Loading
Life sciences · Journal article
ESMO Real World Data and Digital Oncology · September 23, 2026
No summary has been generated for this record yet. What follows is drawn from its source metadata only.
Journal article.
No findings were extractable from the material analysed.
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
The source did not state who this applies to in practice.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
This record has not been graded across any dimension yet. Treat the label above as provisional and read the source.
What is missing. This record has no bottom line, key findings, reported figures, evidence dimensions. That is a gap in the analysis, not a judgement about the study.
Population-based cancer registries rely heavily on the manual coding of unstructured medical reports, a process that is both time-consuming and resource-intensive. The number of cases and reports is increasing and simultaneously qualified coding personnel are rare, making it challenging to maintain timely and high-quality cancer registration. Between March and September 2025, we implemented and evaluated an artificial intelligence (AI)-enhanced tool, a modular Python-based application, for tumor data extraction from 82 384 medical reports received by the Eastern Switzerland Cancer Registry. The tool, based on fine-tuned, pretrained German-language bidirectional encoder representations from transformers models, processed reports from >200 institutions, and provided an output file that can be directly integrated into the Swiss cancer registry software NICERStat-KRG (Cantonal Cancer Registries, Switzerland). For 10 739 reports (13%), the incidence date, International Classification of Diseases (ICD) 10 code, and ICD-O-3.2 topography, morphology, and behavior showed complete agreement with the corresponding tumor records in our database. Remaining reports were validated and registered by coding staff. Across core cancer registry variables, the tool achieved moderate-to-high agreement with the coding by our staff, with exact match rates of 19%-53% and partial matches up to 82%. It also correctly classified 82% of previously ignored reports as irrelevant. Despite a 27% reduction in coding staff resources, overall productivity increased markedly compared with 2024. These findings demonstrate that a locally deployed, privacy-preserving AI-enhanced tool can substantially improve productivity and data completeness in population-based cancer registration. The results support the potential of AI-assisted workflows to facilitate routine registry operations under increasing workload demands.