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

—Descriptive features of nuclei positions from tumor tissue samples are studied to construct mathematical models for tumor growth. Extracted from kidney cancer patients, tumorous tissue pieces are implanted in the flanks of mice to measure the course of tumor mass, which are then sampled on glass slides. H&E slides are digitized under light microscope and analyzed to identify the structure of nuclei positions. Using k-means clustering method, the nuclei locations of each H&E slide are evaluated. The cluster features are used as inputs to our artificial intelligence based personalized tumor growth parameter computation method, called PReP-C. The exponential linear tumor growth model parameters and the corresponding growth curves computed by PReP-C are compared to the preclinical tumor volume measurements. The correlation between the computed results and the measurements from 14 H&E pathology slides is encouraging to build personalized mathematical models for tumor growth.

Modeling Tumor Growth for Kidney Cancer Based on Nuclei Clusters of Pathology Slides