Quantitative Analysis of [18F]FMISO PET for Tumor Hypoxia: Correlation of Modeling Results with Immunohistochemistry.

Purpose: Quantitative evaluation of tumor hypoxia based on H-1-(3-[18F]fluoro-2-hydroxypropyl)-2-nitroimidazole ([18F]FMISO) positron emission tomography (PET) can deliver important information for treatment planning in radiotherapy. However, the merits and limitations of different analysis methods...

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Publicado en:Molecular Imaging & Biology Vol. 19; no. 1; pp. 120 - 130
Autores principales: Shi, Kuangyu, Bayer, Christine, Astner, Sabrina, Gaertner, Florian, Vaupel, Peter, Schwaiger, Markus, Huang, Sung-Cheng, Ziegler, Sibylle, Astner, Sabrina T, Gaertner, Florian C, Ziegler, Sibylle I
Formato: diagnostic images equations & formulas pictorial research tables/charts Journal Article
Publicado: Springer Nature Feb2017
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Feb2017
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      pub: Springer Nature
      place: New York, New York
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        10.1007/s11307-016-0975-4
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        atl: Quantitative Analysis of [18F]FMISO PET for Tumor Hypoxia: Correlation of Modeling Results with Immunohistochemistry.
      aug:
        au:
          Shi, Kuangyu
          Bayer, Christine
          Astner, Sabrina
          Gaertner, Florian
          Vaupel, Peter
          Schwaiger, Markus
          Huang, Sung-Cheng
          Ziegler, Sibylle
          Astner, Sabrina T
          Gaertner, Florian C
          Ziegler, Sibylle I
        affil: Department of Nuclear Medicine , Klinikum rechts der Isar, Technische Universität München , Ismaningerstrasse. 22 81675 Munich Germany
      sug:
        subj:
          Tomography, Emission-Computed Methods
          Imidazoles Analogs and Derivatives
          Immunohistochemistry
          Imidazoles
          Cell Line, Tumor
          Mice
          Animal Studies
      ab: Purpose: Quantitative evaluation of tumor hypoxia based on H-1-(3-[18F]fluoro-2-hydroxypropyl)-2-nitroimidazole ([18F]FMISO) positron emission tomography (PET) can deliver important information for treatment planning in radiotherapy. However, the merits and limitations of different analysis methods in revealing the underlying physiological feature are not clear. This study aimed to assess these quantitative analysis methods with the support of immunohistological data.Procedures: Sixteen nude mice bearing xenografted human squamous cell carcinomas (FaDu or CAL-33) were scanned using 2-h dynamic [18F]FMISO PET. Tumors were resected and sliced, and the hypoxia marker pimonidazole was immunostained followed by H&E staining. The pimonidazole signal was segmented using a k-means clustering algorithm, and the hypoxic fraction (HF) was calculated as the hypoxic area/viable tumor-tissue-area ratio pooled over three tissue slices from the apical, center, and basal layers. PET images were analyzed using various methods including static analysis [standard uptake value (SUV), tumor-to-blood ratio (T/B), tumor-to-muscle ratio (T/M)] and kinetic modeling (Casciari αk A , irreversible and reversible two-tissue compartment k 3, Thorwarth w A k 3, Patlak K i , Logan V d , Cho K), and correlated with HF.Results: No significant correlation was found for static analysis. A significant correlation between k 3 of the irreversible two-tissue compartment model and HF was observed (r = 0.61, p = 0.01). The correlation between HF and αk A of the Casciari model could be improved through reducing local minima by testing more sets of initial values (r = 0.59, p = 0.02) or by reducing the model complexity by fixing three parameters (r = 0.63, p = 0.0008).Conclusions: With support of immunohistochemistry data, this study shows that various analysis methods for [18F]FMISO PET perform differently for assessment of tumor hypoxia. A better fitting quality does not necessarily mean a higher physiological correlation. Hypoxia PET analysis needs to consider both the mathematical stability and physiological fidelity. Based on the results of this study, preference should be given to the irreversible two-tissue compartment model as well as the Casciari model with reduced parameters.
      pubtype: Academic Journal
      doctype:
        diagnostic images
        equations & formulas
        pictorial
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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