Lung Cancer Detection And Characterisation Through Genomic And Radiomic Biomarkers

Medical image bio-markers of cancer are expected to improve patient care through advances in precision medicine. Compared to genomic bio-markers, bio-markers obtained directly from medical images provide the advantages of being a non-invasive procedure, and characterizing a heterogeneous tumor in its entirety, as opposed to limited tissue available for biopsy. In this paper, with the aim to demonstrate that non-invasive features can obtain better performances if compared to invasive ones in lung cancer detection and characterisation, we propose a method to discriminate between different lung cancers (i.e., Adenocarcinoma and Squamous Cell Carcinoma) by adopting both invasive (genomic) and non-invasive (radiomic) bio-markers, by building supervised machine learning models exploiting both invasive and non-invasive features. Experiments on a data-set of 130 patients show that radiomic bio-markers obtain better performances (with an f-measure equal to 0.993) if compared to the ones obtained by considering genomic ones (reaching an f-measure equal to 0.929) in lung cancer detection and characterisation.