Ahanger Abdul Basit, Aalam Syed Wajid, Masoodi Tariq Ahmad, Shah Asma, Khan Meraj Alam, Bhat Ajaz A, Assad Assif, Macha Muzafar Ahmad, Bhat Muzafar Rasool
Department of Computer Science, Islamic University of Science and Technology (IUST), Kashmir, 192122, India.
Human Immunology Department, Research Branch, Sidra Medicine, Doha, Qatar.
J Transl Med. 2025 Jan 27;23(1):121. doi: 10.1186/s12967-025-06101-5.
Glioblastoma (GBM) is a highly aggressive brain tumor associated with a poor patient prognosis. The survival rate remains low despite standard therapies, highlighting the urgent need for novel treatment strategies. Advanced imaging techniques, particularly magnetic resonance imaging (MRI), are crucial in assessing GBM. Disruptions in various oncogenic signaling pathways, such as Receptor Tyrosine Kinase (RTK)-Ras-Extracellular signal-regulated kinase (ERK) signaling, Phosphoinositide 3- Kinases (PI3Ks), tumor protein p53 (TP53), and Neurogenic locus notch homolog protein (NOTCH), contribute to the development of different tumor types, each exhibiting distinct morphological and phenotypic features that can be observed at a microscopic level. However, identifying genetic abnormalities for targeted therapy often requires invasive procedures, prompting exploration into non-invasive approaches like radiogenomics. This study explores the utility of radiogenomics and machine learning (ML) in predicting these oncogenic signaling pathways in GBM patients.
We collected post-operative MRI scans (T1w, T1c, FLAIR, T2w) from the BRATS-19 dataset, including scans from patients with both GBM and LGG, linked to genetic and clinical data via TCGA and CPTAC. Signaling pathway data was manually extracted from cBioPortal. Radiomic features were extracted from four MRI modalities using PyRadiomics. Dimensionality reduction and feature selection were applied and Data imbalance was addressed with SMOTE. Five ML models were trained to predict signaling pathways, with Grid Search optimizing hyperparameters and 5-fold cross-validation ensuring unbiased performance. Each model's performance was evaluated using various metrics on test data.
Our results showed a positive association between most signaling pathways and the radiomic features derived from MRI scans. The best models achieved high AUC scores, namely 0.7 for RTK-RAS, 0.8 for PI3K, 0.75 for TP53, and 0.4 for NOTCH, and therefore, demonstrated the potential of ML models in accurately predicting oncogenic signaling pathways from radiomic features, thereby informing personalized therapeutic approaches and improving patient outcomes.
We present a novel approach for the non-invasive prediction of deregulation in oncogenic signaling pathways in glioblastoma (GBM) by integrating radiogenomic data with machine learning models. This research contributes to advancing precision medicine in GBM management, highlighting the importance of integrating radiomics with genomic data to understand tumor behavior and treatment response better.
胶质母细胞瘤（GBM）是一种侵袭性很强的脑肿瘤，患者预后较差。尽管采用了标准疗法，但其生存率仍然很低，这凸显了对新型治疗策略的迫切需求。先进的成像技术，尤其是磁共振成像（MRI），在评估GBM方面至关重要。各种致癌信号通路的破坏，如受体酪氨酸激酶（RTK）-Ras-细胞外信号调节激酶（ERK）信号通路、磷酸肌醇3激酶（PI3K）、肿瘤蛋白p53（TP53）和神经源性Notch同源蛋白（NOTCH），促成了不同肿瘤类型的发展，每种肿瘤类型都表现出在微观水平上可观察到的独特形态和表型特征。然而，识别用于靶向治疗的基因异常通常需要侵入性程序，这促使人们探索像放射基因组学这样的非侵入性方法。本研究探讨了放射基因组学和机器学习（ML）在预测GBM患者这些致癌信号通路方面的效用。
我们从BRATS-19数据集中收集了术后MRI扫描（T1w、T1c、FLAIR、T2w），包括GBM和低级别胶质瘤（LGG）患者的扫描数据，并通过TCGA和CPTAC将其与基因和临床数据相链接。信号通路数据是从cBioPortal手动提取的。使用PyRadiomics从四种MRI模态中提取放射组学特征。应用了降维和特征选择，并使用SMOTE解决数据不平衡问题。训练了五个ML模型来预测信号通路，通过网格搜索优化超参数，并采用五折交叉验证确保无偏性能。使用各种指标在测试数据上评估每个模型的性能。
我们的结果显示，大多数信号通路与MRI扫描得出的放射组学特征之间存在正相关。最佳模型获得了较高的AUC分数，即RTK-RAS为0.7，PI3K为0.8，TP53为0.75，NOTCH为0.4，因此证明了ML模型在根据放射组学特征准确预测致癌信号通路方面的潜力，从而为个性化治疗方法提供依据并改善患者预后。
我们提出了一种通过将放射基因组数据与机器学习模型相结合，对胶质母细胞瘤（GBM）致癌信号通路失调进行非侵入性预测的新方法。本研究有助于推进GBM管理中的精准医学，突出了将放射组学与基因组数据相结合以更好地理解肿瘤行为和治疗反应的重要性。
