Astroblastoma: a rare case report

Palled, Siddanna R.; Thimmaya, Naveen; Jagadheesan, Sugashwaran; Khaleel, Ibrahim

doi:10.1017/s1460396915000485

Cited by 1 publication

(1 citation statement)

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“…We excluded 36 studies based on the inclusion/exclusion criteria. We identified 65 studies, including 71 patients with astroblastomas, meeting the requirements of the systematic review (Figure 2), 1–6,10,12,16–72 ranging from 2002 to 2021. In addition, we included the unpublished reports of eight patients with astroblastomas from our hospital (Table 1), resulting in a final study cohort of 79 patients.…”

Section: Resultsmentioning

confidence: 99%

Neuroimaging of astroblastomas: A case series and systematic review

Kurokawa

Baba

Kurokawa

et al. 2021

Journal of Neuroimaging

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Background and Purpose 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. Methods 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. Results 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. Conclusions 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.

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Section: Resultsmentioning

confidence: 99%