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Record W2145035318 · doi:10.2967/jnumed.111.095653

Quantitative Preclinical Imaging of TSPO Expression in Glioma Using <i>N,N</i>-Diethyl-2-(2-(4-(2-<sup>18</sup>F-Fluoroethoxy)Phenyl)-5,7-Dimethylpyrazolo[1,5-<i>a</i>]Pyrimidin-3-yl)Acetamide

2012· article· en· W2145035318 on OpenAlexfundno aff
Dewei Tang, Matthew R. Hight, Eliot T. McKinley, Allie Fu, Jason R. Buck, R. Adam Smith, Mohammed Noor Tantawy, Todd E. Peterson, Daniel C. Colvin, M. Sib Ansari, Michael L. Nickels, H. Charles Manning

Bibliographic record

VenueJournal of Nuclear Medicine · 2012
Typearticle
Languageen
FieldMedicine
TopicDrug Transport and Resistance Mechanisms
Canadian institutionsnot available
FundersNational Institute of Biomedical Imaging and BioengineeringNational Cancer InstituteMcGill University
KeywordsTranslocator proteinRadioligandIn vivoChemistryMolecular imagingGliomaNuclear medicineIn vitroPathologyCancer researchMedicineBiochemistryBiology

Abstract

fetched live from OpenAlex

UNLABELLED: There is a critical need to develop and rigorously validate molecular imaging biomarkers to aid diagnosis and characterization of primary brain tumors. Elevated expression of translocator protein (TSPO) has been shown to predict disease progression and aggressive, invasive behavior in a variety of solid tumors. Thus, noninvasive molecular imaging of TSPO expression could form the basis of a novel, predictive cancer imaging biomarker. In quantitative preclinical PET studies, we evaluated a high-affinity pyrazolopyrimidinyl-based TSPO imaging ligand, N,N-diethyl-2-(2-(4-(2-(18)F-fluoroethoxy)phenyl)-5,7-dimethylpyrazolo[1,5-a]pyrimidin-3-yl)acetamide ((18)F-DPA-714), as a translational probe for quantification of TSPO levels in glioma. METHODS: Glioma-bearing rats were imaged with (18)F-DPA-714 in a small-animal PET system. Dynamic images were acquired simultaneously on injection of (18)F-DPA-714 (130-200 MBq/0.2 mL). Blood was collected to derive the arterial input function (AIF), with high-performance liquid chromatography radiometabolite analysis performed on selected samples for AIF correction. Compartmental modeling was performed using the corrected AIF. Specific tumor cell binding of DPA-714 was evaluated by radioligand displacement of (3)H-PK 11195 with DPA-714 in vitro and displacement of (18)F-DPA-714 with an excess of DPA-714 in vivo. Immediately after imaging, tumor and healthy brain tissues were harvested for validation by Western blotting and immunohistochemistry. RESULTS: (18)F-DPA-714 was found to preferentially accumulate in tumors, with modest uptake in the contralateral brain. Infusion with DPA-714 (10 mg/kg) displaced (18)F-DPA-714 binding by greater than 60% on average. Tumor uptake of (18)F-DPA-714 was similar to another high-affinity TSPO imaging ligand, (18)F-N-fluoroacetyl-N-(2,5-dimethoxybenzyl)-2-phenoxyaniline, and agreed with ex vivo assay of TSPO levels in tumor and healthy brain. CONCLUSION: These studies illustrate the feasibility of using (18)F-DPA-714 for visualization of TSPO-expressing brain tumors. Importantly, (18)F-DPA-714 appears suitable for quantitative assay of tumor TSPO levels in vivo. Given the relationship between elevated TSPO levels and poor outcome in oncology, these studies suggest the potential of (18)F-DPA-714 PET to serve as a novel predictive cancer imaging modality.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.072
GPT teacher head0.368
Teacher spread0.296 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations61
Published2012
Admission routes1
Has abstractyes

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