European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026
Machine learning prognostication in nasopharyngeal carcinoma: a european multicentre analysis of survival and risk of second malignancy.
СтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод в очереди
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Pușcaș Irina Maria, Gata Anda, Rizzo Paolo Boscolo, Stellin Marco, Rampinelli Vittorio, Tomasini Davide, Piazza Cesare, Borsetto Daniele, Ince Will, Chiesa-Estomba Carlos M, Landa-Garmendia Maria, Surda Pavol, Crossley Eleanor, Lechner Matt, Ursu Teodora Maria, Cernău Laura Diana, Bajcsi Adél, Diosan Laura Silvia, Chira Camelia, Roman Alexandra, Gâta Vlad Alexandru, Irimie Alexandru, Albu Silviu
Аннотация
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.
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.