학술논문
Extracting structured information from unstructured histopathology reports using generative pre-trained transformer 4 (GPT-4).
Document Type
Academic Journal
Author
Truhn D; Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany.; Loeffler CM; 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.; Müller-Franzes G; Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany.; Nebelung S; Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany.; Hewitt KJ; Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany.; Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany.; Brandner S; Department of Neurosurgery, University Hospital Erlangen, Erlangen, Germany.; Bressem KK; Department of Radiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.; Foersch S; Institute of Pathology, University Medical Center Mainz, Mainz, Germany.; Kather JN; Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany.; Department of Medicine I, University Hospital 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.
Source
Publisher: John Wiley And Sons Country of Publication: England NLM ID: 0204634 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1096-9896 (Electronic) Linking ISSN: 00223417 NLM ISO Abbreviation: J Pathol Subsets: MEDLINE
Subject
Language
English
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.
(© 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.)
(© 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.)