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

Recent radiomic studies have witnessed promising performance of deep learningtechniques in learning radiomic features and fusing multimodal imaging data.Most existing deep learning based radiomic studies build predictive models in asetting of pattern classification, not appropriate for survival analysisstudies where some data samples have incomplete observations. To improveexisting survival analysis techniques whose performance is hinged on imagingfeatures, we propose a deep learning method to build survival regression modelsby optimizing imaging features with deep convolutional neural networks (CNNs)in a proportional hazards model. To make the CNNs applicable to tumors withvaried sizes, a spatial pyramid pooling strategy is adopted. Our method hasbeen validated based on a simulated imaging dataset and a FDG-PET/CT dataset ofrectal cancer patients treated for locally advanced rectal cancer. Comparedwith survival prediction models built upon hand-crafted radiomic features usingCox proportional hazards model and random survival forests, our method achievedcompetitive prediction performance.

Deep Convolutional Neural Networks for Imaging Data Based Survival Analysis of Rectal Cancer