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Predicting survival in patients with buccal cancer: A study based on SEER database and external validation in China
- Source :
- Cancer Medicine, Vol 13, Iss 3, Pp n/a-n/a (2024)
- Publication Year :
- 2024
- Publisher :
- Wiley, 2024.
-
Abstract
- Abstract Objective Buccal mucosa cancer (BMC) is one of the most common oral cancers and has poor prognosis. The study aimed to develop and validate nomograms for predicting the 1‐, 3‐, and 5‐year overall survival (OS) and cancer‐specific survival (CSS) of BMC patients. Methods We collected and reviewed information on BMC patients diagnosed between 2004 and 2019 from the Surveillance Epidemiology and End Results database. Two nomograms were developed and validated to predict the OS and CSS based on predictors identified by univariate and multivariate Cox regression. An extra external validation was further performed using data from Sun Yat‐sen Memorial Hospital (SYSMH). Results A total of 3154 BMC patients included in this study were randomly assigned to training and validation groups in a 2:1 ratio. Independent prognostic predictors were identified, confirmed, and fitted into nomograms for OS and CSS, respectively. The C‐indices are 0.767 (Training group OS), 0.801 (Training group CSS), 0.763 (Validation group OS), and 0.781 (Validation group OS), respectively. Moreover, the nomograms exhibited remarkable precision in forecasting and significant clinical significance, as evidenced by receiver operating characteristic (ROC) curves, calibration curves, and decision curve analyses (DCA). The final validation using our data from SYSMH also showed high accuracy and substantial clinical benefits within the nomograms. The C‐indices are 0.849 (SYSMH group OS) and 0.916 (SYSMH group CSS). These indexes are better than tumor, node, and metastasis stage based on prediction results. Conclusions The nomograms developed with great performance predicted 1‐, 3‐, and 5‐year OS and CSS of BMC patients. Use of the nomograms in clinical practices shall bring significant benefits to BMC patients.
Details
- Language :
- English
- ISSN :
- 20457634
- Volume :
- 13
- Issue :
- 3
- Database :
- Directory of Open Access Journals
- Journal :
- Cancer Medicine
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.f4e72e2993bd4874ad0d40acb10a6e8f
- Document Type :
- article
- Full Text :
- https://doi.org/10.1002/cam4.6907