Exploiting Liver CT scans in Colorectal Carcinoma genomics mutation classification
This addresses the need for real-time, biopsy-free mutation analysis in colorectal cancer patients, though it appears incremental as it builds on existing AI methods for medical imaging.
The paper tackled the problem of non-invasive classification of colorectal carcinoma genomics mutations from liver CT scans, achieving an F1 score of 0.73 for identifying RAS mutations.
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.