{"id":1684,"date":"2026-10-06T19:04:22","date_gmt":"2026-10-06T19:04:22","guid":{"rendered":"https:\/\/www.cs.ubbcluj.ro\/~meco\/machine-learning-prognostication-in-nasopharyngeal-carcinoma-a-european-multicentre-analysis-of-survival-and-risk-of-second-malignancy-2026\/"},"modified":"2026-10-06T19:24:25","modified_gmt":"2026-10-06T19:24:25","slug":"machine-learning-prognostication-in-nasopharyngeal-carcinoma-a-european-multicentre-analysis-of-survival-and-risk-of-second-malignancy-2026","status":"publish","type":"post","link":"https:\/\/www.cs.ubbcluj.ro\/~meco\/machine-learning-prognostication-in-nasopharyngeal-carcinoma-a-european-multicentre-analysis-of-survival-and-risk-of-second-malignancy-2026\/","title":{"rendered":"Machine learning prognostication in nasopharyngeal carcinoma: a european multicentre analysis of survival and risk of second malignancy (2026)"},"content":{"rendered":"
\n

European Archives of Oto-Rhino-Laryngology<\/p>\n

Authors<\/h2>\n

I. Pu\u0219ca\u0219, Anda G\u00e2ta, P. Rizzo, M. Stellin, V. Rampinelli, D. Tomasini, C. Piazza, Daniele Borsetto, W. Ince, C. Chiesa-Estomba, Maria Landa-Garmendia, P. Surda, Eleanor J. Crossley, Matt Lechner, Teodora Maria Ursu, L. Cernau, Ad\u00e9l Bajcsi, Laura-Silvia Dio\u015fan, C. Chira, Alexandra Roman, V. G\u00e2ta, Alexandru Irimie, Silviu Albu<\/p>\n

Abstract<\/h2>\n

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). 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\u2013test 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. 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. 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.<\/p>\n

Citation<\/h2>\n
@Inproceedings{Pu\u0219ca\u02192026MachineLP,\n author = {I. Pu\u0219ca\u0219 and Anda G\u00e2ta and P. Rizzo and M. Stellin and V. Rampinelli and D. Tomasini and C. Piazza and Daniele Borsetto and W. Ince and C. Chiesa-Estomba and Maria Landa-Garmendia and P. Surda and Eleanor J. Crossley and Matt Lechner and Teodora Maria Ursu and L. Cernau and Ad\u00e9l Bajcsi and Laura-Silvia Dio\u015fan and C. Chira and Alexandra Roman and V. G\u00e2ta and Alexandru Irimie and Silviu Albu},\n booktitle = {European Archives of Oto-Rhino-Laryngology},\n title = {Machine learning prognostication in nasopharyngeal carcinoma: a european multicentre analysis of survival and risk of second malignancy},\n year = {2026}\n}<\/pre>\n<\/div>\n","protected":false},"excerpt":{"rendered":"
European Archives of Oto-Rhino-Laryngology Authors I. Pu\u0219ca\u0219, Anda G\u00e2ta, P. Rizzo, M. Stellin, V. Rampinelli, D. Tomasini, C. Piazza, Daniele Borsetto, W. Ince, C. Chiesa-Estomba, Maria Landa-Garmendia, P. Surda, Eleanor J. Crossley, Matt Lechner, Teodora Maria Ursu, L. Cernau, Ad\u00e9l Bajcsi, Laura-Silvia Dio\u015fan, C. Chira, Alexandra Roman, V. G\u00e2ta, Alexandru Irimie, Silviu Albu Abstract Nasopharyngeal…<\/p>\n","protected":false},"author":6,"featured_media":1709,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":[],"categories":[4],"tags":[69,11],"_links":{"self":[{"href":"https:\/\/www.cs.ubbcluj.ro\/~meco\/wp-json\/wp\/v2\/posts\/1684"}],"collection":[{"href":"https:\/\/www.cs.ubbcluj.ro\/~meco\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.cs.ubbcluj.ro\/~meco\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.cs.ubbcluj.ro\/~meco\/wp-json\/wp\/v2\/users\/6"}],"replies":[{"embeddable":true,"href":"https:\/\/www.cs.ubbcluj.ro\/~meco\/wp-json\/wp\/v2\/comments?post=1684"}],"version-history":[{"count":2,"href":"https:\/\/www.cs.ubbcluj.ro\/~meco\/wp-json\/wp\/v2\/posts\/1684\/revisions"}],"predecessor-version":[{"id":1710,"href":"https:\/\/www.cs.ubbcluj.ro\/~meco\/wp-json\/wp\/v2\/posts\/1684\/revisions\/1710"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.cs.ubbcluj.ro\/~meco\/wp-json\/wp\/v2\/media\/1709"}],"wp:attachment":[{"href":"https:\/\/www.cs.ubbcluj.ro\/~meco\/wp-json\/wp\/v2\/media?parent=1684"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.cs.ubbcluj.ro\/~meco\/wp-json\/wp\/v2\/categories?post=1684"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.cs.ubbcluj.ro\/~meco\/wp-json\/wp\/v2\/tags?post=1684"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}