Ludwig, RomanRomanLudwigBrönnimann JulianPérez Haas Yoel SamuelLooman Esmée LaurenBalermpas, PanagiotisPanagiotisBalermpasBenavente, SergiSergiBenaventeSchubert, AdrianAdrianSchubertBarbatei DorotheaBauwens LaurenceHoffmann Jean-MarcEliçin, OlgunOlgunEliçinDettmer, MatthiasMatthiasDettmer0000-0003-0948-1392Pouymayou, BertrandBertrandPouymayouGiger, RolandRolandGigerVincent GrégoireUnkelbach, JanJanUnkelbach2024-11-212024-11-212024https://boris-portal.unibe.ch/handle/20.500.12422/189551Abstract We previously developed a mechanistic hidden Markov model (HMM) to predict the lymphatic tumor progression in oropharyngeal squamous cell carcinomas. To extend the model to other tumor subsites in the head and neck defined by ICD-10 codes, we develop a mixture model combining multiple HMMs. The mixture coefficients and the model parameters are learned via an EM-like algorithm from a large multi-centric dataset on lymph node involvement. The methodology is demonstrated for tumors in the oropharynx and oral cavity. The mixture model groups anatomically nearly subsites and yields interpretable mixture coefficients consistent with anatomical location. It allows the prediction of differences in lymph node involvement depending on tumor subsite.enconference_item