  The elective clinical target volume (CTV-N) in oropharyngeal squamous cell
carcinoma (OPSCC) is currently based mostly on the prevalence of lymph node
metastases in different lymph node levels (LNLs) for a given primary tumor
location. We present a probabilistic model for ipsilateral lymphatic spread
that can quantify the microscopic nodal involvement risk based on an individual
patient's T-category and clinical involvement of LNLs at diagnosis. We extend a
previously published hidden Markov model (HMM), which models the LNLs (I, II,
III, IV, V, and VII) as hidden binary random variables (RVs). Each represents a
patient's true state of lymphatic involvement. Clinical involvement at
diagnosis represents the observed binary RVs linked to the true state via
sensitivity and specificity. The primary tumor and the hidden RVs are connected
in a graph. Each edge represents the conditional probability of metastatic
spread per abstract time-step, given disease at the edge's starting node. To
learn these probabilities, we draw Markov chain Monte Carlo samples from the
likelihood of a dataset (686 OPSCC patients) from three institutions. We
compute the model evidence using thermodynamic integration for different graphs
to determine which describes the data best. The graph maximizing the model
evidence connects the tumor to each LNL and the LNLs I through V in order. It
predicts the risk of occult disease in level IV is below 5% if level III is
clinically negative, and that the risk of occult disease in level V is below 5%
except for advanced T-category (T3 and T4) patients with clinical involvement
of levels II, III, and IV. The provided statistical model of nodal involvement
in OPSCC patients trained on multi-institutional data may guide the design of
clinical trials on volume-deescalated treatment of OPSCC and contribute to more
personal guidelines on elective nodal treatment.
