  The liver is the most involved organ by distant metastasis in colon-rectal
cancer (CRC) patients and it comes necessary to be aware of the mutational
status of the lesions to correctly design the best individual treatment. So
far, efforts have been made in order to develop non-invasive and real-time
methods that permit the analysis of the whole tumor, using new artificial
intelligence tools to analyze the tumor's image obtained by Computed Tomography
(CT) scan. In order to address the current medical workflow, that is biopsy
analysis-based, we propose the first DeepLearning-based exploration, to our
knowledge, of such classification approach from the patient medical imaging. We
propose i) a solid pipeline for managing undersized datasets of available CT
scans and ii) a baseline study for genomics mutation diagnosis support for
preemptive patient follow-up. Our method is able to identify CRC RAS mutation
family from CT images with 0.73 F1 score.
