English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Background and Purpose: Biopsy is the main determinants of glioma clinicalmanagement, but require invasive sampling that fail to detect relevant featuresbecause of tumor heterogeneity. The purpose of this study was to evaluate theaccuracy of a voxel-wise, multiparametric MRI radiomic method to predictfeatures and develop a minimally invasive method to objectively assessneoplasms.  Methods: Multiparametric MRI were registered to T1-weighted gadoliniumcontrast-enhanced data using a 12 degree-of-freedom affine model. Theretrospectively collected MRI data included T1-weighted, T1-weighted gadoliniumcontrast-enhanced, T2-weighted, fluid attenuated inversion recovery, andmulti-b-value diffusion-weighted acquired at 1.5T or 3.0T. Clinical expertsprovided voxel-wise annotations for five disease states on a subset of patientsto establish a training feature vector of 611,930 observations. Then, ak-nearest-neighbor (k-NN) classifier was trained using a 25% hold-out design.The trained k-NN model was applied to 13,018,171 observations from seventeenhistologically confirmed glioma patients. Linear regression tested overallsurvival (OS) relationship to predicted disease compositions (PDC) anddiagnostic age (alpha = 0.05). Canonical discriminant analysis tested if PDCand diagnostic age could differentiate clinical, genetic, and microscopicfactors (alpha = 0.05).  Results: The model predicted voxel annotation class with a Dice similaritycoefficient of 94.34% +/- 2.98. Linear combinations of PDCs and diagnostic agepredicted OS (p = 0.008), grade (p = 0.014), and endothelia proliferation (p =0.003); but fell short predicting gene mutations for TP53BP1 and IDH1.  Conclusions: This voxel-wise, multi-parametric MRI radiomic strategy holdspotential as a non-invasive decision-making aid for clinicians managingpatients with glioma.

Predicted disease compositions of human gliomas estimated from  multiparametric MRI can predict endothelial proliferation, tumor grade, and  overall survival