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

With the advanced imaging technology, digital pathology imaging of tumortissue slides is becoming a routine clinical procedure for cancer diagnosis.This process produces massive imaging data that capture histological details inhigh resolution. Recent developments in deep-learning methods have enabled usto automatically detect and characterize the tumor regions in pathology imagesat large scale. From each identified tumor region, we extracted 30 well-defineddescriptors that quantify its shape, geometry, and topology. We demonstratedhow those descriptor features were associated with patient survival outcome inlung adenocarcinoma patients from the National Lung Screening Trial (n=143).Besides, a descriptor-based prognostic model was developed and validated in anindependent patient cohort from The Cancer Genome Atlas Program program(n=318). This study proposes new insights into the relationship between tumorshape, geometrical, and topological features and patient prognosis. We providesoftware in the form of R code on GitHub:https://github.com/estfernandez/Slide_Image_Segmentation_and_Extraction.

Discovering Clinically Meaningful Shape Features for the Analysis of Tumor Pathology Images