Multiparametric MRI in Era of Artificial Intelligence for Bladder Cancer Therapies.

This review focuses on the principles, applications, and performance of mpMRI for bladder imaging. Quantitative imaging biomarkers (QIBs) derived from mpMRI are increasingly used in oncological applications, including tumor staging, prognosis, and assessment of treatment response. To standardize mpMRI acquisition and interpretation, an expert panel developed the Vesical Imaging-Reporting and Data System (VI-RADS). Many studies confirm the standardization and high degree of inter-reader agreement to discriminate muscle invasiveness in bladder cancer, supporting VI-RADS implementation in routine clinical practice. The standard MRI sequences for VI-RADS scoring are anatomical imaging, including T2w images, and physiological imaging with diffusion-weighted MRI (DW-MRI) and dynamic contrast-enhanced MRI (DCE-MRI). Physiological QIBs derived from analysis of DW- and DCE-MRI data and radiomic image features extracted from mpMRI images play an important role in bladder cancer. The current development of AI tools for analyzing mpMRI data and their potential impact on bladder imaging are surveyed. AI architectures are often implemented based on convolutional neural networks (CNNs), focusing on narrow/specific tasks. The application of AI can substantially impact bladder imaging clinical workflows; for example, manual tumor segmentation, which demands high time commitment and has inter-reader variability, can be replaced by an autosegmentation tool. The use of mpMRI and AI is projected to drive the field toward the personalized management of bladder cancer patients.

Multi-layer analysis of quantitative 7 T magnetic resonance imaging in the cortex of multiple sclerosis patients reveals pathology associated with disability.

BACKGROUND: Cortical demyelination is a relevant aspect of tissue damage in multiple sclerosis (MS). Microstructural changes may affect each layer in the cortex differently. OBJECTIVES: To evaluate the sensitivity of quantitative magnetic resonance imaging (qMRI) measurements on cortical layers as clinically accessible biomarkers of grey matter (GM) pathology. METHODS: Forty-five participants with MS underwent 7 T magnetic resonance imaging (MRI) of the brain. Magnetization prepared two rapid acquisition gradient echoes (MP2RAGE) was processed for T1-weighted images and a T1 map. Multi-echo gradient echo images were processed for quantitative susceptibility and R2* maps. Cortical GM volumes were segmented into four cortical layers, and relaxometry metrics were calculated within and between these layers. RESULTS: Significant correlations were found for disability scales and multi-layer metrics, for example, Expanded Disability Status Scale (EDSS) and peak height (PH) in the subpial (T1: ρ = -0.372, p < 0.050) and inner (R2*: ρ = -0.359, p < 0.050) cortical layers. Multivariate regression showed interdependency between atrophy and cortical metrics in some instances, but an independent relationship between cortical metrics and disability in others. CONCLUSION: Cortical layer 7 T qMRI analyses reveal layer-specific relationships with disability in MS and allow emergence of clinically relevant associations that are hidden when analysing the full cortex.

No association between cortical lesions and leptomeningeal enhancement on 7-Tesla MRI in multiple sclerosis.

BACKGROUND: Autopsy data suggest a causative link between meningeal inflammation and cortical lesions (CLs) in multiple sclerosis (MS). OBJECTIVE: To use leptomeningeal enhancement (LME) and CLs on 7-Tesla (7T) magnetic resonance imaging (MRI) to investigate associations between meningeal inflammation and cortical pathology. METHODS: Forty-one participants with MS underwent 7T MRI of the brain. CLs and foci of LME were quantified. RESULTS: All MS participants had CLs; 27 (65.8%) had >1 focus of LME. Except for hippocampal CL count (ρ = 0.32 with spread/fill-sulcal pattern LME, p = 0.042), no significant correlations were seen between LME and CLs. Mean cortical thickness correlated with the number of LME foci (ρ = -0.43, p = 0.005). Participants with relapsing-remitting multiple sclerosis (RRMS) showed no correlation with neocortical CLs, but significant correlations were seen between LME and hippocampal lesion count (ρ = 0.39, p = 0.030), normalized cortical gray matter (GM) volume (ρ = -0.49, p = 0.005), and mean cortical thickness (ρ = -0.59, p < 0.001). CONCLUSION: This study supports a relationship between LME and cortical GM atrophy but does not support an association of LME and neocortical CLs. This may indicate that meningeal inflammation is involved with neurodegenerative inflammatory processes, rather than focal lesion development.