Imaging genotyping of functional signaling pathways in lung squamous cell carcinoma using a radiomics approach View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-12

AUTHORS

So Hyeon Bak, Hyunjin Park, Ho Yun Lee, Youngwook Kim, Hyung-Lae Kim, Sin-Ho Jung, Hyeseung Kim, Jonghoon Kim, Keunchil Park

ABSTRACT

Imaging features can be useful for identifying distinct genomic differences and have predictive power for certain phenotypes attributed to genomic mutations. We aimed to identify predictive imaging biomarkers that underpin genomic alterations and clinical outcomes in lung squamous cell carcinoma (SQCC) using a radiomics approach. In 57 patients with lung SQCC who underwent preoperative computed tomography (CT) and whole-exome DNA sequencing, 63 quantitative imaging features were extracted from CT and 73 clinicoradiological features including imaging features were classified into 8 categories: clinical, global, histogram-based, lung cancer-specific, shape, local, regional, and emphysema. Mutational profiles for core signaling pathways of lung SQCC were classified into five categories: redox stress, apoptosis, proliferation, differentiation, and chromatin remodelers. Range and right lung volume was significantly associated with alternation of apoptosis and proliferation pathway (p = 0.03, and p = 0.03). Energy was associated with the redox stress pathway (p = 0.06). None of the clinicoradiological features showed any significant association with the alteration of differentiation and chromatin remodelers pathway. This study showed that radiomic features indicating five different functional pathways of lung SQCC were different form one another. Radiomics approaches to lung SQCC have the potential to noninvasively predict alterations in core signaling pathways and clinical outcome. More... »

PAGES

3284

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41598-018-21706-1

DOI

http://dx.doi.org/10.1038/s41598-018-21706-1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1101020067

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/29459639


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Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1038/s41598-018-21706-1'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/s41598-018-21706-1'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/s41598-018-21706-1'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/s41598-018-21706-1'


 

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