1. Head and neck cancers survival in Europe, Taiwan, and Japan: results from RARECAREnet Asia based on a privacy-preserving federated infrastructure
- Author
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Laura Botta, Tomohiro Matsuda, Hadrien Charvat, Chun-ju Chiang, Wen-Chung Lee, Anna Jacoba van Gestel, Frank Martin, Gijs Geleijnse, Matteo Cellamare, Simone Bonfarnuzzo, Rafael Marcos-Gragera, Marcela Guevara, Mohsen Mousavi, Stephanie Craig, Jessica Rodrigues, Jordi Rubió-Casadevall, Lisa Licitra, Stefano Cavalieri, Carlo Resteghini, Gemma Gatta, Annalisa Trama, and the RARECAREnet working group
- Subjects
population-based cancer registry ,survival ,head and neck cancers ,geographical differences ,federated learning approach ,Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,RC254-282 - Abstract
BackgroundThe head and neck cancers (HNCs) incidence differs between Europe and East Asia. Our objective was to determine whether survival of HNC also differs between European and Asian countries.MethodsWe used population-based cancer registry data to calculate 5-year relative survival (RS) for the oral cavity, hypopharynx, larynx, nasal cavity, and major salivary gland in Europe, Taiwan, and Japan. We modeled RS with a generalized linear model adjusting for time since diagnosis, sex, age, subsite, and histological grouping. Analyses were performed using federated learning, which enables analyses without sharing sensitive data.FindingsFive-year RS for HNC varied between geographical areas. For each HNC site, Europe had a lower RS than both Japan and Taiwan. HNC subsites and histologies distribution and survival differed between the three areas. Differences between Europe and both Asian countries persisted even after adjustments for all HNC sites but nasal cavity and paranasal sinuses, when comparing Europe and Taiwan.InterpretationSurvival differences can be attributed to different factors including different period of diagnosis, more advanced stage at diagnosis, or different availability/access of treatment. Cancer registries did not have stage and treatment information to further explore the reasons of the observed survival differences. Our analyses have confirmed federated learning as a feasible approach for data analyses that addresses the challenges of data sharing and urge for further collaborative studies including relevant prognostic factors.
- Published
- 2023
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