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

Publikation: Beitrag in FachzeitschriftForschungsartikelBeigetragenBegutachtung

Beitragende

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.

Details

OriginalspracheEnglisch
Seitenumfang10
FachzeitschriftJournal of pathology
Jahrgang262
Ausgabenummer3
PublikationsstatusVeröffentlicht - 2023
Peer-Review-StatusJa

Externe IDs

Mendeley e5fceb30-77fd-3047-a650-21d3ae19355b

Schlagworte

Ziele für nachhaltige Entwicklung

ASJC Scopus Sachgebiete

Schlagwörter

  • artificial intelligence, large language models, named entity recognition, natural language processing, pathology report, text mining