Magnetic Resonance Texture Analysis in Alzheimer's disease

Choi

et al. 2021

JAD

Background: Noninvasive identification of amyloid-β (Aβ) is important for better clinical management of mild cognitive impairment (MCI) patients. Objective: To investigate whether radiomics features in the hippocampus in MCI improve the prediction of cerebrospinal fluid (CSF) Aβ42 status when integrated with clinical profiles. Methods: A total of 407 MCI subjects from the Alzheimer’s Disease Neuroimaging Initiative were allocated to training (n = 324) and test (n = 83) sets. Radiomics features (n = 214) from the bilateral hippocampus were extracted from magnetic resonance imaging (MRI). A cut-off of <192 pg/mL was applied to define CSF Aβ42 status. After feature selection, random forest with subsampling methods were utilized to develop three models with which to predict CSF Aβ42: 1) a radiomics model; 2) a clinical model based on clinical profiles; and 3) a combined model based on radiomics and clinical profiles. The prediction performances thereof were validated in the test set. A prediction model using hippocampus volume was also developed and validated. Results: The best-performing radiomics model showed an area under the curve (AUC) of 0.674 in the test set. The best-performing clinical model showed an AUC of 0.758 in the test set. The best-performing combined model showed an AUC of 0.823 in the test set. The hippocampal volume model showed a lower performance, with an AUC of 0.543 in the test set. Conclusion: Radiomics models from MRI can help predict CSF Aβ42 status in MCI patients and potentially triage the patients for invasive and costly Aβ tests.

Section: Discussionmentioning

confidence: 99%

Predicting Amyloid Pathology in Mild Cognitive Impairment Using Radiomics Analysis of Magnetic Resonance Imaging

Choi

et al. 2021

JAD

“…In a review of MRI texture analyses with machine learning techniques [17], many studies performed classification and prediction of AD. Even though these studies included a greater number of subjects, the vast majority of them focused specifically on the hippocampal region and used only one machine learning technique.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, signal-and texture-related features extracted from MRI scans and selected machine learning techniques have emerged as possible novel markers of AD [17]. In addition, studies of the progression of AD showed that highly asymmetrical contralateral hippocampi and amygdala may indicate an early and accelerated deterioration [18].…”

Section: Introductionmentioning

confidence: 99%

Signal and Texture Features from T2 Maps for the Prediction of Mild Cognitive Impairment to Alzheimer’s Disease Progression

et al. 2021

Early detection of Alzheimer’s disease (AD) is crucial to preserve cognitive functions and provide the opportunity for patients to enter clinical trials. In recent years, some studies have reported that features related to the signal and texture of MRI images can be an effective biomarker of AD. To test these claims, a study was conducted using T2 maps, a sequence not previously studied, of 40 patients with mild cognitive impairment (MCI) from the Alzheimer’s Disease Neuroimaging Initiative database, who either progressed to AD (18) or remained stable (22). From these maps, the mean value and absolute difference of 37 signal and texture imaging features for 40 contralateral pairs of regions were measured. We used seven machine learning methods to analyze whether, by adding these imaging features to the neuropsychological studies currently used for diagnosis, we could more accurately identify patients who will progress to AD. The predictive models improved with the addition of signal and texture features. Additionally, features related to the signal and texture of the images were much more relevant than volumetric ones. Our results suggest that contralateral signal and texture features should be further investigated as potential biomarkers for the prediction of AD.

“…In recent years, texture-based analysis, which models local patterns of relative intensity variations rather than the absolute value of intensity, has proven a useful neuroimaging tool. Texture-based analysis can detect group-level differences not evident at the voxel scale (Cai et al, 2020). Unlike a voxel-wise approach, the texture-based analysis aims to describe variations in the patterns of spatial intensity along with a specific orientation.…”

Section: Introductionmentioning

confidence: 99%

Anatomical texture patterns identify cerebellar distinctions between essential tremor and Parkinson's disease

Hett

Lyu

Trujillo

et al. 2021

Human Brain Mapping

Voxel-based morphometry is an established technique to study focal structural brain differences in neurologic disease. More recently, texture-based analysis methods have enabled a pattern-based assessment of group differences, at the patch level rather than at the voxel level, allowing a more sensitive localization of structural differences between patient populations. In this study, we propose a texture-based approach to identify structural differences between the cerebellum of patients with Parkinson's disease (n = 280) and essential tremor (n = 109). We analyzed anatomical differences of the cerebellum among patients using two features: T1-weighted MRI intensity, and a texture-based similarity feature. Our results show anatomical differ-