Nuran Abdullayev , Jonathan Kottlors , Hikmat Habibov , Fahrettin Yilmaz , Chantal Zimmer , Nils Große Hokamp , Simon Lennartz , Vilayat Valiyev , Cavid Abbasli , Lukas Goertz , Jan Borggrefe , David Maintz , Koray Ersahin , Sebastian Sanduleanu
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引用次数: 0
Abstract
Breast cancer is the most common cancer among women worldwide. Treatment decision-making in multidisciplinary tumor boards is complex, involving integration of clinical guidelines, patient data, and preferences.
We retrospectively evaluated MammaBoardGPT, an few-shot in-context learning and Retrieval Augmented Generation (RAG) enhanced GPT-4 model with European guidelines and five Tumor Board labeled cases, against standard GPT-4 in 25 breast cancer cases discussed at a German hospital.
After recursive prompting, MammaBoardGPT achieved complete agreement with tumor board decisions in 84 % of cases, partial agreement in 16 % and no disagreements; standard GPT-4 showed 76 % complete agreement, 20 % partial agreement, and 4 % complete disagreements.
A significant difference was observed in MammaBoardGPT's (Stuart-Maxwell P = 0.0048) but not for GPT-4 agreement with tumor board decisions before versus after recursive prompting (Stuart-Maxwell P = 0.135), neither for MammaBoardGPT and GPT-4 in their tumor board decision agreement post-recursive prompting (Stuart-Maxwell P = 0.37).
These findings support MammaBoardGPT's potential for tumor board decision support, warranting prospective validation and real-time assessment.
期刊介绍:
JSO - European Journal of Surgical Oncology ("the Journal of Cancer Surgery") is the Official Journal of the European Society of Surgical Oncology and BASO ~ the Association for Cancer Surgery.
The EJSO aims to advance surgical oncology research and practice through the publication of original research articles, review articles, editorials, debates and correspondence.