Craniopharyngioma

Fraioli, Mario Francesco; Contratti, F; Fraioli, Chiara

doi:10.3171/ped.2007.106.6.517a

Cited by 1 publication

(1 citation statement)

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“…Meningioma presents approximately 36% of all central nervous system tumors, with an occurrence rate of 7.61/100000 [5,6]. e patients with craniopharyngioma and meningioma in the sellar/parasellar area may suffer from similar symptoms, headache, visual change, and pituitary dysfunction, which are caused by the anatomical proximity of the tumor to the optic nerve/chiasma and hypothalamic-pituitary axes [7][8][9][10]. Magnetic resonance imaging (MRI) is the standard preoperative modality to detect craniopharyngioma or meningioma.…”

Section: Introductionmentioning

confidence: 99%

Radiomic Analysis of Craniopharyngioma and Meningioma in the Sellar/Parasellar Area with MR Images Features and Texture Features: A Feasible Study

Tian

Chen

Zhang

et al. 2020

Contrast Media & Molecular Imaging

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Purpose. To investigate the ability of qualitative Magnetic Resonance (MR) images features and quantitative Magnetic Resonance Imaging (MRI) texture features in the contrastive analysis between craniopharyngioma and meningioma. Method. A total number of 127 patients were included in this study (craniopharyngioma = 63; meningioma = 64). All the features analyzed in this study were acquired from preoperative MRI images. Qualitative MR images features were evaluated with chi-square tests or Fisher exact test, while MRI texture features were evaluated with the Mann–Whitney U test with the Benjamini–Hochberg method. Then binary logistic regression analysis for texture features was performed to evaluate their ability as independent predictors, and the diagnostic accuracy was calculated next for these texture features with high abilities as independent predictors using receiver operating characteristic (ROC) curves. Results. Four qualitative MR images features showed significant difference between craniopharyngioma and meningioma, but only cystic alteration could be considered as diagnostic independent predictors. Meanwhile, three quantitative parameters, histogram-based matrix- (HISTO-) Skewness, Grey-level co-occurrence matrix- (GLCM-) Contrast on contrast-enhanced images, and HISTO-Skewness on images of T2-weighted imaging (T2WI), showed promising abilities in the contrastive analysis. Besides, these texture features were found significantly to be relative to cystic alteration. Conclusion. MR images features and texture features were useful in the contrastive analysis of craniopharyngioma and meningioma. Furthermore, qualitative MR images features and MRI texture features could be related to each other.

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