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dc.contributor.authorZaw Thin, M
dc.contributor.authorMoore, C
dc.contributor.authorSnoeks, T
dc.contributor.authorKalber, T
dc.contributor.authorDownward, J
dc.contributor.authorBehrens, A
dc.coverage.spatialEngland
dc.date.accessioned2023-03-03T15:21:13Z
dc.date.available2023-03-03T15:21:13Z
dc.date.issued2022-12-09
dc.identifier10.1038/s41596-022-00769-5
dc.identifier.citationNature Protocols, 2022,
dc.identifier.issn1754-2189
dc.identifier.urihttps://repository.icr.ac.uk/handle/internal/5706
dc.identifier.eissn1750-2799
dc.identifier.eissn1750-2799
dc.identifier.doi10.1038/s41596-022-00769-5
dc.description.abstractX-ray computed tomography is a reliable technique for the detection and longitudinal monitoring of pulmonary nodules. In preclinical stages of diagnostic or therapeutic development, the miniaturized versions of the clinical computed tomography scanners are ideally suited for carrying out translationally-relevant research in conditions that closely mimic those found in the clinic. In this Protocol, we provide image acquisition parameters optimized for low radiation dose, high-resolution and high-throughput computed tomography imaging using three commercially available micro-computed tomography scanners, together with a detailed description of the image analysis tools required to identify a variety of lung tumor types, characterized by specific radiological features. For each animal, image acquisition takes 4-8 min, and data analysis typically requires 10-30 min. Researchers with basic training in animal handling, medical imaging and software analysis should be able to implement this protocol across a wide range of lung cancer models in mice for investigating the molecular mechanisms driving lung cancer development and the assessment of diagnostic and therapeutic agents.
dc.formatPrint-Electronic
dc.languageeng
dc.language.isoeng
dc.publisherNATURE PORTFOLIO
dc.relation.ispartofNature Protocols
dc.rights.urihttp://www.rioxx.net/licenses/all-rights-reserved
dc.subjectScience & Technology
dc.subjectLife Sciences & Biomedicine
dc.subjectBiochemical Research Methods
dc.subjectBiochemistry & Molecular Biology
dc.subjectTRANSGENIC MOUSE MODEL
dc.subjectX-RAY TUBE
dc.subjectCOMPUTED-TOMOGRAPHY
dc.subjectMICROCOMPUTED TOMOGRAPHY
dc.subjectCANCER MODELS
dc.subjectLUNG-TUMORS
dc.subjectSPECTRA
dc.subjectLUCIFERASE
dc.subjectRESISTANCE
dc.subjectGROWTH
dc.titleMicro-CT acquisition and image processing to track and characterize pulmonary nodules in mice.
dc.typeJournal Article
dcterms.dateAccepted2022-08-09
dc.date.updated2023-03-03T15:18:57Z
rioxxterms.versionAM
rioxxterms.versionofrecord10.1038/s41596-022-00769-5
rioxxterms.licenseref.startdate2022-12-09
rioxxterms.typeJournal Article/Review
pubs.author-urlhttps://www.ncbi.nlm.nih.gov/pubmed/36494493
pubs.organisational-group/ICR
pubs.organisational-group/ICR/Primary Group
pubs.organisational-group/ICR/Primary Group/ICR Divisions
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Cancer Biology
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Cancer Biology/Lung Cancer Group
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Closed research teams
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Molecular Pathology
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Molecular Pathology/Cancer Stem Cell
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Closed research teams/Lung Cancer Group
pubs.publication-statusPublished online
pubs.publisher-urlhttp://dx.doi.org/10.1038/s41596-022-00769-5
icr.researchteamCancer Stem Cell
icr.researchteamLung Cancer Group
icr.researchteamConvergence SC Management
dc.contributor.icrauthorZaw Thin, May
dc.contributor.icrauthorBehrens, Axel
icr.provenanceDeposited by Mr Arek Surman on 2023-03-03. Deposit type is initial. No. of files: 1. Files: ZawThin&Moore_NP-P210179C.pdf


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