Rule-aware reasoning visual language model for molecular prediction in adult-type diffuse glioma lacking contrast enhancement

Accurate preoperative prediction of isocitrate dehydrogenase(IDH) mutation and 1p/19q codeletion is crucial for treatment planning in adult-type diffuse glioma lacking contrast enhancement(ADG-LCE). Existing methods face a trade-off between interpretability and automation. We developed a visual language model (VLM) framework for interpretable molecular prediction in ADG-LCE using standard CT and MRI.

This retrospective multi-center study included 873 pathologically confirmed ADG-L\CE patients. Molecular Inference-Guided Semantic Vision–Language Model(MIG-SemVLM) extracted five semantic imaging features (T2-FLAIR mismatch sign, cortical involvement, calcification, FLAIR homogeneity, and tumor border), which were integrated with age and ADC ratio via a rule-aware LLM following a simple scoring system. Model performance was evaluated by AUC, accuracy, F1-score, and balanced accuracy, with human-AI collaboration assessed in 300 cases.

MIG-SemVLM achieved excellent semantic extraction performance across five features (AUC range: 0.9215–0.9536). The rule-aware LLM demonstrated strong molecular prediction on the test set: IDH mutation (AUC: 0.9349, accuracy: 0.8806) and 1p/19q codeletion (AUC: 0.9126, accuracy: 0.8538). AI assistance was associated with improved diagnostic accuracy among readers (IDH: +7.0%, 1p/19q: +7.0%) and reduced decision time by 56.2 s per case in the exploratory human–AI reader study.

The proposed VLM framework with rule-aware reasoning provides accurate, interpretable molecular prediction in ADG-LCE, benefiting less experienced readers in clinical decisions. This work was supported by the Chongqing Natural Science Foundation General Project, Grant No. CSTB2025NSCQ-GPX1232; National Natural Science Foundation of China, Grant No. 82672488, and Science and technology research project of Chongqing Municipal Education Commission, Grant No.

Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China Department of Radiology, Sichuan Provincial Woman’s and Children’s Hospital/The Affiliated Women’s and Children’s Hospital of Chengdu Medical College, Chengdu, China College of Computer & Information Science, Southwest University, Chongqing, China Department of Radiology, The People’s Hospital of Shifang, Deyang, China Graduate Studies Management, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China Department of Radiology, The Affiliated Hospital of Southwest Medical university, Luzhou, China Department of Radiology, Daping Hospital, Army Medical University, Chongqing, China Department of Radiology, 7T Magnetic Resonance Translational Medicine Research Center, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China Department of Radiology, Chongqing Hospital of Traditional Chinese Medicine, Chongqing, China Department of Radiology, The 958th Army Hospital of the Chinese People’s Liberation Army, Chongqing, China Department of Radiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, China Correspondence to Zhi Liu, Xinyi Xu, Zhipeng Wen, Chengling Huang or Yongmei Li. The authors declare no competing interests. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material.