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

Truhn D, Loeffler CM, Müller-Franzes G, Nebelung S, Hewitt KJ, Brandner S, Bressem KK, Foersch S, Kather JN (2023)

Publication Type: Journal article

Publication year: 2023

Journal

Journal of Pathology Wiley

DOI: 10.1002/path.6232

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 with CRIS profile

Sebastian Brandner Department of Neurosurgery

Involved external institutions

Universitätsklinikum Aachen (UKA) DE Germany (DE) Technische Universität Dresden DE Germany (DE) Charité - Universitätsmedizin Berlin DE Germany (DE) Universitätsmedizin der Johannes Gutenberg-Universität Mainz DE Germany (DE)

How to cite

APA:

Truhn, D., Loeffler, C.M., Müller-Franzes, G., Nebelung, S., Hewitt, K.J., Brandner, S.,... Kather, J.N. (2023). Extracting structured information from unstructured histopathology reports using generative pre-trained transformer 4 (GPT-4). Journal of Pathology. https://doi.org/10.1002/path.6232

MLA:

Truhn, Daniel, et al. "Extracting structured information from unstructured histopathology reports using generative pre-trained transformer 4 (GPT-4)." Journal of Pathology (2023).

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