Detection and Grading of Gliomas Using a Novel Two-Phase Machine Learning Method Based on MRI Images

Abstract

The early detection and grading of gliomas is important for treatment decision and assessment of prognosis. Over the last decade numerous automated computer analysis tools have been proposed, which can potentially lead to more reliable and reproducible brain tumor diagnostic procedures. In this paper, we used the gradient-based features extracted from structural magnetic resonance imaging (sMRI) images to depict the subtle changes within brains of patients with gliomas. Based on the gradient features, we proposed a novel two-phase classification framework for detection and grading of gliomas. In the first phase, the probability of each local feature being related to different types (e.g., diseased or healthy for detection, benign or malignant for grading) was calculated. Then the high-level feature representing the whole MRI image was generated by concatenating the membership probability of each local feature. In the second phase, the supervised classification algorithm was used to train a classifier based on the high-level features and patient labels of the training subjects. We applied this framework on the brain imaging data collected from Zhongnan Hospital of Wuhan University for glioma detection, and the public TCIA datasets including glioblastomas (WHO IV) and low-grade gliomas (WHO II and III) data for glioma grading. The experimental results showed that the gradient-based classification framework could be a promising tool for automatic diagnosis of brain tumors.

Differentiating Glioblastoma Multiforme from Brain Metastases Using Multidimensional Radiomics Features Derived from MRI and Multiple Machine Learning Models

“…The voxel size resampling (1 * 1 * 1) and bin width (64) were applied to the images. Pyradiomics was used to extract radiomic features from each ROI based on its three-dimensional region of interest (3D ROI) [29][30][31][32][33][34]. From each sequence, 107 features were extracted (Table 1), and these features were grouped into three categories: first-order statistics (n = 18), shape-based features (n = 14), and textural features.…”

Application of Artificial Intelligence in Diagnosis of Craniopharyngioma

“…Analogously, Ahammed et al ( 42) also performed skull stripping by using BrainSuite software, which adopted a brain surface extraction algorithm (BSE) to operate the skull stripping. Aiming to solve the non-standardization of MRI intensity, Chen et al (43) (2) ROI segmentation: Image ROI segmentation has an important impact on the final result of medical classification. ROI segmentation can remove surrounding tissues of the lesion and irrelevant interference information in the background, and identify the lesion area by describing the density, shape and other characteristics of the ROI.…”

“…Analogously, Ahammed et al ( 42 ) also performed skull stripping by using BrainSuite software, which adopted a brain surface extraction algorithm (BSE) to operate the skull stripping. Aiming to solve the non-standardization of MRI intensity, Chen et al ( 43 ) used bias correction and Z-score normalization which were implemented in Statistical Parametric Mapping (SPM12) for pre-processing. Siakallis et al ( 44 ) performed log-transformation, normalization, bias field correction and intensity matching on skull stripping images.…”

Texture Feature Analysis of MRI-ADC Images to Differentiate Glioma Grades Using Machine Learning Techniques.

“…Therefore, the supervised learning method can be identified as one of the most common ML paradigms that use labeled input data to train ML algorithms [22], [32,33,34]. When the data is fed into the algorithm, it identifies the hidden characteristics, patterns, and correlations for each class and makes ML models using such information.…”