Structural gray matter alterations in glioblastoma and high-grade glioma-A potential biomarker of survival.
Structural gray matter alterations in glioblastoma and high-grade glioma-A potential biomarker of survival.
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DOI：
10.1093/noajnl/vdad034
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发表时间：
2023-01
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胶质母细胞瘤(GBM)和高级别胶质瘤(HGG，世界卫生组织IV级胶质瘤)患者预后较差。因此，对可获得和非侵入性获得的预测患者总存活率的生物标志物的临床需求尚未得到满足。这项研究评估了从肿瘤侵袭部位(即对侧大脑半球)分离出来的大脑的形态变化。具体地说，我们研究了GBM/HGG患者皮质厚度(CT)广泛改变的预后价值。我们使用Freesurfer，应用高分辨率T1加权MRI检查CT，在通过手术和化疗进行标准治疗之前对患者(GBM/HGG，N=162，平均年龄61.3岁)和127名健康对照(HC，平均年龄61.9岁)进行评估。然后，我们比较了患者的CT和HC，并研究了患者CT的相关变化作为总存活率的潜在生物标志物。与HC患者相比，患者在肿瘤诊断时对侧大脑半球的灰质较薄。患者在顶叶、颞叶和枕叶有明显的皮质变薄。14个皮质区CT显示降低，5个皮质区CT增厚。值得注意的是，对侧大脑半球、不同脑叶和病灶的CT可以预测总的存活率。机器学习分类算法显示，CT可以区分短期和长期存活的患者，准确率为83.3%。这些发现确认了位于原发肿瘤对侧半球的皮质以前未被注意到的结构变化。观察到的CT改变可能对预后有价值，这可能会影响个别患者的护理和治疗计划。
Patients with glioblastoma (GBM) and high-grade glioma (HGG, World Health Organization [WHO] grade IV glioma) have a poor prognosis. Consequently, there is an unmet clinical need for accessible and noninvasively acquired predictive biomarkers of overall survival in patients. This study evaluated morphological changes in the brain separated from the tumor invasion site (ie, contralateral hemisphere). Specifically, we examined the prognostic value of widespread alterations of cortical thickness (CT) in GBM/HGG patients. We used FreeSurfer, applied with high-resolution T1-weighted MRI, to examine CT, evaluated prior to standard treatment with surgery and chemoradiation in patients (GBM/HGG, N = 162, mean age 61.3 years) and 127 healthy controls (HC; 61.9 years mean age). We then compared CT in patients to HC and studied patients’ associated changes in CT as a potential biomarker of overall survival. Compared to HC cases, patients had thinner gray matter in the contralesional hemisphere at the time of tumor diagnosis. patients had significant cortical thinning in parietal, temporal, and occipital lobes. Fourteen cortical parcels showed reduced CT, whereas in 5, it was thicker in patients’ cases. Notably, CT in the contralesional hemisphere, various lobes, and parcels was predictive of overall survival. A machine learning classification algorithm showed that CT could differentiate short- and long-term survival patients with an accuracy of 83.3%. These findings identify previously unnoticed structural changes in the cortex located in the hemisphere contralateral to the primary tumor mass. Observed changes in CT may have prognostic value, which could influence care and treatment planning for individual patients.