Development of a decision tree diagram for classifying study designs in tumour pathology research: a multidisciplinary approach

The World Health Organization (WHO) Classification of Tumours: A Living Evidence Gap Map by Tumour Type (WCT EVI MAP) project aims to develop Evidence Gap Maps of the available evidence, primarily to inform the WHO Classification of Tumours. The project, covering all tumour types, faces the challenge of reviewing a huge number of studies by reviewers from multiple backgrounds. The aim was to develop a decision tree (DT) diagram for classifying study designs reporting on tumour pathology studies, in order to support the decision-making process when assigning evidence levels across various disciplines. A modified consensus process, incorporating stakeholder workshops, was conducted in three phases: (1) development of the initial DT diagram draft (literature review and expert evaluation); (2) iterative reviews with project partners; and (3) testing the advanced DT diagram version with several sets of references to refine critical points. A total of 368 records were used for training throughout the entire process. Consensus was achieved when classifications could categorise studies consistently without causing discordance in new example sets. A DT diagram and its Glossary of Operational Definitions with 27 decision nodes and 26 categories were developed. The DT diagram is organised into six sections: WCT EVI MAP selection criteria, evidence synthesis, basic research related studies, descriptive studies, observational and experimental studies, and diagnostic test studies. The DT diagram is a valuable tool for the project's needs, successfully integrating diverse disciplinary perspectives for classifying evidence in tumour pathology research according to study design. It lays the foundation for future advancements in evidence mapping and classification within tumour pathology and related disciplines.

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Publication Details

Journal
The Journal of Pathology Clinical Research
Published
2025-11-29
DOI
https://doi.org/10.1002/2056-4538.70056
Citations
1
Primary Topic
AI in cancer detection
Type
article
Field-Weighted Citation Impact
2.48

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article

Development of a decision tree diagram for classifying study designs in tumour pathology research: a multidisciplinary approach

Inga Trulson, Karolina Worf, Ian A. Cree, Ester García‐Ovejero et al.
1 citations
The Journal of Pathology Clinical Research
AI in cancer detection
2.48
article

Development of a decision tree diagram for classifying study designs in tumour pathology research: a multidisciplinary approach

Inga Trulson, Karolina Worf, Ian A. Cree, Ester García‐Ovejero, Ramon Cierco Jiménez, Marina Pollán, Łukasz Taraszkiewicz, Richard T. Colling, Elena Plans‐Beriso, Beatriz Pérez‐Gómez, Magdalena Chechlińska, Magdalena Kowalewska, Irmina Maria Michałek, K.I. Maslova, Puay Hoon Tan, Stefan Holdenrieder, Blanca Iciar Indave Ruiz, Gabrielle Goldman‐Lévy, Oana Craciun, Dilani Lokuhetty, Fiona Campbell, Christine Giesen, Javier del Águila, Sophie Gabriel, M. Montes-Mota
article en
1 citations

Abstract

The World Health Organization (WHO) Classification of Tumours: A Living Evidence Gap Map by Tumour Type (WCT EVI MAP) project aims to develop Evidence Gap Maps of the available evidence, primarily to inform the WHO Classification of Tumours. The project, covering all tumour types, faces the challenge of reviewing a huge number of studies by reviewers from multiple backgrounds. The aim was to develop a decision tree (DT) diagram for classifying study designs reporting on tumour pathology studies, in order to support the decision-making process when assigning evidence levels across various disciplines. A modified consensus process, incorporating stakeholder workshops, was conducted in three phases: (1) development of the initial DT diagram draft (literature review and expert evaluation); (2) iterative reviews with project partners; and (3) testing the advanced DT diagram version with several sets of references to refine critical points. A total of 368 records were used for training throughout the entire process. Consensus was achieved when classifications could categorise studies consistently without causing discordance in new example sets. A DT diagram and its Glossary of Operational Definitions with 27 decision nodes and 26 categories were developed. The DT diagram is organised into six sections: WCT EVI MAP selection criteria, evidence synthesis, basic research related studies, descriptive studies, observational and experimental studies, and diagnostic test studies. The DT diagram is a valuable tool for the project's needs, successfully integrating diverse disciplinary perspectives for classifying evidence in tumour pathology research according to study design. It lays the foundation for future advancements in evidence mapping and classification within tumour pathology and related disciplines.

The Journal of Pathology Clinical ResearchVol. 12(1)
Instituto de Salud Carlos III (ES), University of Oxford (GB), Eye & Retina Surgeons (SG), Greater Poland Cancer Center (PL), National Institute of Oncology (HU), Oxford University Hospitals NHS Trust (GB), Deutsches Herzzentrum München (DE), The Maria Sklodowska-Curie National Research Institute of Oncology (PL), Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (ES), Centre international de recherche sur le cancer (FR), Universidad Autónoma de Madrid (ES), Newcastle University (GB)
World Health Organization, UK Research and Innovation, European Commission, HORIZON EUROPE Framework Programme
Openalex Percentile: Top 8%
AI in cancer detection
2.48
DOIOpen Access PDFOpenAlex
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