Predicting the Risk of Metastatic Dissemination in Non-small Cell Lung Cancer Using Clinical and Genetic Data.

The major cause of cancer patient death is the development of distant tumors (metastases) that leads to a disease that is hard to treat. Therefore, the emergence of metastasis is a key time-point in the management of cancer leading to a decision about more intensive treatment. One of the most metastatic cancer types is lung cancer, in which the most common subtype is non-small cell lung cancer (NSCLC). We selected a set of common genetic variants in 27 genes and identified them in DNA from the peripheral blood of 335 NSCLC patients. Our goal is to apply a predictive machine learning model to estimate the risk of metastasis based on clinical and SNPs data. We have found that Cox regression with LASSO regularization gives the best predictive accuracy with a concordance index equal to 0.63.