ArticleESMO real world data and digital oncology2026
Semi-automated monitoring of deviations from historical incidence trends for enhanced timeliness in cancer control.
Article in ESMO real world data and digital oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Authors and funding
7 authors.
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Abstract
Background: Population-based cancer registries aim for timely reporting of cancer incidence and rapid identification of unexpected patterns to support cancer control. To facilitate large-scale, systematic, first evaluation of incidence data, a semi-automated tool was built that flags statistically significant deviations from historical trends, which may merit further investigation. Materials and methods: The tool was developed in the open-source programming language R and uses a CSV file to specify input parameters. For each row, observed or age-standardised incidence rates are calculated, and corresponding graphical outputs, with various stratifications and substratifications, are produced. A flexible regression model is fitted to all but the most recent incidence year, with an automated algorithm determining optimal knotpoint placement. The model is then extrapolated to estimate the expected incidence for the final year, which is statistically compared with the observed value. The model is visually represented, including confidence bands, in the graphical output. Results are exported as PNG files organised in hierarchical folders, along with a structured Quarto HTML report containing all figures. Results: The modelling approach captured complex temporal patterns without overfitting and reliably identified deviations from historical trends. This high-throughput tool is highly customisable and can be extended to analyse stage-specific incidence trends or stratifications by other categorical variables. Conclusions: This semi-automated tool enables efficient first-line visual evaluation of newly available registry data to highlight emerging trends potentially warranting further investigation from either a data quality or public health perspective. The code is available from the authors upon request. Output is highly customisable via CSV input and can be stratified on multiple levels.
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