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

The aim of this study was to investigate the clinical factors affecting the survival prognosis of lung adenocarcinoma, and to establish a predictive model of survival prognosis of lung adenocarcinoma by artificial neural network.Download the cancer genome atlas (TCGA) database for lung adenocarcinoma research data, perform cox regression analysis and descriptive statistics on the obtained clinical data, draw the survival curve by Kaplan-Meier method, select the independent variables that are statistically significant for constructing the artificial neural networks (ANN) model, and establish artificial neural network model.The number of valid cases included in the study was 524, including 280 men and 244 women, with an age range of 33 to 88 years, mean age 66.87 years, and median progression-free survival (PFS) was 37.7 months. The median overall survival time (OS) was 41.1 months. Cox multivariate analysis showed that smoking history, tumor stage, and surgical margin resection status were independently associated with PFS, and tumor stage and surgical margin resection status were independently associated with OS. The accuracy of the established ANN model itself was predicted to be 65.8%. The accuracy of correctly predicting the prognosis of the predicted samples was 75.0%, and the area under the receiver operating characteristic curve was 0.712.The clinical prognostic factors of lung adenocarcinoma include: smoking history, tumor stage, and surgical margin resection status. The established ANN model can be used to predict the prognosis of lung adenocarcinoma.

Exploring the survival prognosis of lung adenocarcinoma based on the cancer genome atlas database using artificial neural network