  Lung cancer is a leading cause of death worldwide. Early-stage detection of
lung cancer is essential for a more favorable prognosis. Radiogenomics is an
emerging discipline that combines medical imaging and genomics features for
modeling patient outcomes non-invasively. This study presents a radiogenomics
pipeline that has: 1) a novel mixed architecture (RA-Seg) to segment lung
cancer through attention and recurrent blocks; and 2) deep feature classifiers
to distinguish Epidermal Growth Factor Receptor (EGFR) mutation status. We
evaluate the proposed algorithm on multiple public datasets to assess its
generalizability and robustness. We demonstrate how the proposed segmentation
and classification methods outperform existing baseline and SOTA approaches
(73.54 Dice and 93 F1 scores).
