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Simple Summary: Tumor therapy in many human malignancies, including head and neck cancer, is increasingly demanding due to advances in diagnostics and individualized treatments. Multidisciplinary tumor boards, especially molecular tumor boards, consider a great amount of information to find the optimal treatment decision. Clinical decision support systems can help in optimizing this complex decision-making process. We designed a digital patient model based on conditional probability algorithms as Bayesian networks to support the decision-making process regarding treatment with approved immunotherapeutic agents (Nivolumab and Pembrolizumab). The model is able to process relevant clinical information to recommend a certain immunotherapeutic agent based on literature, approval, and guidelines. New diagnostic methods and novel therapeutic agents spawn additional and heterogeneous information, leading to an increasingly complex decision-making process for optimal treatment of cancer. A great amount of information is collected in organ-specific multidisciplinary tumor boards (MDTBs). By considering the patient's tumor properties, molecular pathological test results, and comorbidities, the MDTB has to consent an evidence-based treatment decision. Immunotherapies are increasingly important in today's cancer treatment, resulting in detailed information that influences the decision-making process. Clinical decision support systems can facilitate a better understanding via processing of multiple datasets of oncological cases and molecular genetic information, potentially fostering transparency and comprehensibility of available information, eventually leading to an optimum treatment decision for the individual patient. We constructed a digital patient model based on Bayesian networks to combine the relevant patient-specific and molecular data with depended probabilities derived from pertinent studies and clinical guidelines to calculate treatment decisions in head and neck squamous cell carcinoma (HNSCC). In a validation analysis, the model can provide guidance within the growing subject of immunotherapy in HNSCC and, based on its ability to calculate reliable probabilities, facilitates estimation of suitable therapy options. We compared actual treatment decisions of 25 patients with the calculated recommendations of our model and found significant concordance (Cohen's κ = 0.505, p = 0.009) and 84% accuracy. [ABSTRACT FROM AUTHOR]

Objectives: Probabilistic modeling of a patient's situation with the goal of providing calculated therapy recommendations can improve the decision making of interdisciplinary teams. Relevant information entities and direct causal dependencies, as well as uncertainty, must be formally described. Possible therapy options, tailored to the patient, can be inferred from the clinical data using these descriptions. However, there are several avoidable factors of uncertainty influencing the accuracy of the inference. For instance, inaccuracy may emerge from outdated information. In general, probabilistic models, e.g. Bayesian Networks can depict the causality and relations of individual information entities, but in general cannot evaluate individual entities concerning their up-to-dateness. The goal of the work at hand is to model diagnostic up-to-dateness, which can reasonably adjust the influence of outdated diagnostic information to improve the inference results of clinical decision models.Methods and Materials: We analyzed 68 laryngeal cancer cases and modeled the state of up-to-dateness of different diagnostic modalities. All cases were used for cross-validation. 55 cases were used to train the model, 13 for testing. Each diagnostic procedure involved in the decision making process of these cases was associated with a specific threshold for the time the information is considered up-to-date, i.e. reliable. Based on this threshold, outdated findings could be identified and their impact on probabilistic calculations could be reduced. We applied the model for reducing the weight of outdated patient data in the computation of TNM stagings for the 13 test cases and compared the results to the manually derived TNM stagings in the patient files.Results: With the implementation of these weights in the laryngeal cancer model, we increased the accuracy of the TNM calculation from 0.61 (8 out of 13 cases correct) to 0.76 (10 out of 13 cases correct).Conclusion: Decision delay may cause specific patient data to be outdated. This can cause contradictory or false information and impair calculations for clinical decision support. Our approach demonstrates that the accuracy of Bayesian Network models can be improved when pre-processing the patient-specific data and evaluating their up-to-dateness with reduced weights on outdated information. [ABSTRACT FROM AUTHOR]