Performance of a large language model in lymphoma stage assignment based on written PET/CT reports

Introduction Large language models (LLMs) have emerged as powerful tools for addressing a wide range of tasks across diverse domains. Growing evidence suggests that they can play a significant role in patient self-education and the choice of diagnostic work-up [ 1 ] [ 2 ]. Moreover, artificial intelligence (AI) might be helpful for the classification of abnormal clinical findings and imaging patterns observed [ 3 ] [ 4 ]. Accurate stage definition is crucial in lymphoma, as therapy regimens are chosen according to disease extent and risk factors. Over the past decades, positron emission tomography (PET) combined with computed tomography (CT) has become an essential part of pre-treatment assessment [ 5 ]. However, medical reports often contain complex descriptions of tumor characteristics such as size and location. Text-processing AI tools have the potential to assist with the categorization of these findings. We therefore investigated the performance of an advanced LLM in Ann Arbor stage assignment based on imaging documentation. Publication History Received: 27 May 2025 Accepted after revision: 22 September 2025 Article published online: 14 October 2025 © 2025. Thieme. All rights reserved. Georg Thieme Verlag KG Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany