Awuah Wireko Andrew, Ben-Jaafar Adam, Roy Subham, Nkrumah-Boateng Princess Afia, Tan Joecelyn Kirani, Abdul-Rahman Toufik, Atallah Oday
Department of Research, Toufik's World Medical Association, Sumy, Ukraine.
School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland.
Eur J Med Res. 2025 Jan 31;30(1):61. doi: 10.1186/s40001-025-02339-3.
Malignant gliomas, including glioblastoma, are amongst the most aggressive primary brain tumours, characterised by rapid progression and a poor prognosis. Survival analysis is an essential aspect of glioma management and research, as most studies use time-to-event outcomes to assess overall survival (OS) and progression-free survival (PFS) as key measures to evaluate patients. However, predicting survival using traditional methods such as the Kaplan-Meier estimator and the Cox Proportional Hazards (CPH) model has faced many challenges and inaccuracies. Recently, advances in artificial intelligence (AI), including machine learning (ML) and deep learning (DL), have enabled significant improvements in survival prediction for glioma patients by integrating multimodal data such as imaging, clinical parameters and molecular biomarkers. This study highlights the comparative effectiveness of imaging-based, non-imaging and combined AI models. Imaging models excel at identifying tumour-specific features through radiomics, achieving high predictive accuracy. Non-imaging approaches also excel in utilising clinical and genetic data to provide complementary insights, whilst combined methods integrate multiple data modalities and have the greatest potential for accurate survival prediction. Limitations include data heterogeneity, interpretability challenges and computational demands, particularly in resource-limited settings. Solutions such as federated learning, lightweight AI models and explainable AI frameworks are proposed to overcome these barriers. Ultimately, the integration of advanced AI techniques promises to transform glioma management by enabling personalised treatment strategies and improved prognostic accuracy.
恶性胶质瘤，包括胶质母细胞瘤，是最具侵袭性的原发性脑肿瘤之一，其特征是进展迅速且预后不良。生存分析是胶质瘤管理和研究的一个重要方面，因为大多数研究使用事件发生时间结局来评估总生存期（OS）和无进展生存期（PFS），将其作为评估患者的关键指标。然而，使用传统方法如Kaplan-Meier估计器和Cox比例风险（CPH）模型预测生存面临着许多挑战和不准确之处。最近，包括机器学习（ML）和深度学习（DL）在内的人工智能（AI）进展，通过整合多模态数据，如图像、临床参数和分子生物标志物，在胶质瘤患者的生存预测方面实现了显著改善。本研究强调了基于影像的、非影像的和联合AI模型的比较有效性。影像模型擅长通过放射组学识别肿瘤特异性特征，实现高预测准确性。非影像方法在利用临床和基因数据提供补充见解方面也表现出色，而联合方法整合了多种数据模式，在准确生存预测方面具有最大潜力。局限性包括数据异质性、可解释性挑战和计算需求，特别是在资源有限的环境中。提出了联邦学习、轻量级AI模型和可解释AI框架等解决方案来克服这些障碍。最终，先进AI技术的整合有望通过实现个性化治疗策略和提高预后准确性来改变胶质瘤的管理。
