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MR histology reveals tissue features beneath heterogeneous MRI signal in genetically engineered mouse models of sarcoma.
Blocker, Stephanie J; Mowery, Yvonne M; Everitt, Jeffrey I; Cook, James; Cofer, Gary Price; Qi, Yi; Bassil, Alex M; Xu, Eric S; Kirsch, David G; Badea, Cristian T; Johnson, G Allan.
Afiliación
  • Blocker SJ; Department of Radiology, Duke University Medical Center, Duke University, Durham, NC, United States.
  • Mowery YM; Department of Radiation Oncology, Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA, United States.
  • Everitt JI; Department of Pathology, Duke University Medical Center, Duke University, Durham, NC, United States.
  • Cook J; Department of Radiology, Duke University Medical Center, Duke University, Durham, NC, United States.
  • Cofer GP; Department of Radiology, Duke University Medical Center, Duke University, Durham, NC, United States.
  • Qi Y; Department of Radiology, Duke University Medical Center, Duke University, Durham, NC, United States.
  • Bassil AM; Department of Radiation Oncology, Duke University Medical Center, Duke University, Durham, NC, United States.
  • Xu ES; Duke University Medical Center, Duke University, Durham, NC, United States.
  • Kirsch DG; Departments of Radiation Oncology and Medical Biophysics, Princess Margaret Cancer Centre, University Health Network (UHN), Toronto, ON, Canada.
  • Badea CT; Department of Radiology, Duke University Medical Center, Duke University, Durham, NC, United States.
  • Johnson GA; Department of Radiology, Duke University Medical Center, Duke University, Durham, NC, United States.
Front Oncol ; 14: 1287479, 2024.
Article en En | MEDLINE | ID: mdl-38884083
ABSTRACT

Purpose:

To identify significant relationships between quantitative cytometric tissue features and quantitative MR (qMRI) intratumorally in preclinical undifferentiated pleomorphic sarcomas (UPS). Materials and

methods:

In a prospective study of genetically engineered mouse models of UPS, we registered imaging libraries consisting of matched multi-contrast in vivo MRI, three-dimensional (3D) multi-contrast high-resolution ex vivo MR histology (MRH), and two-dimensional (2D) tissue slides. From digitized histology we generated quantitative cytometric feature maps from whole-slide automated nuclear segmentation. We automatically segmented intratumoral regions of distinct qMRI values and measured corresponding cytometric features. Linear regression analysis was performed to compare intratumoral qMRI and tissue cytometric features, and results were corrected for multiple comparisons. Linear correlations between qMRI and cytometric features with p values of <0.05 after correction for multiple comparisons were considered significant.

Results:

Three features correlated with ex vivo apparent diffusion coefficient (ADC), and no features correlated with in vivo ADC. Six features demonstrated significant linear relationships with ex vivo T2*, and fifteen features correlated significantly with in vivo T2*. In both cases, nuclear Haralick texture features were the most prevalent type of feature correlated with T2*. A small group of nuclear topology features also correlated with one or both T2* contrasts, and positive trends were seen between T2* and nuclear size metrics.

Conclusion:

Registered multi-parametric imaging datasets can identify quantitative tissue features which contribute to UPS MR signal. T2* may provide quantitative information about nuclear morphology and pleomorphism, adding histological insights to radiological interpretation of UPS.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Front Oncol Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Suiza
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Front Oncol Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Suiza