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Front. Oncol. 15:1665690 .  \ndoi: 10.3389/fonc.2025.1665690  \nCOPYRIGHT  \n© 2025 Xie, Sun, Liang, Shang, Wang, Wang, Wei, Chen, Zhang and Niu. This is an openaccess article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nPredicting isocitrate dehydrogenase status in glioma using hierarchical attentionbased deep 3D multiple instance learning  \nQinqin Xie 1,2, Yongheng Sun 3, Yuxia Liang 1, Yu Shang 1, Haifeng Wang 3, Fan Wang 3, Rong Wei 2, Bin Chen 4, Ming Zhang 1 and Chen Niu 1,2*  \n1 PET-CT Center, The First Affiliated Hospital of Xi ’an Jiaotong University, Xi ’an, China, 2 Department of Network Information, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China, 3School of Mathematics and Statistics, Xi’an Jiaotong University, Xi ’an, China, 4 Data Center, Hangzhou First People’s Hospital, Hangzhou, China  \nBackground: According to the 2021 WHO classification of tumors of the central nervous system, isocitrate dehydrogenase (IDH) status serve an independent prognostic biomarker and is closely associated with tumor diagnosis and treatment response. At present, the determination of IDH status still relies on invasive surgical procedures.  \nMethod: A total of 345 patients with pathologically confirmed gliomas diagnosed at the First Affiliated Hospital of Xi’an Jiaotong University between October 2019 and October 2024 were retrospectively included, comprising 148 (42 . 9%) IDHwild and 197 (57 . 1%) IDH-mutant. An additional 495 glioma patients were obtained from the public TCIA dataset. Patients were randomly split into training, validation, and test cohorts 6:2:2 . A Hierarchical Attention-Based Multiple Instance Learning (HAB-MIL) framework was developed, integrating auxiliary positional encoding into feature maps to capture spatially specific information and generate refined 3D lesion representations . Model performance was evaluated using five-fold cross-validation, with receiver operating characteristic (ROC) curves, area under the curve (AUC), sensitivity, and specificity as assessment metrics.  \nResult: HAB-MIL achieved competitive performance, with AUCs of 0 . 917 and 0. 892 on the glioma datasets from TCIA and the First Affiliated Hospital of Xi’an Jiaotong University. Additionally, our work achieves results that are comparable to the state-of-the-art methods in TCIA dataset and demonstrates that multiple instance learning has great potential for IDH prediction.  \nConclusion: The proposed HAB-MIL achieved IDH classification based on conventional preoperative MRI images, eliminating the need for pixel-level annotations and significantly reducing the annotation burden for doctors.  \nKEYWORDS  \ndynamic gated attention, glioma, IDH, location encoding, Multiple Instance Learning  \nFrontiers in Oncology 01 [frontiersin.org](frontiersin.org)  \n1 Introduction  \nGliomas, the most prevalent malignant tumor in the central nervous system,","cbCaiqGnxlIn1LhE","https://ap.wps.com/l/cbCaiqGnxlIn1LhE","pdf",5719386,13,"English","# Background\n## Materials and methods\n## Results\n## Conclusion\n# Keywords","[{\"question\":\"Does the method require pixel-level annotations?\",\"answer\":\"No. The model eliminates the need for pixel-level annotations by using multiple instance learning, which significantly reduces the annotation burden for clinicians.\"}]","Predicting isocitrate dehydrogenase status in glioma using hierarchical attention-based deep 3D multiple instance learning | PDF",1790582821,33]