Performance of GPT-based large language models in hepatocellular carcinoma stratification: liver function assessment, BCLC staging, and treatment recommendations
Abstract
Abstract Large language models (LLMs) like GPT have been proposed to support complex clinical decision-making. This study evaluated the performance of GPT-based LLM in analyzing clinical, radiological, and laboratory data from patients with hepatocellular carcinoma (HCC) to assess liver function, assign BCLC stage, and recommend treatment. Data from 106 HCC patients (82% male, median age 65 [22–86]) were compiled into anonymized integrated reports. Four GPT-versions (4, o1, o3, 5.4) were prompted—using both short and long instructions—to calculate MELD, ALBI, and Child–Pugh scores, assign BCLC stage, and generate treatment recommendations based on current guidelines. Outputs were compared to expert consensus and tumor board decisions. Errors were categorized by type and source. Time and cost analyses compared GPT to clinical staff. All GPT versions achieved high accuracy (> 85%) in liver function assessment, with MELD calculation being the most error-prone. BCLC staging accuracy ranged from 46.2% (version 4) to 84.0% (o3), with misclassification of radiological reports as the main error source. Reasoning-optimized models (o1, o3) performed best for treatment recommendations, achieving an overall accuracy (correct suggestions and acceptable alternatives) of up to 90.6%. In 9–14% of cases, GPT suggestions were retrospectively more guideline-concordant than tumor board decisions. GPT processing was significantly faster and reduced costs by approximately 300- to 1300-fold compared to clinical staff. GPT-based LLMs show potential as decision-support tools for liver function assessment, BCLC staging, and treatment guidance in HCC. Particularly with reasoning-optimized models and detailed prompting, LLMs may serve as valuable adjuncts in multidisciplinary HCC workflows. However, a non-negligible error rate requires expert oversight and further model refinement.
Article Details
Authors (9)
Max Masthoff
Amelie Zipser
Michael Praktiknjo
Jonel Trebicka
Department of Internal Medicine B, University of Münster
Haluk Morgül
Andreas Pascher
From Bielefeld University, Medical School and University Medical Center Ostwestfalen-Lippe, Campus Hospital Lippe, Detmold, Germany (J.H.); the Department of Radiation Oncology, Medical University of Graz, Graz, Austria (T.B.); the Clinical Trials Unit, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany (C.S.); the Institute of Surgical Pathology, University Medical Center Freiburg, Germany (P.B.); the Department of Surgery, University Medical Center Schleswig-Holstein–Campus Lübeck, Lübeck, Germany (B.K., T.K.); Comprehensive Cancer Center Augsburg, Faculty of Medicine, University of Augsburg, Augsburg, Germany (R.C.); the Department of General and Visceral Surgery, University Medical Center Freiburg, Freiburg, Germany (S.U.); the Department of General, Visceral, and Thoracic Surgery, University Medical Center Hamburg–Eppendorf, Hamburg, Germany (J.R.I.); the Department of Gastrointestinal Surgery, IRCCS San Raffaele Scientific Institute and San Raffaele Vita-Salute Universi...
Gesa Pöhler
Michael Köhler
Philipp Schindler