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Related Experiment Video

Updated: May 10, 2026

Models of Bone Metastasis

Models of Bone Metastasis

Published on: September 4, 2012

The Fine-Tuned Large Language Model for Extracting the Progressive Bone Metastasis from Unstructured Radiology

Noriko Kanemaru¹, Koichiro Yasaka², Nana Fujita¹

¹Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8655, Japan.

Journal of Imaging Informatics in Medicine

|August 26, 2024

Summary

This summary is machine-generated.

A fine-tuned large language model (LLM) can efficiently detect bone metastasis from Japanese radiology reports. This AI tool shows performance comparable to radiologists, significantly reducing analysis time for early patient detection and prognosis improvement.

Keywords:

Bone metastasis Deep learning Large language model

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Area of Science:

Medical Informatics
Artificial Intelligence in Radiology
Oncology

Background:

Early detection of bone metastasis is critical for improving patient prognosis.
Manual review of radiology reports for metastasis detection is time-consuming.
Large Language Models (LLMs) show potential for automating medical text analysis.

Purpose of the Study:

To evaluate the feasibility of a fine-tuned, locally run LLM for extracting bone metastasis information from unstructured Japanese radiology reports.
To compare the LLM's performance against manual annotation by radiologists.
To assess the time efficiency of LLM-based detection versus manual review.

Main Methods:

A retrospective study utilized Japanese radiology reports from three distinct periods for training, validation, and testing.
Reports were classified by radiologists into no bone metastasis, progressive bone metastasis, or stable/decreased bone metastasis.
A fine-tuned LLM was developed and tested against two independent radiologists on an under-sampled test dataset.

Main Results:

The fine-tuned LLM achieved an accuracy of 0.979, comparable to radiologists (0.996 and 0.993).
Sensitivity for detecting different metastasis groups was high for both the LLM and radiologists.
The LLM required significantly less time (105 seconds) for classification compared to manual annotation (2312 and 3094 seconds).

Conclusions:

A fine-tuned LLM can effectively extract patients with bone metastasis from Japanese radiology reports.
The LLM demonstrates satisfactory performance, comparable or slightly lower than expert radiologists.
LLM-based analysis offers a substantial time-saving advantage for early detection and prognosis improvement.