A mixture of hidden Markov models to predict the lymphatic spread in head and neck cancer depending on primary tumor location

Abstract 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.