Ependymoma and pilocytic astrocytoma: Differentiation using radiomics approach based on machine learning. (August 2020)

• Record Type: Journal Article
• Authors: Li, Mengmeng; Wang, Haofeng; Shang, Zhigang; Yang, Zhongliang; Zhang, Yong; Wan, Hong
• Is Part Of: Journal of clinical neuroscience. Volume 78(2020)
• Page Start: 175
• Page End: 180
• Publication Date: 2020-08
• Subjects: Ependymoma -- Pilocytic Astrocytoma -- MRI -- Radiomics -- Machine Learning; Brain -- Surgery -- Periodicals; Neurosciences -- Periodicals; Nervous system -- Surgery -- Periodicals; Brain -- surgery -- Periodicals; Neurosurgical Procedures -- Periodicals; Neurosciences -- Periodicals; Electronic journals; 616.8
• DOI: 10.1016/j.jocn.2020.04.080
• Languages: English
• ISSNs: 0967-5868

Abstract

Highlights: A radiomics approach is applied for pediatric posterior fossa tumor differentiation. 300 multimodal features are extracted to describe the statistics of the MRIs. Machine learning methods are combined for effective assisted clinical diagnosis. Abstract: Mandatory accurate and specific diagnosis demands have brought about increased challenges for radiologists in pediatric posterior fossa tumor prediction and prognosis. With the development of high-performance computing and machine learning technologies, radiomics provides increasing opportunities for clinical decision-making. Several studies have applied radiomics as a decision support tool in intracranial tumors differentiation. Here we seek to achieve preoperative differentiation between ependymoma (EP) and pilocytic astrocytoma (PA) using radiomics analysis method based on machine learning. A total of 135 Magnetic Resonance Imaging (MRI) slices are divided into training sets and validation sets. Three kinds of radiomics features, including Gabor transform, texture and wavelet transform based ones are used to obtain 300 multimodal features. Kruskal–Wallis test score (KWT) and support vector machines (SVM) are applied for feature selection and tumor differentiation. The performance is investigated via accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) analysis. Results show that the accuracy, sensitivity, specificity, and AUC of the selected feature set are 0.8775, 0.9292, 0.8000, and 0.8646 respectively, having no significant differences compared with the overall feature set. For different types of features, texture features yield the best differentiation performance and the significance analysis results are consistent with this. Our study demonstrates texture features perform better than the other features. The radiomics approach based on machine learning is efficient for pediatric posterior fossa tumors differentiation and could enhance the application of radiomics methods for assisted clinical diagnosis.