Early evaluation of the WCT EVI MAP project: implementation, challenges, and emerging impact on evidence-based tumour classification

Background
The WHO Classification of Tumours (WCT) underpins cancer diagnosis globally, but the evidence-base used to inform tumour classification has never been systematically appraised. The WCT EVI MAP project was designed to address this need by developing a structured, evidence-based framework and an associated tool to map and visualise the evidence base underpinning tumour classification. This paper aims to present an overview of the project and its initial outputs, with a view to considering the impact on the WCT and beyond.

Methods
A multi-phase, multi-institutional programme was implemented across eleven work packages. Early phases focused on methodological development, including a Delphi-based consensus process to define a tumour pathology-specific hierarchy of evidence and a consultation-driven framework for evidence gap mapping. Subsequent phases involved large-scale systematic identification, screening, coding, and mapping of the literature across tumour domains, producing Evidence Gap Maps (EGMs). Project implementation was evaluated using mixed methods, including structured monitoring and stakeholder feedback.

Results
Core methodological outputs were successfully established, including a novel Hierarchy of Evidence in Tumour Pathology (HETP) to assess levels of evidence and standardised mapping framework. Large-scale evidence mapping was undertaken across multiple tumour domains, with tens of thousands of records screened and thousands of studies coded. Substantial variability in evidence volume, study design, and quality was observed across tumour types. The WCT Evidence Explorer tool was launched as an open-access, interactive web-based platform enabling multidimensional exploration of EGMs.

Conclusions
The WCT EVI MAP project demonstrates the feasibility of applying evidence gap mapping methods at scale within tumour pathology and provides a novel, data-driven approach to supporting decision-making processes related to tumour classification. The resulting framework and tool represent a step towards a living, evidence-based tool to inform tumour classification, with potential implications for research prioritisation, policy-making, and clinical practice.