Extracting structured information from unstructured histopathology reports using generative pre‐trained transformer 4 ( GPT ‐4)

The Journal of Pathology, vol. 262, pp. 310–319

Abstract

Deep learning applied to whole-slide histopathology images (WSIs) has the potential to enhance precision oncology and alleviate the workload of experts. However, developing these models necessitates large amounts of data with ground truth labels, which can be both time-consuming and expensive to obtain. Pathology reports are typically unstructured or poorly structured texts, and efforts to implement structured reporting templates have been unsuccessful, as these efforts lead to perceived extra workload. In this study, we hypothesised that large language models (LLMs), such as the generative pre-trained transformer 4 (GPT-4), can extract structured data from unstructured plain language reports using a zero-shot approach without requiring any re-training. We tested this hypothesis by utilising GPT-4 to extract information from histopathological reports, focusing on two extensive sets of pathology reports for colorectal cancer and glioblastoma. We found a high concordance between LLM-generated structured data and human-generated structured data. Consequently, LLMs could potentially be employed routinely to extract ground truth data for machine learning from unstructured pathology reports in the future. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Authors 9

1. Daniel Truhn Aachen Department of Diagnostic and Interventional Radiology

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Department of Diagnostic and Interventional Radiology University Hospital RWTH Aachen Aachen Germany

   Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany

2. Chiara ML Loeffler Aachen Department of Medicine III

   Universitätsklinikum Aachen · University Hospital Carl Gustav Carus · Else Kröner Fresenius Center for Digital Health · RWTH Aachen University

   Affiliation as printed

   Department of Medicine I University Hospital Dresden Dresden Germany

   Department of Medicine III University Hospital RWTH Aachen Aachen Germany

   Else Kroener Fresenius Center for Digital Health Technical University Dresden Dresden Germany

   Department of Medicine I, University Hospital Dresden, Dresden, Germany

   Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany

   Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany

3. Gustav Anton Müller‐Franzes Aachen Department of Diagnostic and Interventional Radiology

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Department of Diagnostic and Interventional Radiology University Hospital RWTH Aachen Aachen Germany

   Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany

4. Sven Nebelung Aachen Department of Diagnostic and Interventional Radiology

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Department of Diagnostic and Interventional Radiology University Hospital RWTH Aachen Aachen Germany

   Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany

5. Katherine Jane Hewitt Aachen Department of Medicine III

   Universitätsklinikum Aachen · Else Kröner Fresenius Center for Digital Health · RWTH Aachen University

   Affiliation as printed

   Department of Medicine III University Hospital RWTH Aachen Aachen Germany

   Else Kroener Fresenius Center for Digital Health Technical University Dresden Dresden Germany

   Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany

   Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany

6. Sebastian Brandner

   Universitätsklinikum Erlangen

   Affiliation as printed

   Department of Neurosurgery University Hospital Erlangen Erlangen Germany

   Department of Neurosurgery, University Hospital Erlangen, Erlangen, Germany

7. Keno K. Bressem

   Humboldt-Universität zu Berlin · Freie Universität Berlin · Charité - Universitätsmedizin Berlin

   Affiliation as printed

   Department of Radiology Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt‐Universität zu Berlin Berlin Germany

8. Sebastian Foersch

   University Medical Center of the Johannes Gutenberg University Mainz

   Affiliation as printed

   Institute of Pathology University Medical Center Mainz Mainz Germany

   Institute of Pathology, University Medical Center Mainz, Mainz, Germany

9. Jakob Nikolas Kather corresponding

   University of Leeds · University Hospital Heidelberg · National Center for Tumor Diseases · University Hospital Carl Gustav Carus · Else Kröner Fresenius Center for Digital Health

   Affiliation as printed

   Department of Medicine I University Hospital Dresden Dresden Germany

   Else Kroener Fresenius Center for Digital Health Technical University Dresden Dresden Germany

   Medical Oncology, National Center for Tumor Diseases (NCT) University Hospital Heidelberg Heidelberg Germany

   Pathology and Data Analytics, Leeds Institute of Medical Research at St James's University of Leeds Leeds UK

   Department of Medicine I, University Hospital Dresden, Dresden, Germany

   Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany

   Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany

   Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK