A Radiogenomics Ensemble to Predict EGFR and KRAS Mutations in NSCLC.

ABSTRACT: Lung cancer causes more deaths globally than any other type of cancer. To determine the best treatment, detecting EGFR and KRAS mutations is of interest. However, non-invasive ways to obtain this information are not available. Furthermore, many times there is a lack of big enough relevant public datasets, so the performance of single classifiers is not outstanding. In this paper, an ensemble approach is applied to increase the performance of EGFR and KRAS mutation prediction using a small dataset. A new voting scheme, Selective Class Average Voting (SCAV), is proposed and its performance is assessed both for machine learning models and CNNs. For the EGFR mutation, in the machine learning approach, there was an increase in the sensitivity from 0.66 to 0.75, and an increase in AUC from 0.68

SUBMITTER: Moreno S 

PROVIDER: S-EPMC8162978 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature