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.

Making complex medical decisions is becoming an increasingly challenging task due to the growing amount of available evidence to consider and the higher demand for personalized treatment and patient care. IT systems for the provision of clinical decision support (CDS) can provide sustainable relief if decisions are automatically evaluated and processed. In this paper, we propose an approach for quantifying similarity between new and previously recorded medical cases to enable significant knowledge transfer for reasoning tasks on a patient-level. Methodologically, 102 medical cases with oropharyngeal carcinoma were analyzed retrospectively. Based on independent disease characteristics, patient-specific data vectors including relevant information entities for primary and adjuvant treatment decisions were created. Utilizing the ϕK correlation coefficient as the methodological foundation of our approach, we were able to determine the predictive impact of each characteristic, thus enabling significant reduction of the feature space to allow for further analysis of the intra-variable distances between the respective feature states. The results revealed a significant feature-space reduction from initially 19 down to only 6 diagnostic variables (ϕK correlation coefficient ≥ 0.3, ϕK significance test ≥ 2.5) for the primary and 7 variables (from initially 14) for the adjuvant treatment setting. Further investigation on the resulting characteristics showed a non-linear behavior in relation to the corresponding distances on intra-variable level. Through the implementation of a 10-fold cross-validation procedure, we were further able to identify 8 (primary treatment) matching cases with an evaluation score of 1.0 and 9 (adjuvant treatment) matching cases with an evaluation score of 0.957 based on their shared treatment procedure as the endpoint for similarity definition. Based on those promising results, we conclude that our proposed method for using data-driven similarity measures for application in medical decision-making is able to offer valuable assistance for physicians. Furthermore, we consider our approach as universal in regard to other clinical use-cases, which would allow for an easy-to-implement adaptation for a range of further medical decision-making scenarios.

EINLEITUNG Onkologische Entscheidungsprozesse werden durch Fortschritte in Diagnostik und individuellere Therapieoptionen zunehmend komplex. Auch bei Kopf-Hals-Tumoren (KHT) erfordert dies neue Techniken der Informationsverarbeitung und geeignete Modelle zur Unterstützung des Entscheidungsprozesses im Kopf-Hals-Tumorboard (KHTB), sowie molekularen Tumorboard (MTB). Dazu wurde auf Basis des digitalen Patientenmodells Larynxkarzinom (LC) ein molekularpathologisches Modell entwickelt. METHODEN: Nachdem das LC-Modell erfolgreich entwickelt wurde ist nun als Submodell das molekularpathologische Modell (MPM) als Bayes‘sches Netz modelliert worden. Das MPM wurde anhand aktueller Leitlinien und Studien erstellt. Die Graphenstruktur wurde optimiert und neu etablierte Therapiemethoden integriert. ERGEBNISSE: Das MPM enthält nach mehrfacher Optimierung 25 Informationseinheiten und dient primär der Evaluation der Therapiefähigkeit mit dem Immuncheckpoint-Inhibitor Pembrolizumab. Nach Evaluation des Interferenzalgorithmus konnte das MPM die Wahrscheinlichkeit einer nutzenbringenden Pembrolizumab-Therapie in 90 % berechnen, was mit den aktuellen Leitlinien korreliert. SCHLUSSFOLGERUNGEN Da Personalisierte Medizin und zielgerichtete Therapie sind in der onkologischen Therapie von zunehmender Bedeutung und erfordern eine strukturierte und umfassende Unterstützung des Informationsmanagements und der Entscheidungsfindung. Unter Berücksichtigung der aktuellen Leitlinien und Studien kann das Modell durch zuverlässige Berechnung von Wahrscheinlichkeiten geeignete Therapieoptionen abschätzen und so eine Unterstützung der Immuntherapie bei KHT geben. Durch eine Optimierung soll das Modell erweitert werden um perspektivisch die Therapieentscheidungsprozesse bei KHT-Patienten zu optimieren. Bundesministerium für Bildung und Forschung Publication Date: 13 May 2021 (online) © . Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany