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Distinguishing brain abscess from necrotic glioblastoma using MRI-based intranodular radiomic features and peritumoral edema/tumor volume ratio

Authors :
Dongdong Xiao
Jiajing Wang
Xuan Wang
Peng Fu
Hongyang Zhao
Pengfei Yan
Xiaobing Jiang
Source :
Journal of Integrative Neuroscience, Vol 20, Iss 3, Pp 623-634 (2021)
Publication Year :
2021
Publisher :
IMR Press, 2021.

Abstract

A correct preoperative diagnosis is essential for the treatment and prognosis of necrotic glioblastoma and brain abscess, but the differentiation between them remains challenging. We constructed a diagnostic prediction model with good performance and enhanced clinical applicability based on data from 86 patients with necrotic glioblastoma and 32 patients with brain abscess that were diagnosed between January 2012 and January 2020. The diagnostic values of three regions of interest based on contrast-enhanced T1 weighted images (including whole tumor, brain-tumor interface, and an amalgamation of both regions) were compared using Logistics Regression and Random Forest. Feature reduction based on the optimal regions of interest was performed using principal component analysis with varimax rotation. The performance of the classifiers was assessed by receiver operator curves. Finally, clinical predictors were utilized to detect the diagnostic power. The mean area under curve (AUC) values of the whole tumor model was significantly higher than other two models obtained from Brain-Tumor Interface (BTI) and combine regions both in training (AUC mean = 0.850) and test/validation set (AUC mean = 0.896) calculated by Logistics Regression and in the testing set (AUC mean = 0.876) calculated by Random Forest. Among these three diagnostic prediction models, the combined model provided superior discrimination performance and yielded an AUC of 0.993, 0.907, and 0.974 in training, testing, and combined datasets, respectively. Compared with the brain-tumor interface and the combined regions, features obtained from the whole tumor showed the best differential value. The radiomic features combined with the peritumoral edema/tumor volume ratio provided the prediction model with the greatest diagnostic performance.

Details

Language :
English
ISSN :
1757448X and 34320903
Volume :
20
Issue :
3
Database :
Directory of Open Access Journals
Journal :
Journal of Integrative Neuroscience
Publication Type :
Academic Journal
Accession number :
edsdoj.bf34320903db43159fb666ca84d3482c
Document Type :
article
Full Text :
https://doi.org/10.31083/j.jin2003066