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DOI: 10.1055/s-0041-1727958
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.
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Bundesministerium für Bildung und Forschung
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Conflict of interest
Der Erstautor gibt keinen Interessenskonflikt an.
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Publication History
Article published online:
13 May 2021
© 2021. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial-License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).
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