ADC texture—An imaging biomarker for high‐grade glioma?

Abstract

By combining PCA and texture analysis, ADC texture characteristics were identified, which seems to hold pretreatment prognostic information, independent of known prognostic factors such as age, stage, and surgical procedure. These findings encourage further studies with a larger patient cohort.

Evaluation of pseudoprogression rates and tumor progression patterns in a phase III trial of bevacizumab plus radiotherapy/temozolomide for newly diagnosed glioblastoma

“…Mandating confirmatory MRIs for suspected pseudoprogression in clinical trials would be effective, but the development of tools to distinguish pseudoprogression without having to wait for a follow-up scan would be preferable. Several techniques are under investigation, including T1-w subtraction maps, diffusion-weighted and perfusion-weighted MRI, 26 the apparent diffusion coefficient, 27 cerebral blood volume measurement by MRI perfusion,…”

Haralick texture features from apparent diffusion coefficient (ADC) MRI images depend on imaging and pre-processing parameters

“…In medical image analysis, texture analysis was adopted for analysis of ultrasound images of the liver 3 and heart 4 in the late 1970s and early 1980s, and gained popularity in the 1990s and 2000s for many medical imaging application, including oncology. Texture analysis enables description of tissue heterogeneity, a property believed to influence the outcome of cancer treatment 5 , which has led to applications in treatment response evaluation 6, 7, 5, 8 . Haralick texture features 1, 9, 10 calculated from a gray level co-occurrence matrix (GLCM) is a common method to represent image texture, as it is simple to implement and results in a set of interpretable texture descriptors 1, 11 Although a large and increasing number of studies uses Haralick’s features to analyze texture in magnetic resonance images (MRI) and images from other modalities 9, 12–15 there is no standardized way of performing these analyzes 13 .…”

Texture analysis- and support vector machine-assisted diffusional kurtosis imaging may allow in vivo gliomas grading and IDH-mutation status prediction: a preliminary study

“…Therefore, first-order statistical methods do not convey spatial information, whereas the second and higher order statistical methods maintain spatial information and may reflect better the lesion heterogeneity. Texture analysis (TA) is a higher-order analysis and constitutes a mathematical method, which has shown preliminary potential to gain detailed insight into tissue composition 16–18 . Ideally, machine learning could be used to assist the diagnostic by enhancing its accuracy.…”