Background/Objectives:Glioblastoma (GBM) is the most malignant subtype of glioma and shows the poorest prognosis with a median survival time of 15 months. The methylation status of the Methylguanine-DNA Methyltransferase (MGMT) was proven to be a crucial factor in selecting the most appropriate therapy. Currently, it is assessed through brain biopsy, which is a highly invasive and very expensive technique. For these reasons, in recent years, the possibility of inferring this information from multi-parametric Magnetic Resonance Imaging (mpMRI) has been widely explored. However, substantial differences in performance are reported in the literature.Methods:In this study, we developed several models based on either radiomic or deep learning approaches and a mixture of them using mpMRI for the MGMT status assessment using the public dataset UPENN-GBM, available on The Cancer Imaging Archive. Despite the tests performed using all MRI acquisitions and different methodological approaches, we did not obtain sufficiently reliable performance to direct the therapeutic path of patients. We thus investigated the impact of segmentation quality on MGMT status prediction since the UPENN-GBM dataset contains both automatic and manual refined segmentation masks.Results:We found that performance obtained through radiomic features computed on manually segmented tumors was significantly higher compared to that obtained using automatic segmentation, even when the differences between segmentation masks, measured in terms of Dice Similarity Coefficient (DSC), is not significantly different.Conclusion:This could be the reason why very different MGMT classification performance is typically reported and suggests the creation of a benchmark dataset, with high-quality segmentation masks.
背景/目的：胶质母细胞瘤（GBM）是胶质瘤中最恶性的亚型，预后极差，中位生存期仅为15个月。甲基鸟嘌呤-DNA甲基转移酶（MGMT）的甲基化状态已被证实是选择最适治疗方案的关键因素。目前，该状态主要通过脑组织活检进行评估，这是一种高度侵入性且费用高昂的技术。因此，近年来，从多参数磁共振成像（mpMRI）中推断此信息的可能性得到了广泛探索。然而，文献中报道的性能存在显著差异。 方法：在本研究中，我们基于公开数据集UPENN-GBM（来自癌症影像档案馆），开发了多种基于影像组学或深度学习方法的模型，以及两者的混合模型，用于通过mpMRI评估MGMT状态。尽管使用了所有MRI采集数据和不同的方法学策略进行测试，我们未能获得足够可靠的性能以指导患者的治疗路径。因此，我们研究了分割质量对MGMT状态预测的影响，因为UPENN-GBM数据集同时包含自动分割和人工精细化分割的掩模。 结果：我们发现，基于人工分割肿瘤计算的影像组学特征所获得的性能显著高于使用自动分割所获得的性能，即使两种分割掩模之间的差异（通过Dice相似系数衡量）并不显著。 结论：这可能是通常报道的MGMT分类性能差异巨大的原因，并提示需要建立一个具有高质量分割掩模的基准数据集。
肿瘤(癌症)患者之家