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Background <p>The availability of comparable epidemiological data on malignant tumours remains limited due to heterogeneous classification systems, divergent reference frameworks, and inconsistent case definitions. Incomplete data collection and an often biased focus on specific subtypes also impair the representativeness of the results. These methodological discrepancies make it difficult to perform a&#xa0;reliable cross-source analysis of tumour frequencies. This study aims to evaluate extensive cancer registry data to enable epidemiological comparisons across locations and to support differential diagnostic decisions by including age, gender, and line differentiation.</p> Methodology <p>Data from the Surveillance, Epidemiology, and End Results (SEER) program (2000–2019, <i>n</i> = 12,809,525) and the German Centre for Cancer Registry Data (ZfKD; 2000–2019, <i>n</i> = 9,754,219) were merged. Tumours were classified according to ICD O&#xa0;3.2. Incomplete and nonspecific datasets were removed and the remaining data visualised.</p> Results and discussion <p>The processed datasets were made available both online and as offline downloads, enabling an aggregated overview based on grouped ICD-O‑3.2&#xa0;codes as well as a&#xa0;differentiated analysis of morphological phenotypes across 73&#xa0;anatomical sites. The resulting visualisations provide a&#xa0;uniform epidemiological basis for comparative tumour analyses across a&#xa0;broad spectrum of locations and entities.</p>

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Pathodashboard: an epidemiological atlas for tumour diagnostics

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月30日

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  • Tiemo S. Gerber,
  • Stephanie Strobl,
  • Patrick Focke,
  • Stefan Porubsky,
  • Wilfried Roth,
  • Beate K. Straub

摘要

Background

The availability of comparable epidemiological data on malignant tumours remains limited due to heterogeneous classification systems, divergent reference frameworks, and inconsistent case definitions. Incomplete data collection and an often biased focus on specific subtypes also impair the representativeness of the results. These methodological discrepancies make it difficult to perform a reliable cross-source analysis of tumour frequencies. This study aims to evaluate extensive cancer registry data to enable epidemiological comparisons across locations and to support differential diagnostic decisions by including age, gender, and line differentiation.

Methodology

Data from the Surveillance, Epidemiology, and End Results (SEER) program (2000–2019, n = 12,809,525) and the German Centre for Cancer Registry Data (ZfKD; 2000–2019, n = 9,754,219) were merged. Tumours were classified according to ICD O 3.2. Incomplete and nonspecific datasets were removed and the remaining data visualised.

Results and discussion

The processed datasets were made available both online and as offline downloads, enabling an aggregated overview based on grouped ICD-O‑3.2 codes as well as a differentiated analysis of morphological phenotypes across 73 anatomical sites. The resulting visualisations provide a uniform epidemiological basis for comparative tumour analyses across a broad spectrum of locations and entities.