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Related Experiment Video

Updated: Jan 7, 2026

Evaluation of Biomarkers in Glioma by Immunohistochemistry on Paraffin-Embedded 3D Glioma Neurosphere Cultures

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Published on: January 9, 2019

Predicting isocitrate dehydrogenase status in glioma using hierarchical attention-based deep 3D multiple instance

Qinqin Xie^1,2, Yongheng Sun³, Yuxia Liang¹

¹PET-CT Center, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.

Frontiers in Oncology

|January 5, 2026

Summary

This study introduces a novel Hierarchical Attention-Based Multiple Instance Learning (HAB-MIL) framework for predicting isocitrate dehydrogenase (IDH) status in gliomas using MRI scans. The HAB-MIL model accurately classifies IDH status, reducing the need for invasive procedures.

Keywords:

IDH Multiple Instance Learning dynamic gated attention glioma location encoding

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Last Updated: Jan 7, 2026

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

Area of Science:

Neuro-oncology
Medical Imaging Analysis
Machine Learning in Medicine

Background:

Isocitrate dehydrogenase (IDH) status is a critical prognostic biomarker for central nervous system tumors, influencing diagnosis and treatment.
Current methods for determining IDH status in gliomas are invasive, relying on surgical biopsies.
The 2021 WHO classification highlights the importance of IDH status in glioma management.

Purpose of the Study:

To develop a non-invasive method for predicting IDH status in gliomas.
To evaluate the efficacy of a novel Hierarchical Attention-Based Multiple Instance Learning (HAB-MIL) framework using preoperative MRI.
To reduce the reliance on invasive surgical procedures for IDH status determination.

Main Methods:

A retrospective cohort of 345 glioma patients from Xi'an Jiaotong University and 495 from the TCIA dataset were included.
A Hierarchical Attention-Based Multiple Instance Learning (HAB-MIL) framework was developed, incorporating positional encoding for 3D lesion representation.
Model performance was assessed using five-fold cross-validation, ROC curves, AUC, sensitivity, and specificity.

Main Results:

The HAB-MIL framework achieved high performance with AUCs of 0.917 (TCIA) and 0.892 (Xi'an Jiaotong University).
The model's performance is comparable to state-of-the-art methods on the TCIA dataset.
Multiple instance learning demonstrates significant potential for accurate IDH prediction in gliomas.

Conclusions:

The HAB-MIL framework enables IDH classification using conventional preoperative MRI, eliminating the need for pixel-level annotations.
This approach significantly reduces the annotation burden for clinicians.
The study presents a promising non-invasive tool for glioma IDH status assessment.