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Pathodashboard: an epidemiological atlas for tumour diagnostics
Tiemo S Gerber1,2, Stephanie Strobl3, Patrick Focke3
1Worms Hospital, Institute of Pathology, Langenbeckstraße 1, 55131, Mainz, Germany. TiemoSven.Gerber@klinikum-worms.de.
Pathologie (Heidelberg, Germany)
|June 30, 2026
Summary
This study merges extensive cancer registry data from the US and Germany, creating a unified dataset for reliable tumor frequency comparisons across diverse locations. The processed data supports differential diagnostics and epidemiological research.
Area of Science:
- Oncology
- Epidemiology
- Data Science
Background:
- Limited comparable epidemiological data on malignant tumors due to inconsistent classification systems and definitions.
- Incomplete data collection and subtype bias hinder representativeness and cross-source analysis of tumor frequencies.
- Methodological discrepancies impede reliable comparisons of tumor incidence across different regions.
Purpose of the Study:
- To evaluate extensive cancer registry data for enabling epidemiological comparisons across locations.
- To support differential diagnostic decisions by incorporating age, gender, and line differentiation.
- To establish a uniform epidemiological basis for comparative tumor analyses.
Main Methods:
- Merged data from the Surveillance, Epidemiology, and End Results (SEER) program and the German Centre for Cancer Registry Data (ZfKD) from 2000-2019.
- Classified tumors using the International Classification of Diseases for Oncology, 3rd Edition (ICD-O-3.2).
- Removed incomplete and nonspecific datasets, then visualized the remaining data.
Main Results:
- Processed datasets were made available online and for offline download.
- Enabled aggregated overviews using grouped ICD-O-3.2 codes.
- Facilitated differentiated analysis of morphological phenotypes across 73 anatomical sites.
Conclusions:
- The study provides a uniform epidemiological basis for comparative tumor analyses.
- Visualizations support differential diagnostic decisions and cancer research.
- The unified dataset enhances the reliability of cross-source tumor frequency analysis.
