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Mapping in vivo tumor oxygenation within viable tumor by 19F-MRI and multispectral analysis.

ABSTRACT: Quantifying oxygenation in viable tumor remains a major obstacle toward a better understanding of the tumor micro-environment and improving treatment strategies. Current techniques are often complicated by tumor heterogeneity. Herein, a novel in vivo approach that combines (19)F magnetic resonance imaging ((19)F-MRI) R 1 mapping with diffusion-based multispectral (MS) analysis is introduced. This approach restricts the partial pressure of oxygen (pO2) measurements to viable tumor, the tissue of therapeutic interest. The technique exhibited sufficient sensitivity to detect a breathing gas challenge in a xenograft tumor model, and the hypoxic region measured by MS (19)F-MRI was strongly correlated with histologic estimates of hypoxia. This approach was then applied to address the effects of antivascular agents on tumor oxygenation, which is a research question that is still under debate. The technique was used to monitor longitudinal pO2 changes in response to an antibody to vascular endothelial growth factor (B20.4.1.1) and a selective dual phosphoinositide 3-kinase/mammalian target of rapamycin inhibitor (GDC-0980). GDC-0980 reduced viable tumor pO2 during a 3-day treatment period, and a significant reduction was also produced by B20.4.1.1. Overall, this method provides an unprecedented view of viable tumor pO2 and contributes to a greater understanding of the effects of antivascular therapies on the tumor's microenvironment.

SUBMITTER: Shi Y

PROVIDER: S-EPMC3858898 | biostudies-other | 2013 Nov

REPOSITORIES: biostudies-other

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Project description:The accurate segmentation of in vivo magnetic resonance imaging (MRI) data is a crucial prerequisite for the reliable assessment of disease progression, patient stratification or the establishment of putative imaging biomarkers. This is especially important for the hippocampal formation, a brain area involved in memory formation and often affected by neurodegenerative or psychiatric diseases. FreeSurfer, a widely used automated segmentation software, offers hippocampal subfield delineation with multiple input options. While a single T1-weighted (T1) sequence is regularly used by most studies, it is also possible and advised to use a high-resolution T2-weighted (T2H) sequence or multispectral information. In this investigation it was determined whether there are differences in volume estimations depending on the input images and which combination of these deliver the most reliable results in each hippocampal subfield. 41 healthy participants (age = 25.2 years ± 4.2 SD) underwent two structural MRIs at three Tesla (time between scans: 23 days ± 11 SD) using three different structural MRI sequences, to test five different input configurations (T1, T2, T2H, T1 and T2, and T1 and T2H). We compared the different processing pipelines in a cross-sectional manner and assessed reliability using test-retest variability (%TRV) and the dice coefficient. Our analyses showed pronounced significant differences and large effect sizes between the processing pipelines in several subfields, such as the molecular layer (head), CA1 (head), hippocampal fissure, CA3 (head and body), fimbria and CA4 (head). The longitudinal analysis revealed that T1 and multispectral analysis (T1 and T2H) showed overall higher reliability across all subfields than T2H alone. However, the specific subfields had a substantial influence on the performance of segmentation results, regardless of the processing pipeline. Although T1 showed good test-retest metrics, results must be interpreted with caution, as a standard T1 sequence relies heavily on prior information of the atlas and does not take the actual fine structures of the hippocampus into account. For the most accurate segmentation, we advise the use of multispectral information by using a combination of T1 and high-resolution T2-weighted sequences or a T2 high-resolution sequence alone.

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Mapping in vivo tumor oxygenation within viable tumor by 19F-MRI and multispectral analysis.

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