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1000 Titel
  • CT Texture analysis and CT scores for characterization of fluid collections
1000 Autor/in
  1. Meyer, Hans-Jonas |
  2. Schnarkowski, Benedikt |
  3. Leonhardi, Jakob |
  4. Mehdorn, Matthias |
  5. Ebel, Sebastian |
  6. Goessmann, Holger |
  7. Denecke, Timm |
1000 Erscheinungsjahr 2021
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2021-12-06
1000 Erschienen in
1000 Quellenangabe
  • 21(1):187
1000 Copyrightjahr
  • 2021
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.1186/s12880-021-00718-w |
  • https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8647367/ |
1000 Publikationsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • Background!#!Texture analysis derived from Computed tomography (CT) might be able to better characterize fluid collections undergoing CT-guided percutaneous drainage treatment. The present study tested, whether texture analysis can reflect microbiology results in fluid collections suspicious for septic focus.!##!Methods!#!Overall, 320 patients with 402 fluid collections were included into this retrospective study. All fluid collections underwent CT-guided drainage treatment and were microbiologically evaluated. Clinically, serologically parameters and conventional imaging findings as well as textures features were included into the analysis. A new CT score was calculated based upon imaging features alone. Established CT scores were used as a reference standard.!##!Results!#!The present score achieved a sensitivity of 0.78, a specificity of 0.69, area under curve (AUC 0.82). The present score and the score by Gnannt et al. (AUC 0.81) were both statistically better than the score by Radosa et al. (AUC 0.75). Several texture features were statistically significant between infected fluid collections and sterile fluid collections, but these features were not significantly better compared with conventional imaging findings.!##!Conclusions!#!Texture analysis is not superior to conventional imaging findings for characterizing fluid collections. A novel score was calculated based upon imaging parameters alone with similar diagnostic accuracy compared to established scores using imaging and clinical features.
1000 Sacherschließung
lokal Female [MeSH]
lokal Aged, 80 and over [MeSH]
lokal Aged [MeSH]
lokal Adult [MeSH]
lokal Humans [MeSH]
lokal Drainage [MeSH]
lokal Retrospective Studies [MeSH]
lokal Middle Aged [MeSH]
lokal Tomography, X-Ray Computed/methods [MeSH]
lokal Drainage treatment
lokal Exudates and Transudates/diagnostic imaging [MeSH]
lokal Radiographic Image Interpretation, Computer-Assisted [MeSH]
lokal CT
lokal Sensitivity and Specificity [MeSH]
lokal Male [MeSH]
lokal Research
lokal Fluid collection
lokal Texture analysis
1000 Liste der Beteiligten
  1. https://frl.publisso.de/adhoc/uri/TWV5ZXIsIEhhbnMtSm9uYXM=|https://frl.publisso.de/adhoc/uri/U2NobmFya293c2tpLCBCZW5lZGlrdA==|https://frl.publisso.de/adhoc/uri/TGVvbmhhcmRpLCBKYWtvYg==|https://frl.publisso.de/adhoc/uri/TWVoZG9ybiwgTWF0dGhpYXM=|https://frl.publisso.de/adhoc/uri/RWJlbCwgU2ViYXN0aWFu|https://frl.publisso.de/adhoc/uri/R29lc3NtYW5uLCBIb2xnZXI=|https://frl.publisso.de/adhoc/uri/RGVuZWNrZSwgVGltbQ==
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  • DeepGreen-ID: 98be13b4626645b4b36635cffe90fcfe ; metadata provieded by: DeepGreen (https://www.oa-deepgreen.de/api/v1/), LIVIVO search scope life sciences (http://z3950.zbmed.de:6210/livivo), Crossref Unified Resource API (https://api.crossref.org/swagger-ui/index.html), to.science.api (https://frl.publisso.de/), ZDB JSON-API (beta) (https://zeitschriftendatenbank.de/api/), lobid - Dateninfrastruktur für Bibliotheken (https://lobid.org/resources/search)
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1000 @id frl:6443524.rdf
1000 Erstellt am 2023-04-27T09:40:23.600+0200
1000 Erstellt von 322
1000 beschreibt frl:6443524
1000 Zuletzt bearbeitet 2023-10-19T14:57:33.410+0200
1000 Objekt bearb. Thu Oct 19 14:57:33 CEST 2023
1000 Vgl. frl:6443524
1000 Oai Id
  1. oai:frl.publisso.de:frl:6443524 |
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