{"ID":2845760,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.03376","arxiv_id":"2511.03376","title":"Computational Imaging Meets LLMs: Zero-Shot IDH Mutation Prediction in Brain Gliomas","abstract":"We present a framework that combines Large Language Models with computational image analytics for non-invasive, zero-shot prediction of IDH mutation status in brain gliomas. For each subject, coregistered multi-parametric MRI scans and multi-class tumor segmentation maps were processed to extract interpretable semantic (visual) attributes and quantitative features, serialized in a standardized JSON file, and used to query GPT 4o and GPT 5 without fine-tuning. We evaluated this framework on six publicly available datasets (N = 1427) and results showcased high accuracy and balanced classification performance across heterogeneous cohorts, even in the absence of manual annotations. GPT 5 outperformed GPT 4o in context-driven phenotype interpretation. Volumetric features emerged as the most important predictors, supplemented by subtype-specific imaging markers and clinical information. Our results demonstrate the potential of integrating LLM-based reasoning with computational image analytics for precise, non-invasive tumor genotyping, advancing diagnostic strategies in neuro-oncology. The code is available at https://github.com/ATPLab-LUMS/CIM-LLM.","short_abstract":"We present a framework that combines Large Language Models with computational image analytics for non-invasive, zero-shot prediction of IDH mutation status in brain gliomas. For each subject, coregistered multi-parametric MRI scans and multi-class tumor segmentation maps were processed to extract interpretable semantic...","url_abs":"https://arxiv.org/abs/2511.03376","url_pdf":"https://arxiv.org/pdf/2511.03376v1","authors":"[\"Syed Muqeem Mahmood\",\"Hassan Mohy-ud-Din\"]","published":"2025-11-05T11:31:08Z","proceeding":"eess.IV","tasks":"[\"eess.IV\",\"cs.AI\",\"q-bio.QM\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false,"code_links":[{"ID":607381,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2845760,"paper_url":"https://arxiv.org/abs/2511.03376","paper_title":"Computational Imaging Meets LLMs: Zero-Shot IDH Mutation Prediction in Brain Gliomas","repo_url":"https://github.com/ATPLab-LUMS/CIM-LLM","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}