Model for decision support of molecular pathological parameters in head and neck cancer

Introduction Oncological decision-making processes are becoming increasingly complex with advances in diagnostics and more individual therapy options. In the case of head and neck tumors (HNC), this requires new information processing techniques and suitable models to support the decision-making process in the head and neck tumor board (HNTB) and molecular tumor board (MTB). For this purpose, a molecular pathological model was developed on the basis of the digital patient model for laryngeal carcinoma (LC).

Methods After the LC model was successfully developed, as a submodel, the molecular pathological model (MPM) has now been modeled as a Bayesian Network. The MPM was created on the basis of recent guidelines and studies. The graph structure was optimized and newly established therapy methods were integrated.

Results After multiple optimization, the MPM contains 25 information units and is primarily used to evaluate the therapeutic ability with the immune checkpoint inhibitor pembrolizumab. After evaluating the interference algorithm, the MPM was able to calculate the probability of a beneficial pembrolizumab therapy in 90 % , which correlates with the current guidelines.

ConclusionsPersonalized medicine and targeted therapy are of increasing importance in oncological therapy and require structured and comprehensive support for information management and decision-making. Taking into account the current guidelines and studies, the model can estimate suitable treatment options by reliably calculating probabilities and thus provide support for immunotherapy in HNC. The model is to be expanded through optimization in order to optimize the therapy decision-making processes in HNC patients.

Poster-PDF A-1514.pdf

Bundesministerium für Bildung und Forschung

Conflict of interest

Der Erstautor gibt keinen Interessenskonflikt an.

Publikationsverlauf

Artikel online veröffentlicht:
13. Mai 2021