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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

We aim to construct more accurate prognostic model for KIRC patients by combining the clinical and genetic information and monitor the disease progression in dynamically updated manner. By obtaining cross-validated prognostic indices from clinical and genetic model, we combine the two sources information into the Super learner model, and then introduce the time-varying effect into the combined model using the landmark method for real-time dynamic prediction. The Super learner model has better prognostic performance since it can not only employ the preferable clinical prognostic model constructed by oneself or reported in the current literature, but also incorporate genome level information to strengthen effectiveness. Apart from this, four representative patients’ mortality curves are drawn in the dynamically updated manner based on the Super learner model. It is found that effectively reducing the two prognostic indices value through suitable treatments might achieve the purpose of controlling the mortality of patients. Combining clinical and genetic information in the Super learner model would enhance the prognostic performance and yield more accurate results for dynamic predictions. Doctors could give patients more personalized treatment with dynamically updated monitoring of disease status, as well as some candidate prognostic factors for future research.

Dynamic prognostic model for kidney renal clear cell carcinoma (KIRC) patients by combining clinical and genetic information