Text-based prediction of ımmunohistochemical biomarkers in breast cancer using a generative large language model: a...

Immunohistochemical (IHC) biomarkers such as estrogen receptor (ER), progesterone receptor (PR), HER2, and Ki-67 are essential for the classification and treatment of breast cancer. While radiomics-based models have demonstrated potential in non-invasive biomarker prediction, the utility of large language models (LLMs) for this task using only textual clinical data remains largely unexplored. This study aimed to evaluate the performance of ChatGPT-4o, a generative LLM, in predicting key IHC biomarkers based solely on structured radiological and pathological reports.

Fifty-five patients with breast cancer were retrospectively analyzed. For each patient, structured clinical, imaging, and pathology reports-excluding IHC data-were entered into ChatGPT-4o. The model was prompted to generate predictions for ER, PR, HER2, and Ki-67 expression. Predictions were repeated at two time points to assess reproducibility. Diagnostic performance was compared to pathology results using accuracy, sensitivity, specificity, and Cohen's kappa.

The model achieved the highest accuracy for HER2 prediction (83.6%, κ = 0.51), followed by ER (81.8%, κ = 0.44) and PR (76.4%, κ = 0.39). For high Ki-67 expression, the sensitivity was 88.9% with moderate overall agreement (κ = 0.55). Inter-prediction agreement was substantial to almost perfect for all biomarkers (κ = 0.69-0.83).

ChatGPT-4o successfully predicted IHC biomarker status using only structured textual data. Its performance was comparable to radiomics models, offering a feasible and accessible AI tool to support early clinical decision-making, especially in resource-limited settings or before IHC results are available.