De novo liquid biopsy and radio genomic diagnostic approach with combined deep learning artificial neural networks for NSCLC

Each year, the mortality rate and incidence of non-small cell lung cancer (NSCLC) are dramatically increasing. The introduction of liquid biopsy in the clinical practice of NSCLC has completely revolutionized the approach to such neoplasm since is generally detected through complex and invasive procedures and unfortunately at advanced stages. The importance and innovation of liquid biopsy are linked with the possibility of cancer detection at every stage, adjuvant treatment, resistance genotyping, systematic initiation of treatment, minimal residual disease, early detection of relapse, and screening of NSCLC. Circulating tumor DNA (ctDNA) is now emerging as a non-invasive biomarker that will help to track tumor burden and allow the monitoring of cancer genome in blood across several malignancies. Recently, the combination of liquid biopsy and radiomics seems to deliver an efficient way to study cancer evolution over time providing an important support tool to daily clinical practice. CT (Computed Tomography) images are of particular importance in this context because they convey functional and anatomical information, respectively. Machine learning provides a variety of approaches for dealing with this potentially high-dimensional challenge. In particular, we used Enet neural network for image assessment. This study represents an interesting attempt to explore the usefulness of liquid biopsy, radiomics, and deep learning in the NSCLC clinical routine. We studied a NSCLC patient cohort from the first access to our department to follow-up. Our results showed a promising correlation between the ctDNA quantity and radiomic features evaluated by automated computed tomography according to RECIST criteria with the Enet deep learning method, which allowed us to define more accurately progression-free survival (PFS) and overall survival (OS) of patients during the course of cancer history. Therefore, the above mentioned diagnostic tools including the combination of liquid biopsy, radiomics, and deep learning tools collectively can represent a very robust and new approach in the monitoring and management of NSCLC.