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EGFR  and  KRAS  are the most frequently mutated genes in lung cancer, being active research topics in targeted therapy. The biopsy is the traditional method to genetically characterise a tumour. However, it is a risky procedure, painful for the patient, and, occasionally, the tumour might be inaccessible. This work aims to study and debate the nature of the relationships between imaging phenotypes and lung cancer-related mutation status. Until now, the literature has failed to point to new research directions, mainly consisting of results-oriented works in a field where there is still not enough available data to train clinically viable models. We intend to open a discussion about critical points and to present new possibilities for future radiogenomics studies. We conducted high-dimensional data visualisation and developed classifiers, which allowed us to analyse the results for  EGFR  and  KRAS  biological markers according to different combinations of input features. We show that  EGFR  mutation status might be correlated to CT scans imaging phenotypes; however, the same does not seem to hold for  KRAS  mutation status. Also, the experiments suggest that the best way to approach this problem is by combining nodule-related features with features from other lung structures.

Identifying relationships between imaging phenotypes and lung cancer-related mutation status: EGFR and KRAS