# Intelligent Glioma Grading Based on Deep Transfer Learning of MRI Radomic Features
## 1 Introduction  
Diffuse gliomas, the most common primary central nervous system (CNS) neoplasm, are formed of tumor cells that display differentiation of glial cells. In the World Health Organization (WHO) classification of tumors of the CNS [1; 2], diffuse gliomas are graded according to their malignantdegree into WHO grades 2 to 4. Patients with diffuse gliomas of lower grades (grades 2 and 3) have more favorable prognoses [2; 3]. On the contrary, glioblastoma multiforme (GBM) is the most aggressive tumor type (WHO grade 4) with dismal prognoses despite advances in various aspects of its clinical management [4]. Since therapeutic strategies for the various grades are not identical [5], distinguishing the different grades of diffuse gliomas is a critical issue in clinical settings. Determining the tumor grade relies on different pathological features including mitotic activity, cytological atypia, neoangiogenesis, and tumor necrosis. However, since the definitions are semiquantitative and subjective [6; 7], histopathological analyses sometimes result in ambiguity in glioma grading. Moreover, previous reports revealed that the heterogeneous expressions of cellular features may result in misgrading in up to one-third of cases with unguided surgical tissue sampling [7; 8; 9; 10; 11]. With the development of medical imaging technologies, magnetic resonance (MR) imaging (MRI) is now the most commonly used modality to evaluate the malignancy of brain tumors [12; 13]. Growing evidence has revealed the feasibility of using MRI features to probe underlying pathological subtypes, suggesting their potential application in differentiating tumor molecular profiles based on imaging traits [14]. In addition to conventional sequences, several physiological MR sequences, including diffusion-weighted imaging (DWI), perfusion-weighted imaging (PWI), and MR spectroscopy (MRS), have also been applied to find physiologically meaningful signals within the tumor, thus helping to evaluate heterogeneous patterns of cell compositions within tumor tissues and noninvasively differentiate gliomas of different degrees of malignancy [15; 16; 17; 18; 19]. A previous study proposed that MRI scans are highly specific for diagnosing brain stem gliomas and can even replace risky biopsies before radiotherapy in most cases [20]. To improve the clinical care of CNS gliomas, getting the most out of MRI information is very important. Various computer-aided diagnosis (CAD) systems have been used to extract relevant diagnostic image features from X-ray radiography, ultrasonography, and MRI to evaluate tumor types, grades, and subsequent treatments [21; 22; 23; 24]. Certain image features, including the intensity, morphology, and textural features, are handcrafted according to clinical experience. When designing handcrafted features, such as shape features, which are described by the experimented radiologists from clinical experience as being malignant tumors that are aggressive and have irregular shapes, implementation not only depends on the interpretive skills of physicians and computer scientists, but also is limited by the experts' available knowledge. Regarding image interpretation, deep learning, as a recent artificial intelligence technique, proposes using automatic convolutions to extract enormous numbers of edge features and object features to recognize the underlying characteristic patterns in images [25; 26; 27]. Human interventions with domain-specific knowledge are thus minimized by convolution layers using hierarchical feature representations. Thus, deep convolutional neural networks (DCNNs) have been successfully applied to object recognition in natural images after being trained on a large amount of training data [28]. However, the use of DCNNs in clinical decision making may be restricted due to limited private medical data. Nevertheless, recent studies have used DCNN or machine learning techniques to classify gliomas. [PERSON] et al. used DCNN to classify 113 gliomas and achieved good accuracy [29].