Fused feature signatures to probe tumour radiogenomics relationships

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dc.contributor.authorXia, Tianen
dc.contributor.authorKumar, Ashnilen
dc.contributor.authorFulham, Michaelen
dc.contributor.authorFeng, Daganen
dc.contributor.authorWang, Yueen
dc.contributor.authorKim, Eun Youngen
dc.contributor.authorJung, Younhyunen
dc.contributor.authorKim, Jinmanen
dc.date.accessioned2022-02-19T20:09:16Zen
dc.date.available2022-02-19T20:09:16Zen
dc.date.issued2022-02-09en
dc.date.updated2022-02-19T20:09:11Zen
dc.description.abstractRadiogenomics relationships (RRs) aims to identify statistically significant correlations between medical image features and molecular characteristics from analysing tissue samples. Previous radiogenomics studies mainly relied on a single category of image feature extraction techniques (ETs); these are (i) handcrafted ETs that encompass visual imaging characteristics, curated from knowledge of human experts and, (ii) deep ETs that quantify abstract-level imaging characteristics from large data. Prior studies therefore failed to leverage the complementary information that are accessible from fusing the ETs. In this study, we propose a fused feature signature (FFSig): a selection of image features from handcrafted and deep ETs (e.g., transfer learning and fine-tuning of deep learning models). We evaluated the FFSig's ability to better represent RRs compared to individual ET approaches with two public datasets: the first dataset was used to build the FFSig using 89 patients with non-small cell lung cancer (NSCLC) comprising of gene expression data and CT images of the thorax and the upper abdomen for each patient; the second NSCLC dataset comprising of 117 patients with CT images and RNA-Seq data and was used as the validation set. Our results show that our FFSig encoded complementary imaging characteristics of tumours and identified more RRs with a broader range of genes that are related to important biological functions such as tumourigenesis. We suggest that the FFSig has the potential to identify important RRs that may assist cancer diagnosis and treatment in the future.en
dc.description.versionPublished versionen
dc.format.extentPages 2173en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1038/s41598-022-06085-yen
dc.identifier.eissn2045-2322en
dc.identifier.issn2045-2322en
dc.identifier.issue1en
dc.identifier.orcidWang, Yue [0000-0002-1788-1102]en
dc.identifier.other10.1038/s41598-022-06085-y (PII)en
dc.identifier.pmid35140267en
dc.identifier.urihttp://hdl.handle.net/10919/108770en
dc.identifier.volume12en
dc.language.isoenen
dc.relation.urihttps://www.ncbi.nlm.nih.gov/pubmed/35140267en
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.titleFused feature signatures to probe tumour radiogenomics relationshipsen
dc.title.serialScientific Reportsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherJournal Articleen
dcterms.dateAccepted2022-01-14en
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Engineeringen
pubs.organisational-group/Virginia Tech/Engineering/Electrical and Computer Engineeringen
pubs.organisational-group/Virginia Tech/Faculty of Health Sciencesen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Engineering/COE T&R Facultyen

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