## 4 Discussion  
The newly integrated Episode and Episode_Event tables, which were introduced in the last OMOP release, represent an enrichment for the representation of long-term chronic diseases, including cancer. Due to the complexity of the disease and treatment approaches, episodic modeling of the respective disease phase is useful. It is now possible to represent the course of disease according to its timeline regarding treatment and disease development. In addition, low-level clinical events of a patient can now be assigned to an Episode via their temporal reference. The use of this new data structure within the CDM increases the capabilities of oncology data analysis and visualization. Regarding overall survival, it can be concluded that the results did not differ significantly between the systems. Nevertheless, only overall survival was evaluated in this work. The median difference in progression-free and disease-free survival was not investigated, which should be completed in the future. In addition, it must be considered that in the context of mapping to the standardized vocabularies, data entries from the source system occasionally could not be mapped to the respective standard, resulting in a reduced number of patients included in the CDM. Additionally, it must be noted that the extension of the CDM is not yet integrated in all applications of the OHDSI community, and is only implemented on the CDM database level. Thus, the extension is also not yet integrated in the analytical and methodological toolchain provided by the OHDSI community, such as Hades, Atlas, and others, which currently limits the evaluation options of the new release. However, it can be assumed that this will be mitigated by the next major release. In addition to implementing the Episodes, this project also addressed vocabulary mapping. Existing terminologies from the source system can be mapped to the OMOP standard. Alternatively, the terminologies in the source system can serve as a starting point for an elaborate mapping process to a completely new vocabulary, as in the case of the HemOnc mapping process. Mapping the HemOnc terminology to the OMOP standard improves the Treatment Regimen evaluation [23]. However, the algorithm developed by the Oncology Working Group (OncoRegimenFinder), which allows the translation of ATC substances to HemOnc terminology, is considered an even greater improvement. It is now possible to derive Treatment Regimens from ATC terminology, which is used in almost all European hospital systems for drug coding. In addition, by mapping source data to the OMOP-ATC and the OMOP-HemOnc standards, it is possible to revert from both terminologies to the respective RxNorm standard, which is a clinical drug dictionary for all drugs that are approved for the pharmaceutical market of the USA [20]. Through the standardized vocabularies maintained by the OHDSI 'CDM and THEMIS Working Group', it is now feasible to translate data elements from one of these three terminologies to each other, enabling international observational cancer research [23]. Furthermore, besides the ontological integration of Treatment Regimens into the CDM, the HemOnc Wiki also includes information on phase I-III clinical trials. In future releases, it is planned to include this information in the Standardized Vocabulary Metadata Repository, which would allow inference from the Treatment Regimen to the performed studies before approval [22; 23]. Overall, the ontological structure of the CDM simplifies the complexity and the effort of the generation and phenotyping of cohorts; high level terms of CDM concepts can be incorporated into the query as a parameter, rather than each parameter individually, as is common in the source system. As part of this work, source data were mapped to the Cancer Modifier vocabulary, an OMOP standard composed of different standards such as NAACCR, WHO, and SEER.