Machine learning prognostication in nasopharyngeal carcinoma: a european multicentre analysis of survival and risk of second malignancy
Year: 2026
Type: article
Study design: Observational Study
Abstract: INTRODUCTION: Nasopharyngeal carcinoma (NPC) is rare in Europe, and emerging data suggest poorer outcomes in Caucasian patients compared with Asian populations, highlighting the need for region-specific prognostic tools. Inflammation-based biomarkers and artificial intelligence show promise for risk stratification and prediction of survival and second primary cancers (SPCs). MATERIALS AND METHODS: We conducted a retrospective multicentre study including 405 NPC patients from six European institutions. Demographic, clinicopathological, and haematologic inflammatory markers were collected, and machine learning algorithms were developed to predict 5-year OS and SPC occurrence. Multiple train-test splitting strategies and machine learning (ML) classifiers were evaluated. Models were tested both with and without systemic inflammatory ratios to assess their added prognostic value. RESULTS: The median age was 52 years, 91.6% of patients were classified as White/European ancestry, and 77.3% received chemoradiotherapy. Five-year OS was 66.6%, while 12.8% developed SPC. The Random Forest classifier achieved the best performance for OS prediction (accuracy 0.74; AUC 0.66) using the complete feature set, while SPC prediction reached an accuracy of 0.80 (AUC 0.74). Exclusion of inflammatory markers resulted in a consistent decline in accuracy across all models. Feature-importance analysis highlighted inflammatory ratios among the strongest predictors for both OS and SPC. The present study was reported according to TRIPOD+AI reporting guidelines. CONCLUSIONS: This study presents the first machine-learning prognostic models for nasopharyngeal carcinoma derived from a predominantly Caucasian European multicentre cohort. Systemic inflammatory markers modestly improved overall survival prediction and substantially enhanced second primary cancer risk estimation. The resulting models are transparent, cost-effective, and support the potential benefit of prognostic assessment through machine learning in non-endemic settings.
Authors: Irina Maria Pușcaș, Anda Gata, Paolo Boscolo Rizzo, Marco Stellin, Vittorio Rampinelli, Davide Tomasini, Cesare Piazza, Daniele Borsetto, Will Ince, Carlos M. Chiesa-Estomba, Maria Landa-Garmendia, Pavol Surda, Eleanor Crossley, Matt Lechner, Teodora Maria Ursu, Laura Diana Cernău, Adél Bajcsi, Laura Silvia Diosan, Camelia Chira, Alexandra Roman, Vlad Alexandru Gâta, Alexandru Irimie, Silviu Albu
Institutions: Iuliu Hațieganu University of Medicine and Pharmacy, University of Padua, University of Trieste, University of Brescia, Azienda Socio Sanitaria Territoriale degli Spedali Civili di Brescia, University of Cambridge, Cambridge University Hospitals NHS Foundation Trust, Universidad de Deusto, Donostiako Unibertsitate Ospitalea, Guy's Hospital, University College London, Babeș-Bolyai University
Language: English
Topic: Head and Neck Cancer Studies
Subfield: Otorhinolaryngology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $2,890
Awards: G213105822, G4414739080
OpenAlex ID: W7168705121