Texture Analysis in Brain Tumor MR Imaging

Abstract

Texture analysis, as well as its broader category radiomics, describes a variety of techniques for image analysis that quantify the variation in surface intensity or patterns, including some that are imperceptible to the human visual system. Cerebral gliomas have been most rigorously studied in brain tumors using MR-based texture analysis (MRTA) to determine the correlation of various clinical measures with MRTA features. Promising results in cerebral gliomas have been shown in the previous MRTA studies in terms of the correlation with the World Health Organization grades, risk stratification in gliomas, and the differentiation of gliomas from other brain tumors. Multiple MRTA studies in gliomas have repeatedly shown high performance of entropy, a measure of the randomness in image intensity values, of either histogram-or gray-level co-occurrence matrix parameters. Similarly, researchers have applied MRTA to other brain tumors, including meningiomas and pediatric posterior fossa tumors. However, the value of MRTA in the clinical use remains undetermined, probably because previous studies have shown only limited reproducibility of the result in the real world. The low-to-modest generalizability may be attributed to variations in MRTA methods, sampling bias that originates from single-institution studies, and overfitting problems to a limited number of samples. To enhance the reliability and reproducibility of MRTA studies, researchers have realized the importance of standardizing methods in the field of radiomics. Another advancement is the recent development of a comprehensive assessment system to ensure the quality of a radiomics study. These two-way approaches will secure the validity of upcoming MRTA studies. The clinical use of texture analysis in brain MRI will be accelerated by these continuous efforts.

Application of DCE-MRI radiomics signature analysis in differentiating molecular subtypes of luminal and non-luminal breast cancer

“…In addition to the conventional first-order statistical features, we employed the ITK-SNAP software to mine high-order texture parameters that are richer inside the tumors. Texture analysis appraises the relationships between pixels that generate patterns of feature organization in an image, many of which go beyond visual perception ( 36 , 37 ). Five GLCM, five GLSZM and one GLDM high-order texture parameters (dependence variance) were included in the feature-screening results.…”

Meningioma typing model construction using radiomics-based multi-parameter magnetic resonance imaging

“…A thorough understanding of the characteristics and anatomy of the tumor before surgery is a prerequisite for complete resection. The consistency of meningioma is one of the most critical factors affecting the di culty of surgery [12], as they can be extremely soft tumors that are easily removed by aspiration or hard tumors that are di cult to resect completely [13]. Meningioma subtypes are usually diagnosed by histopathology and immunohistochemistry.…”

“…There are two texture features, T1C_auto_logarithm_glszm_Small Area Low Gray Level Emphasis and T1C_auto_original_glszm_Small Area Low Gray Level Emphasis, which are highly correlated with meningioma classi cation. GLSZM represents regions with the same interconnect adjacent pixels or voxels and can be used to quantify gray level regions in an image [12]. There are signi cant differences between broblastic meningiomas and non-broblastic meningiomas with different texture parameters.…”

Current State of Artificial Intelligence in Clinical Applications for Head and Neck MR Imaging

“…Prognosis prediction contributes to the optimization of treatment strategies, including the selection of initial therapy and the indication for adjuvant therapies, and the prediction is also considered useful to validate the treatment plan for each patient with head or neck cancer. Over the past few decades, imaging methods have been well developed and used together with advanced imaging techniques such as DWI and DCE, [99][100][101][102][103][104][105][106][107][108][109][110] or a high-dimensional analytical method such as a texture analysis or radiomics method 14,22,24,111,112 in various diseases as well as head and neck lesions. However, achieving accurate predictions is still challenging.…”