Abstract B018: Practical benchmarking of large language models for structuring synthetic prostate cancer histopathology statements in English and Finnish

Abstract Large Language Models (LLMs) are increasingly applied to clinical oncology tasks, such as structuring free-text format histopathology statements. Given the rapid pace of LLM development, guidance is needed for optimal model, parameter, and prompt design choices, especially in minority languages. A total of 100 novel synthetic histopathological prostate cancer (PCa) statements were manually generated to mimic real-life data of Helsinki University Hospital. First, 25 statements were generated in Finnish and manually translated to English. Manually censored versions were then generated, with key information replaced by filler text to test hallucinations. The following models were evaluated: Claude (3 variants of 3.5 Haiku/Sonnet and 3.7 Sonnet-20250219), DeepSeek (R1), Gemini (1.5 Pro with 2 snapshots, 2.0 flash-001 and flash-lite-001), GPT (4o with 3 snapshots and 4.1 normal, mini and nano), Grok (3-beta and 2-1212), Llama 4 (Maverick and Scout), and Mistral (large-2411). Each model was tested with zero-shot (no examples), one-shot (one example in prompt), and few-shot prompts (one positive and one negative example), and queried for three structured elements in JSON format: sample type (TURP/RP/Biopsy), malignancy status (Yes/No), and Gleason score (major+minor=sum). Missing values were requested as NA. All queries were replicated thrice with temperature-parameter 0. F-score (F) was used to assess model performance. Python v3.12 was used to construct experimental data via APIs followed by statistical analysis in R v4.4. All data and code is made available via GitHub. All models benefited from the few-shot prompt design, thus results are here summarized for the few-shot prompt. Median (IQR) F was 0.76 (0.70-0.86) in English and 0.75 (0.69-0.80) in Finnish across all models. Top proprietary model was GPT-4o, with all snapshots within F 0.96-0.98 in English and F 0.92-0.94 in Finnish. Top open weight model was Mistral with F 0.87 / 0.75 in English / Finnish, followed by DeepSeek’s R1 with F 0.80 / 0.75. Most notable underperformer was GPT-4.1 nano with F 0.47 / 0.37 in English / Finnish. Gemini 1.5 Pro snapshot 001 vs 002 had notable variance with F 0.71 / 0.69 vs F 0.62 / 0.64 similarly as Claude 3.5 Sonnet 20240620 vs. 20241022 with F 0.66 / 0.69 vs F 0.77 / 0.74 in English / Finnish. Our results from PCa histopathology highlight that current state-of-the-art LLMs are agnostic to extracting data from a minority language (Finnish) in comparison with the de facto largest corpus training language (English). Few-shot prompts systematically performed best, highlighting the importance of prompt engineering. While top-performer emerged from the GPT-family, open weight models such as Mistral and DeepSeek-R1 hold potential, especially since they can be locally deployed to sensitive data settings without internet access. Further, researchers ought to carefully choose their model, as light-weight models may result in poor performance (GPT-4.1 nano) and performance can vary notably between time-dependent snapshots (Gemini 1.5 Pro and Claude 3.5 Sonnet). Citation Format: Tolou Shadbahr, Antti S. Rannikko, Tuomas Mirtti, Teemu D. Laajala. Practical benchmarking of large language models for structuring synthetic prostate cancer histopathology statements in English and Finnish [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr B018.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.