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 noninvasive 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.

Lung Cancer Detection and Characterisation through Genomic and Radiomic Biomarkers

Brunese L.;Mercaldo F.;Santone A.
2020-01-01

Abstract

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 noninvasive 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.
2020
978-1-7281-6926-2
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11695/109550
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