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 Plus annual

Presents the access of premium content as premium feature

Premium Content

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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Accurate characterization of glioma is crucial for clinical decision making.A delineation of the tumor is also desirable in the initial decision stages butis a time-consuming task. Leveraging the latest GPU capabilities, we developeda single multi-task convolutional neural network that uses the full 3D,structural, pre-operative MRI scans to can predict the IDH mutation status, the1p/19q co-deletion status, and the grade of a tumor, while simultaneouslysegmenting the tumor. We trained our method using the largest, most diversepatient cohort to date containing 1508 glioma patients from 16 institutes. Wetested our method on an independent dataset of 240 patients from 13 differentinstitutes, and achieved an IDH-AUC of 0.90, 1p/19q-AUC of 0.85, grade-AUC of0.81, and a mean whole tumor DICE score of 0.84. Thus, our methodnon-invasively predicts multiple, clinically relevant parameters andgeneralizes well to the broader clinical population.

WHO 2016 subtyping and automated segmentation of glioma using multi-task deep learning