<p>Analysis of Large Language Models for Magnetic Resonance Imaging Brain Tumor Segmentation Reporting</p>

This paper builds an end-to-end model that automates the process of analyzing and reporting brain tumors based on multiparametric MRI scans, which is a step toward overcoming the bottleneck of translating specific imaging health records into specific clinical reports in neuroradiology. The pipeline takes post-treatment glioma cases from the Multimodal Brain Tumor Image Segmentation (BraTS) 2024 dataset, which has been divided into 80% for training and 20% for validation, and Swin UNETR is used for 3D segmentation of tumor subregions to improve the identification of the tumor, tumor core, and whole tumor, followed by the extraction of quantitative features of subregion volumes, geometric centroids, and radiomics descriptors. These structured queries prompt three large language models: GPT-5 for higher-level diagnostic reasoning, Claude 4 for safety-conscious, clinically aligned phrasing, and Llama 4 for concise, high-throughput triage reporting. The methodology entails using high-precision segmentation and valid multi-metric assessment, which consists of Dice similarity for visual consistency and BERTScore for semantic consistency, GREEN score for clinical safety, and RadGraph F1 for factual entity-relation overlap. The comparative analysis compares the models on the basis of diagnostic reasoning, liability-conscious documentation, and rapid triage use cases and indicates the task-specific advantages that can be used to direct clinical deployment. The framework bridges the image-to-text gap that has existed in neuroradiology, with multimodal integration providing production-scale artificial intelligence suitable for picture archiving and communication systems and demonstrating that model selection should be based on workflow priorities - depth of analysis versus generation speed. This method creates a clinically translational standard for neuro-oncology reporting, which can provide real-time support for treatment planning with quantitative confidence and informative results.