AI-driven multi-modal framework for prognostic modeling in glioblastoma: Enhancing clinical decision support

IF 4.9 2区医学 Q1 ENGINEERING, BIOMEDICAL

Computerized Medical Imaging and Graphics Pub Date : 2025-08-07 DOI:10.1016/j.compmedimag.2025.102628

Zihan Zhao , Nguyen Quoc Khanh Le , Matthew Chin Heng Chua

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引用次数: 0

Abstract

Objective

Glioblastoma (GBM) is the most aggressive malignant brain tumor, associated with poor prognosis and limited therapeutic options. Accurate prognostic modeling is essential for guiding personalized treatment strategies. However, existing models often rely on single-modality data, limiting their ability to capture the complex molecular and histopathological heterogeneity of GBM. This study proposes an AI-driven, multi-modal framework for clinical decision support, encompassing both early triage and prognostic evaluation stages. A Vision Transformer (ViT) is first employed to classify tumor grades (WHO grades 2–4) using radiological images. Subsequently, an attention-based deep learning model integrates histopathological and transcriptomic data to improve risk stratification and inform treatment planning.

Methods

The ViT model was trained on FLAIR MRI scans from the UCSF-PDGM dataset to perform tumor grading during the early triage phase. For prognostic modeling, whole-slide histopathological images and RNA sequencing profiles were integrated using an attention-based deep learning architecture. Model performance was evaluated using the area under the curve (AUC), concordance index (C-index), and Kaplan–Meier survival analysis across independent CPTAC-GBM and TCGA-GBM cohorts.

Results

The ViT model achieved F1-scores exceeding 0.89 across all WHO tumor grades. The multi-modal model significantly outperformed single-modality baselines, demonstrating higher C-index values and superior prognostic accuracy. Kaplan–Meier analysis revealed statistically significant differences (p < 0.0001) between high- and low-risk patient groups.

Conclusion

This AI-enabled, multi-modal framework improves clinical decision support in GBM by enabling accurate risk stratification and treatment planning. The integration of radiological imaging, histopathology, and transcriptomics offers a comprehensive and personalized approach to GBM prognosis.

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人工智能驱动的胶质母细胞瘤预后建模多模式框架：增强临床决策支持

目的胶质母细胞瘤（GBM）是最具侵袭性的恶性脑肿瘤，预后差，治疗选择有限。准确的预后建模对于指导个性化治疗策略至关重要。然而，现有的模型往往依赖于单模态数据，限制了它们捕捉GBM复杂的分子和组织病理学异质性的能力。本研究提出了一个人工智能驱动的多模式临床决策支持框架，包括早期分诊和预后评估阶段。首先使用视觉转换器（Vision Transformer, ViT）根据放射图像对肿瘤分级（WHO分级2-4）进行分类。随后，基于注意力的深度学习模型集成了组织病理学和转录组学数据，以改善风险分层并为治疗计划提供信息。方法采用UCSF-PDGM数据集的FLAIR MRI扫描对ViT模型进行训练，在早期分诊阶段进行肿瘤分级。为了进行预后建模，使用基于注意力的深度学习架构整合了整片组织病理学图像和RNA测序图谱。通过独立的CPTAC-GBM和TCGA-GBM队列的曲线下面积（AUC）、一致性指数（C-index）和Kaplan-Meier生存分析来评估模型的性能。结果ViT模型在所有WHO肿瘤分级中均达到f1评分超过0.89。多模态模型明显优于单模态基线，显示出更高的c指数值和更高的预后准确性。Kaplan-Meier分析显示，高危组和低危组之间存在统计学差异（p <； 0.0001）。结论：该人工智能支持的多模式框架通过实现准确的风险分层和治疗计划，提高了GBM的临床决策支持。放射影像学、组织病理学和转录组学的整合为GBM的预后提供了一个全面和个性化的方法。

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来源期刊

Computerized Medical Imaging and Graphics 医学-核医学

CiteScore

10.70

自引率

3.50%

发文量

审稿时长

26 days

期刊介绍： The purpose of the journal Computerized Medical Imaging and Graphics is to act as a source for the exchange of research results concerning algorithmic advances, development, and application of digital imaging in disease detection, diagnosis, intervention, prevention, precision medicine, and population health. Included in the journal will be articles on novel computerized imaging or visualization techniques, including artificial intelligence and machine learning, augmented reality for surgical planning and guidance, big biomedical data visualization, computer-aided diagnosis, computerized-robotic surgery, image-guided therapy, imaging scanning and reconstruction, mobile and tele-imaging, radiomics, and imaging integration and modeling with other information relevant to digital health. The types of biomedical imaging include: magnetic resonance, computed tomography, ultrasound, nuclear medicine, X-ray, microwave, optical and multi-photon microscopy, video and sensory imaging, and the convergence of biomedical images with other non-imaging datasets.