Categorical net classification improvement and integrated discrimination improvement suggested that the predictive accuracy of the nomogram exceeded that of the classic staging system. With respect to decision curve analyses, the nomogram exhibited preferable net benefit gains than the staging system across a wide range of threshold probabilities.This proposed nomogram exhibits an excellent performance with regard to its predictive accuracy, discrimination capability, and clinical utility, and thus can be used as a convenient and reliable tool for prognosis prediction in patients with NPC.
Identifiants
pubmed: 31977914
doi: 10.1097/MD.0000000000018974
pii: 00005792-202001240-00073
pmc: PMC7004579
doi:
Types de publication
Journal Article
Validation Study
Langues
eng
Sous-ensembles de citation
IM
Pagination
e18974Références
Tang LL, Chen WQ, Xue WQ, et al. Global trends in incidence and mortality of nasopharyngeal carcinoma. Cancer Lett 2016;374:22–30.
Chua MLK, Wee JTS, Hui EP, et al. Nasopharyngeal carcinoma. Lancet 2016;387:1012–24.
Lee AW, Ma BB, Ng WT, et al. Management of nasopharyngeal carcinoma: current practice and future perspective. J Clin Oncol 2015;33:3356–64.
Lee AW, Fee WE, Ng WT, et al. Nasopharyngeal carcinoma: salvage of local recurrence. Oral Oncol 2012;48:768–74.
Lam KO, Lee AW, Choi CW, et al. Global pattern of nasopharyngeal cancer: correlation of outcome with access to radiation therapy. Int J Radiat Oncol Biol Phys 2016;94:1106–12.
Tan WL, Tan EH, Lim DW, et al. Advanced in systemic treatment for nasopharyngeal carcinoma. Chin Clin Oncol 2016;5:21.
Ren Y, Qiu H, Yuan Y, et al. Evaluation of 7th edition of AJCC staging system for nasopharyngeal carcinoma. J Cancer 2017;8:1665–72.
Lee AWM, Ng WT, Chan LK, et al. The strength/weakness of the AJCC/UICC staging system (7th edition) for nasopharyngeal cancer and suggestion for future improvement. Oral Oncol 2012;48:1007–13.
Dong F, Shen Y, Gao F, et al. Nomograms to predict individual prognosis of patients with primary small cell carcinoma of the bladder. J Cancer 2018;9:1152–64.
Balachandran VP, Gonen M, Smith JJ, et al. Nomograms in oncology: more than meets the eye. Lancet Oncol 2015;16:e173–80.
Liang W, Shen G, Zhang Y, et al. Development and validation of a nomogram for predicting the survival of patients with non-metastatic nasopharyngeal carcinoma after curative treatment. Chin J Cancer 2016;35:98.
Luo R, Li M, Yang Z, et al. Nomogram for radiation-induced hypothyroidism prediction in nasopharyngeal carcinoma after treatment. Br J Radiol 2017;90: doi: 10.1259/bjr.20160686.
doi: 10.1259/bjr.20160686
Wu S, Xia B, Han F, et al. Prognostic nomogram for patients with nasopharyngeal carcinoma after intensity-modulated radiotherapy. PLoS One 2015;10:e0134491.
National Cancer Institute. Surveillance, Epidemiology, and End Results Program. Available at http://seer.cancer.gov. Assessed May 5, 2019.
Fritz A, Percy C, Jack A, et al. International Classification of Disease for Oncology. 3rd ed.Geneva, Switzerland: World Health Organization; 2000.
Harrell FE, Lee KL, Mark DB. Multivariate prognostic models: issues in developing models, evaluation assumptions and adequacy, and measuring and reducing errors. Stat Med 1996;15:361–87.
Thomas LE, O’Brien EC, Piccini JP, et al. Application of net reclassification index to non-nested and point-based risk prediction models: a review. Eur Heart 2018;doi: 10.1093/eurheartj/ehy345.
doi: 10.1093/eurheartj/ehy345
Pencina MJ, D’Agostino RB Sr, D’Agostino RB Jr, et al. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med 2008;27:207–12.
Alba AC, Agoritsas T, Walsh M, et al. Discrimination and calibration of clinical prediction models: users’ guides to the medical literature. JAMA 2017;318:1377–84.
Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making 2006;26:565–74.
Vickers AJ, Cronin AM, Elkin EB, et al. Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers. BMC Med Inform Decis Making 2008;8:53.
Roupret M, Hupertan V, Seisen T, et al. Prediction of cancer specific survival after radical nephroureterectomy for upper tract urothelial carcinoma: development of an optimized postoperative nomogram using decision curve analysis. J Urol 2013;189:1662–9.
Pan Z, You H, Bu Q, et al. Development and validation of a nomogram for predicting cancer-specific survival in patients with Wilms’ tumor. J Cancer 2019;10:5299–305.
Wang C, Yang C, Wang W, et al. A prognostic nomogram for cervical cancer after surgery from SEER Database. J Cancer 2018;9:3923–8.
Wu SG, Liao XL, He ZY, et al. Demographic and clinicopathological characteristics of nasopharyngeal carcinoma and survival outcomes according to age at diagnosis: a population-based analysis. Oral Oncol 2017;73:83–7.
Huang SJ, Tang YY, Liu HM, et al. Impact of age on survival of locoregional nasopharyngeal carcinoma: an analysis of the Surveillance, Epidemiology, and End Results program database. Clin Otolaryngol 2018;43:1209–18.
Wang Y, Zhang Y, Ma S. Racial differences in nasopharyngeal carcinoma in the United States. Cancer Epidemiol 2013;37:793–802.
Xu C, Liu X, Chen YP, et al. Impact of marital status at diagnosis on survival and its change over time between 1973 and 2012 in patients with nasopharyngeal carcinoma: a propensity score-matched analysis. Cancer Med 2017;6:3040–51.
Wei WI, Sham JS. Nasopharyngeal carcinoma. Lancet 2005;365:2041–54.
Tang LQ, Li CF, Li J, et al. Establishment and validation of prognostic nomograms for endemic nasopharyngeal carcinoma. J Natl Cancer Inst 2015;108: doi: 10.1093/jnci/djv291.
doi: 10.1093/jnci/djv291
Cho JK, Lee GJ, Yi KI, et al. Development and external validation of nomograms predictive of response to radiation therapy and overall survival in nasopharyngeal cancer patients. Eur J Cancer 2015;51:1303–11.
Huang XD, Zhou GQ, Lv JW, et al. Competing risk nomograms for nasopharyngeal carcinoma in the intensity-modulated radiotherapy era: a big-data, intelligence platform-based analysis. Radiother Oncol 2018;129:389–95.