Evidence map›Paper›PMID 42423906›Full record

ArticleJapanese journal of radiology2026

Open-source offline-deployable retrieval-augmented large language model for assisting pancreatic cancer staging.

Hisashi Johno, Akitomo Amakawa, Atsushi Komaba, Ryota Tozuka, Yuki Johno, Junichi Sato, Kentaro Yoshimura, Kazunori Nakamoto, Shintaro Ichikawa

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Article in Japanese journal of radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Hisashi JohnoDepartment of Diagnostic Radiology, Faculty of Medicine, University of Yamanashi, 1110 Shimokato, Chuo, Yamanashi, Japan. johnoh@yamanashi.ac.jp.ORCID http://orcid.org/0000-0002-8058-1401
Akitomo AmakawaDepartment of Diagnostic Radiology, Faculty of Medicine, University of Yamanashi, 1110 Shimokato, Chuo, Yamanashi, Japan.
Atsushi KomabaDepartment of Diagnostic Radiology, Faculty of Medicine, University of Yamanashi, 1110 Shimokato, Chuo, Yamanashi, Japan.
Ryota TozukaDepartment of Therapeutic Radiology, Faculty of Medicine, University of Yamanashi, Yamanashi, Japan.
Yuki JohnoDepartment of Internal Medicine, Kyonan Medical Center Fujikawa Hospital, Yamanashi, Japan.
Junichi SatoDepartment of Diagnostic Radiology, Faculty of Medicine, University of Yamanashi, 1110 Shimokato, Chuo, Yamanashi, Japan.
Kentaro YoshimuraDivision of Molecular Biology, Center for Medical Education and Sciences, Faculty of Medicine, University of Yamanashi, Yamanashi, Japan.
Kazunori NakamotoDivision of Mathematics, Center for Medical Education and Sciences, Faculty of Medicine, University of Yamanashi, Yamanashi, Japan.
Shintaro IchikawaDepartment of Diagnostic Radiology, Faculty of Medicine, University of Yamanashi, 1110 Shimokato, Chuo, Yamanashi, Japan.

Funding

Japan Society for the Promotion of Science JP24K06686
6 · The paper itself

Abstract

purposeLarge language models (LLMs) are increasingly applied in radiology, but key challenges remain, including data leakage from cloud-based systems, false outputs, and limited reasoning transparency. This study aimed to develop an open-source, offline-deployable retrieval-augmented LLM (RA-LLM) system in which local execution prevents data leakage and retrieval-augmented generation (RAG) improves output accuracy and transparency using reliable external knowledge (REK), demonstrated in pancreatic cancer staging. MATERIALS AND

methodsLlama-3.2 11B and Gemma-3 27B were used as local LLMs, and GPT-4o mini served as a cloud-based comparator. The Japanese pancreatic cancer guideline, written in English, served as REK. Relevant REK excerpts were retrieved to generate retrieval-augmented responses. System performance, including classification accuracy, retrieval metrics, and execution time, was evaluated on 100 simulated pancreatic cancer cases with CT findings described in English, using non-RAG LLMs as baselines. McNemar tests were applied to TNM staging and resectability classification.

resultsRAG improved TNM staging accuracy for all LLMs (GPT-4o mini 61% → 90%, p < 0.001; Llama-3.2 11B 53% → 72%, p < 0.001; Gemma-3 27B 59% → 87%, p < 0.001) and mildly improved resectability classification for GPT-4o mini and Llama-3.2 11B (72% → 84%, p = 0.012; 58% → 73%, p = 0.006), while the improvement for Gemma-3 27B was not evident (77% → 86%, p = 0.093). Gemma-3 27B showed performance comparable to GPT-4o mini. Retrieval performance was high (context recall = 1; context precision = 0.5-1), and local models ran at speeds comparable to the cloud-based GPT-4o mini.

conclusionWe developed an offline-deployable RA-LLM system for pancreatic cancer staging and publicly released its full source code. RA-LLMs outperformed baseline LLMs, and the offline-capable Gemma-3 27B performed comparably to the widely used cloud-based GPT-4o mini.

Indexed as

Large language modelsOffline deploymentOpen-source softwarePancreatic cancer stagingRetrieval-augmented generation

Identifiers

PMID42423906

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.