Tasci Erdal, Zhuge Ying, Zhang Longze, Ning Holly, Cheng Jason Y, Miller Robert W, Camphausen Kevin, Krauze Andra V
Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, 9000 Rockville Pike, Building 10, CRC, Bethesda, MD 20892, USA.
Diagnostics (Basel). 2025 May 21;15(10):1292. doi: 10.3390/diagnostics15101292.
: Glioblastoma (GBM) is a highly aggressive primary central nervous system tumor with a median survival of 14 months. MGMT (O6-methylguanine-DNA methyltransferase) promoter methylation status is a key biomarker as a prognostic indicator and a predictor of chemotherapy response in GBM. Patients with MGMT methylated disease progress later and survive longer (median survival rate 22 vs. 15 months, respectively) as compared to patients with MGMT unmethylated disease. Patients with GBM undergo an MRI of the brain prior to diagnosis and following surgical resection for radiation therapy planning and ongoing follow-up. There is currently no imaging biomarker for GBM. Studies have attempted to connect MGMT methylation status to MRI imaging appearance to determine if brain MRI can be leveraged to provide MGMT status information non-invasively and more expeditiously. : Artificial intelligence (AI) can identify MRI features that are not distinguishable to the human eye and can be linked to MGMT status. We employed the UPenn-GBM dataset patients for whom methylation status was available ( = 146), employing a novel radiomic method grounded in hybrid feature selection and weighting to predict MGMT methylation status. : The best MGMT classification and feature selection result obtained resulted in a mean accuracy rate value of 81.6% utilizing 101 selected features and five-fold cross-validation. : This compared favorably with similar studies in the literature. Validation with external datasets remains critical to enhance generalizability and propagate robust results while reducing bias. Future directions include multi-channel data integration with radiomic features and deep and ensemble learning methods to improve predictive performance.
胶质母细胞瘤（GBM）是一种极具侵袭性的原发性中枢神经系统肿瘤，中位生存期为14个月。O6-甲基鸟嘌呤-DNA甲基转移酶（MGMT）启动子甲基化状态是GBM中作为预后指标和化疗反应预测指标的关键生物标志物。与MGMT未甲基化疾病患者相比，MGMT甲基化疾病患者进展较晚且生存期更长（中位生存率分别为22个月和15个月）。GBM患者在诊断前以及手术切除后进行脑部MRI检查，以用于放疗计划和持续随访。目前GBM尚无影像学生物标志物。已有研究试图将MGMT甲基化状态与MRI影像表现联系起来，以确定脑部MRI是否可用于非侵入性且更快速地提供MGMT状态信息。人工智能（AI）可以识别肉眼无法区分的MRI特征，并将其与MGMT状态联系起来。我们使用了可获取甲基化状态的宾夕法尼亚大学GBM数据集患者（n = 146），采用一种基于混合特征选择和加权的新型放射组学方法来预测MGMT甲基化状态。所获得的最佳MGMT分类和特征选择结果利用101个选定特征和五折交叉验证得出的平均准确率值为81.6%。这与文献中的类似研究相比具有优势。使用外部数据集进行验证对于提高通用性、传播可靠结果同时减少偏差仍然至关重要。未来的方向包括将多通道数据与放射组学特征以及深度学习和集成学习方法进行整合，以提高预测性能。
