Division of Neuroradiology, Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA.
Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan, USA.
J Neuroimaging. 2022 Mar;32(2):201-212. doi: 10.1111/jon.12948. Epub 2021 Nov 23.
Astroblastoma is a rare type of glial tumor, histologically classified into two types with different prognoses: high and low grade. We aimed to investigate the CT and MRI findings of astroblastomas by collecting studies with analyzable neuroimaging data and extracting the imaging features useful for tumor grading.
We searched for reports of pathologically proven astroblastomas with analyzable neuroimaging data using PubMed, Scopus, and Embase. Sixty-five studies with 71 patients with astroblastomas met the criteria for a systematic review. We added eight patients from our hospital, resulting in a final study cohort of 79 patients. The proportion of high-grade tumors was compared in groups based on the morphology (typical and atypical) using Fisher's exact test.
High- and low-grade tumors were 35/71 (49.3%) and 36/71 (50.7%), respectively. There was a significant difference in the proportion of high-grade tumors based on the tumor morphology (typical morphology: high-grade = 33/58 [56.9%] vs. atypical morphology, 2/13 [15.4%], p = .012). The reviews of neuroimaging findings were performed using the images included in each article. The articles had missing data due to the heterogeneity of the collected studies.
Detailed neuroimaging features were clarified, including tumor location, margin status, morphology, CT attenuation, MRI signal intensity, and contrast enhancement pattern. The classification of tumor morphology may help predict the tumor's histological grade, contributing to clinical care and future oncologic research.
星形母细胞瘤是一种罕见的神经胶质瘤，组织学上分为两种具有不同预后的类型：高级别和低级别。我们旨在通过收集具有可分析神经影像学数据的研究，并提取有助于肿瘤分级的影像学特征，来研究星形母细胞瘤的 CT 和 MRI 表现。
我们使用 PubMed、Scopus 和 Embase 搜索了具有可分析神经影像学数据的经病理证实的星形母细胞瘤报告。65 项研究共 71 例星形母细胞瘤患者符合系统评价标准。我们从我们的医院增加了 8 例患者，最终研究队列共有 79 例患者。使用 Fisher 确切检验比较基于形态（典型和非典型）的分组中高级别肿瘤的比例。
高级别和低级别肿瘤分别为 35/71（49.3%）和 36/71（50.7%）。肿瘤形态的高级别肿瘤比例存在显著差异（典型形态：高级别=33/58[56.9%]与非典型形态，2/13[15.4%]，p=0.012）。神经影像学发现的综述使用了各文章中包含的图像进行。由于收集研究的异质性，文章存在数据缺失。
详细的神经影像学特征得到了明确，包括肿瘤位置、边缘状态、形态、CT 衰减、MRI 信号强度和对比增强模式。肿瘤形态的分类可能有助于预测肿瘤的组织学等级，有助于临床护理和未来的肿瘤学研究。
