A Bayesian network model of lymphatic tumor progression for personalized elective CTV definition in head and neck cancers.

Many tumors including head and neck squamous cell carcinoma (HNSCC) spread along the lymphatic network. Current imaging modalities can only detect sufficiently large metastases. Therefore, adjacent lymph node levels (LNL) are irradiated electively since they may harbor microscopic tumors. We apply Bayesian Networks (BN) to model lymphatic tumor progression. The model can subsequently be used to personalize the risk estimation of microscopic lymph node metastases in newly diagnosed patients based on their distribution of macroscopic metastases. A BN is a graphical representation of a joint probability distribution. We represent LNLs by binary random variables corresponding to the BN nodes. Each LNL is associated with a hidden microscopic state and an observed macroscopic state (e.g. F-FDG-PET/CT imaging). The primary tumor is represented by network input nodes. We demonstrate the concept for early T-stage oropharyngeal carcinomas and their spread to ipsilateral lymph node levels (LNL) Ib to IV. We show that the BN parameters can be efficiently learnt by merging pathology findings on microscopic tumor progression (which is limited to a few published studies) and imaging data on macroscopic tumor progression such as CT and F-FDG-PET (which are widely available in clinical practice). The trained network can be used to quantify how the distribution of macroscopic metastases impacts the probability of microscopic involvement of the remaining LNLs. The analysis suggests that the risk of microscopic involvement of level IV exceeds 5% only if level III harbors metastases. Excluding level IV from the elective CTV for other patients would reduce the integral dose delivered to the patient and potentially reduce acute and late side effects.