Related Experiment Video
Updated: Jul 13, 2025
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
Prognostic prediction models for oropharyngeal squamous cell carcinoma (OPSCC): a protocol for systematic review,
Zhen Lu1, Xinyi Zhou1, Leiwen Fu1
1School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, Guangdong, China.
Introduction:
Oropharyngeal squamous cell carcinoma (OPSCC) is increasingly prevalent and has significantly heterogeneous risks of survival for diagnosed individuals due to the inter-related risk factors. Precise prediction of the risk of survival for an individual patient with OPSCC presents a useful adjunct to therapeutic decision-making regarding the management of OPSCC. The aim of this systematic review, critical appraisal and meta-analysis is to assess prognostic prediction models for OPSCC and lay a foundation for future research programmes to develop and validate prognostic prediction models for OPSCC.
Methods And Analysis:
This protocol will follow the Preferred Reporting Items for Systematic Review and Meta-Analyses Protocol statement. Based on predefined criteria, electronic databases including MEDLINE, Embase, Web of Science, the Cochrane Library and China National Knowledge Infrastructure (CNKI) will be searched for relevant studies without language restrictions from inception of databases to present. This study will systematically review published prognostic prediction models for survival outcomes in patients with OPSCC, describe their characteristics, compare performance and assess risk of bias and real-world clinical utility. Selection of eligible studies, data extraction and critical appraisal will be conducted independently by two reviewers. A third reviewer will resolve any disagreements. Included studies will be systematically summarised using appropriate tools designed for prognostic prediction modelling studies. Risk of bias and quality of studies will be assessed using the Prediction Model Risk of Bias Assessment Tool and the Transparent Reporting of a multivariable prediction model for individual prognosis or diagnosis. Performance measures of these models will be pooled and analysed with meta-analyses if feasible.
Ethics And Dissemination:
This review will be conducted completely based on published data, so approval from an ethics committee or written consent is not required. The results will be disseminated through a peer-reviewed publication.
Prospero Registration Number:
CRD42023400272.
