Large language model-based multi-source integration pipeline for automated diagnostic classification and zero-shot prognoses for brain tumor

作者：Zhuoqi Ma, Lulu Bi, P. Collins, Owen P. Leary, Maliha Imami, Zhusi Zhong, Shaolei Lu, Grayson L. Baird, Nikos Tapinos, Uğur Çetintemel, Harrison X. Bai, Jerrold L. Boxerman, Zhicheng Jiao · 发表于：Meta-Radiology · 年份：2025 · DOI：10.1016/j.metrad.2025.100150 · 被引用次数：5 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Brain Tumor Detection and Classification、AI in cancer detection

In this study, we use large language models (LLMs) to integrate information from multi-source medical reports to enhance the accuracy of automated diagnostic classification and prognosis for brain tumors. Brain MRI reports from a cohort of 426 brain tumor patients were manually labeled for tumor presence and stability. Pathology reports from the same cohort were incorporated as an additional information source. A pre-trained LLM was used to extract features from the multi-source reports, and a Multi-layer perceptron (MLP) was trained for classification tasks. Model performance was evaluated on the test set using Micro F1 scores and AUROCs. The model’s zero-shot prognostic capability was validated on an independent cohort of 33 glioblastoma patients. Micro F1-score 0.849 (95%CI: 0.814, 0.880) for tumor presence classification and 0.929 (95%CI: 0.904, 0.954) for tumor stability classification are reached. Compared to using solely radiology reports, the developed model showed improvements on Micro F1 of 10.4 % for tumor presence and 5.6 % for stability classification. Log-rank tests confirmed significant distinction between the high- and low-risk patient groups stratified by model-predicted “Tumor Stability” label ( p -value = 0.017), confirming the prognostic value of the model-generated labels. This study developed a multi-source integration model based on LLMs for automated diagnostic classification and zero-shot prognosis of brain tumors. The integration of multi-source repo...