## 1 Introduction
A solution could be the mapping of cancer data to a common data model (CDM) which represents knowledge with unified semantics and enables decentralized analysis. Many CDMs come with analytical applications. Thus, the integration of heterogenous operational databases into a CDM enables the use of CDM-developed analytical applications, such as package libraries and REST APIs. A well-known CDM in the field of clinical research is the PCORNet Model from the Patient-Centered Outcomes Research Institute (PCORI). They have developed a policy of data standards to enable the efficient use of data in clinical and patient-powered research without violating data protection regulations. These data standards lead to the semantic alignment of the source data, so that multi-centered studies are possible without the respective institutions having to give up control of their data [9]. This can enable larger cohort sizes, which can be analyzed using AI through federated learning. The Clinical Data Interchange Standards Consortium (CDISC) has developed several data models that cover the different phases of the clinical research process. There are data models for study planning, data collection, the tabulation of study data, and analysis. These data models maintain compatible standards across all converted datasets. In a related study using resident registry data, the most common CDMs in the clinical research domain (SCDM v.5.0, PCORnet v.3.0, OMOP v.3.0, CDISC, SDTM v.1.4.) were evaluated in terms of completeness, integrity, flexibility, integrability, and implementability for EHR-based longitudinal registry data [10]. It was found that the OMOP CDM v.3.0, provided by the Observational Health Data Sciences and Informatics (OHDSI) community, achieved the best scores regarding the evaluation criteria of the study. OHDSI is a multi-stakeholder interdisciplinary collaboration founded in 2014. It arose from the public-private partnership with the US Food and Drug Administration (FDA). After FDA funding ceased, it was decided that a collaboration should be developed; the CDM was adopted as an open-source project with the aim of integration into scientific applications. Nowadays, this collaboration consists of an international network of researchers and over 100 observational health databases from 19 countries. It develops technical solutions for the representation of uniform medical data from different source systems, tackling the lack of standardized her and EMR data and the absence of consistent patient-level data in observational databases [11]. It provides open-source applications with the goal of strengthening the research community, whose findings can then be considered in clinical questions. For example, there is a comprehensive R package library that allows feature extraction from OMOF, and AI-based analysis of these extracted OMOF data to be performed. Also of note is the PatientLevelPrediction package, which provides patient-specific prediction models using machine learning and deep learning algorithms [12]. In addition, federated pipelines of different semantic homogeneity databases reduce capture bias, and a large number of observed patients in a study leads to higher statistical power and greater stratification possibilities. In September 2021, OMOF was supplemented by a new Episode Domain [13]. This Episode Domain contains the master table Episode, which displays an episodic modeling of the course of a disease, depending on its respective concepts. Episodic modelling of cancer is essential to represent the complex disease process. Correct episodic modeling is therefore of particular importance to derive evidence from oncology data. By implementing the standardized concept of Disease Dynamic in the Episode Domain, survival probabilities with cancer-specific endpoints can be calculated via the CDM. This concept is based on the Response Evaluation Criteria in Solid Tumors (RECIST).