## 1 Introduction  
A cancer diagnosis is often followed by complex treatment that can last for years. Recently, many new therapeutic approaches have been developed, either derived from basic research or the use of new diagnostic measures, such as DNA sequencing, which examine the tumor in more detail [1]. Thus, an initial diagnosis of cancer is often accompanied by a series of diagnostic modifiers, such as Gleason score, grading, stage group. From this set of characteristics, the treatment strategy can be derived, and success can be estimated. When a new therapeutic approach is selected, the physician considers which therapeutic measures have already been carried out to increase the probability of a positive response and to reduce the risk of an adverse reaction [2]. These developments in predictive medicine have ensured that guideline-based treatment is increasingly shifting towards a personalized approach. However, the ability to give a more detailed specification of the tumor phenotype due to greater stratification possibilities also leads to decreasing case numbers within a specific tumor entity. The use of an appropriate study population to achieve significant results is limited by complex inclusion and exclusion criteria. In addition, the methods for analyzing these complex relationships, for example using Artificial Intelligence (AI) techniques, are continuing to evolve. These models require larger sample sizes than the current statistical methods in order to derive valid results. The potential applications of AI in the field of oncology have grown rapidly in recent years. Especially, the use of AI in the field of image analysis has delivered great progress [3]. The identification of complex patterns in radiological images aids the detection of malignant tumors and simplifies clinical decision-making processes. For example, one study has shown that an algorithm can predict whether a pulmonary nodule will become cancerous within the next 2 years, with 80% accuracy [4]. In addition to this use of AI in early cancer detection, image analysis can assist in the identification of tumor-specific diagnostic factors. In another study, a deep learning algorithm was used to predict the Gleason score of a patient tumor using prostatectomy images with 70% accuracy [5]. Besides the use of image analysis, AI can also help with the analysis of genomic sequencing data. As sequencing capabilities are increasing, so is the number of discovered genomic mutations, leading to researchers having to clarify associations between genomic mutations and phenotypes using literature research. This is where AI approaches might be able to simplify human workloads [6]. In modern cancer research, it is crucial to establish data exchange or decentralized analysis pipelines based on a homogeneous data semantic base in the joint networks of individual research institutions [7]. For example, Cancer Core Europe, a consortium of 28 European cancer institutions, has stated that there is a \"need for creating a uniform platform for translational cancer research to bring together enough centers to generate the critical mass of patients, expertise and resource required to make a significant breakthrough in cancer care\" [7] (p. 523). However, the German Cancer Consortium has identified several challenges for the establishment of such networks. Because of different data protection laws worldwide, merging data is challenging. Furthermore, depending on the data infrastructure, there are different technical requirements, such as documentation systems and others, that can make data exchange difficult. However, in general, the greatest challenge lies in semantic heterogeneity [8]. Semantic heterogeneity in this context means that two IT systems fulfill the prerequisites for receiving data from each other (syntactic interoperability), but the interpretation of this data is not possible due to ambiguous semantics.