Non-small cell lung cancer: Artificial Intelligence enables the preliminary identification of complementary survival signatures that involve previous therapies
A new method has been developed by the Centre Léon Bérard in Lyon (France) to improve the prognosis of on-small cell lung cancer (NSCLC), one of the most common cancers in the world. The Centre Léon Bérard has developed a HOT score which, based on gene expression, predicts patient survival. This process was carried out with the help of the KEM® (Knowledge Extraction and Management) explanatory artificial intelligence platform, which systematically extracts association rules between variables in a database and refines the HOT score.
Using this new tool, the analysis identified 4 genes that predict survival. The results have yet to be confirmed by the Cancer Research Institute’s iAtlas, a database containing gene expression for more than 1,100 cancer patients across five different tissue types. Specific adjustments may still be required, but this is a strong advance made with the help of the KEM® explanatory artificial intelligence platform.