dc.contributor.author | Zaw Thin, M | |
dc.contributor.author | Moore, C | |
dc.contributor.author | Snoeks, T | |
dc.contributor.author | Kalber, T | |
dc.contributor.author | Downward, J | |
dc.contributor.author | Behrens, A | |
dc.coverage.spatial | England | |
dc.date.accessioned | 2023-03-03T15:21:13Z | |
dc.date.available | 2023-03-03T15:21:13Z | |
dc.date.issued | 2022-12-09 | |
dc.identifier | 10.1038/s41596-022-00769-5 | |
dc.identifier.citation | Nature Protocols, 2022, | |
dc.identifier.issn | 1754-2189 | |
dc.identifier.uri | https://repository.icr.ac.uk/handle/internal/5706 | |
dc.identifier.eissn | 1750-2799 | |
dc.identifier.eissn | 1750-2799 | |
dc.identifier.doi | 10.1038/s41596-022-00769-5 | |
dc.description.abstract | X-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.format | Print-Electronic | |
dc.language | eng | |
dc.language.iso | eng | |
dc.publisher | NATURE PORTFOLIO | |
dc.relation.ispartof | Nature Protocols | |
dc.rights.uri | http://www.rioxx.net/licenses/all-rights-reserved | |
dc.subject | Science & Technology | |
dc.subject | Life Sciences & Biomedicine | |
dc.subject | Biochemical Research Methods | |
dc.subject | Biochemistry & Molecular Biology | |
dc.subject | TRANSGENIC MOUSE MODEL | |
dc.subject | X-RAY TUBE | |
dc.subject | COMPUTED-TOMOGRAPHY | |
dc.subject | MICROCOMPUTED TOMOGRAPHY | |
dc.subject | CANCER MODELS | |
dc.subject | LUNG-TUMORS | |
dc.subject | SPECTRA | |
dc.subject | LUCIFERASE | |
dc.subject | RESISTANCE | |
dc.subject | GROWTH | |
dc.title | Micro-CT acquisition and image processing to track and characterize pulmonary nodules in mice. | |
dc.type | Journal Article | |
dcterms.dateAccepted | 2022-08-09 | |
dc.date.updated | 2023-03-03T15:18:57Z | |
rioxxterms.version | AM | |
rioxxterms.versionofrecord | 10.1038/s41596-022-00769-5 | |
rioxxterms.licenseref.startdate | 2022-12-09 | |
rioxxterms.type | Journal Article/Review | |
pubs.author-url | https://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-status | Published online | |
pubs.publisher-url | http://dx.doi.org/10.1038/s41596-022-00769-5 | |
icr.researchteam | Cancer Stem Cell | |
icr.researchteam | Lung Cancer Group | |
icr.researchteam | Convergence SC Management | |
dc.contributor.icrauthor | Zaw Thin, May | |
dc.contributor.icrauthor | Behrens, Axel | |
icr.provenance | Deposited by Mr Arek Surman on 2023-03-03. Deposit type is initial. No. of files: 1. Files: ZawThin&Moore_NP-P210179C.pdf | |