Ependymoma and pilocytic astrocytoma: Differentiation using radiomics approach based on machine learning |
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| Affiliation: | 1. School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China;2. Industrial Technology Research Institute, Zhengzhou University, Zhengzhou 450001, China;3. Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou University, Zhengzhou 450001, China;4. Magnetic Resonance Department, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China;1. Neuroradiology Department, Hospital de Braga, Sete Fontes – São Victor, 4710-243 Braga, Portugal;2. Neurology Department, Hospital de Braga, Sete Fontes – São Victor, 4710-243 Braga, Portugal;1. Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100041, China;2. Department of Neurology, Peking University Shougang Hospital, Beijing 100144, China;3. China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China;1. Department of Neurosurgery, The Canberra Hospital, Garran, ACT, Australia;2. Department of Neurosurgery, The Royal Melbourne Hospital, Parkville, Victoria, Australia;1. Department of Neurological Surgery, University of Miami Miller School of Medicine, Miami, FL, USA;2. Department of Neurological Surgery, San Francisco University of Quito School of Medicine, Quito, Ecuador;3. Department of Pathology, University of Miami Miller School of Medicine, Miami, FL, USA |
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| 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. |
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| Keywords: | Ependymoma Pilocytic Astrocytoma MRI Radiomics Machine Learning |
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