• Medientyp: E-Artikel
  • Titel: Relationship between histological tumor margins and magnetic resonance imaging signal intensities in brain neoplasia of dogs
  • Beteiligte: Johnson, Philippa J.; Rivard, Benjamin C.; Wood, Jonathan H.; DiRubio, Mattisen L.; Henry, Joshua G.; Miller, Andrew D.
  • Erschienen: Wiley, 2022
  • Erschienen in: Journal of Veterinary Internal Medicine, 36 (2022) 3, Seite 1039-1048
  • Sprache: Englisch
  • DOI: 10.1111/jvim.16431
  • ISSN: 1939-1676; 0891-6640
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  • Beschreibung: AbstractBackgroundIntracranial neoplasia is relatively common in dogs and stereotactic radiotherapy, surgical debulking, or both, are the most successful treatment approaches. A key component of treatment planning involves delineating tumor margin on magnetic resonance imaging (MRI) examinations. How MRI signal intensity alterations relate to histological tumor margins is unknown.ObjectivesDirectly compare histological brain sections to MRI sequence images and determine which sequence alteration best correlates with tumor margins.AnimalsFive dogs with glioma, 4 dogs with histiocytic sarcoma, and 3 dogs with meningioma.MethodsRetrospective cohort study. Histological brain sections were registered to in vivo MRI scan images obtained within 7 days of necropsy. Margins of signal intensity alterations (T2‐weighted, fluid‐attenuating inversion recovery [FLAIR], T1‐weighted and contrast enhancement) were compared directly to solid tumor and surgical margins identified on histology. Jacquard similarity metrics (JSM) and cross‐sectional areas were calculated.ResultsIn glioma cases, margins drawn around T2‐weighted hyperintensity were most similar to surgical margins (JSM, 0.66 ± 0.17) when compared to other sequences. In both meningioma (JSM, 0.57 ± 0.21) and histiocytic sarcoma (JSM, 0.75 ± 0.11) margins of contrast enhancement were most similar to surgical margins.Conclusions and Clinical ImportanceSignal intensities correspond to tumor margins for different tumor types and facilitate surgical and radiation therapy planning using MRI images.
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