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Record W2586186137

PET/MRI fusion imaging in the follow-up of glucose metabolism and perfusion in tumors after chemotherapy in a murine model of breast cancer

2006· article· en· W2586186137 on OpenAlexaffabout
Étienne Croteau, Martin Pellerin, Luc Tremblay, Étienne Rousseau, Simon Authier, Céléna Dubuc, Roger Lecomte, Martin Lepage, François Bénard

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicMedical Imaging Techniques and Applications
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsMedicinePerfusionNuclear medicineBreast cancerPositron emission tomographyMagnetic resonance imagingDynamic contrast-enhanced MRIBreast MRIChemotherapyRadiologyPerfusion scanningCancerInternal medicineMammography
DOInot available

Abstract

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504 Objectives: While 18F-FDG PET can measure the metabolic activity of tumors and has been shown useful to assess the response to therapy, dynamic contrast enhanced MRI (DCE-MRI) can show corresponding alterations in tumor perfusion and vascular permeability. This study was performed to correlate changes in metabolic activity and perfusion in response to therapy using multi-modality imaging with small animal PET and MRI. Methods: PET and MRI were used to monitor treatment response with doxorubicin of Balb/c mice bearing breast tumors (MC7-L1 and MC4-L2). Imaging and treatment began on day 0, which was 4 weeks after subcutaneous inoculation of 107 cancer cells. Chemotherapy with doxorubicin (7.5 µg/g) was administrated immediately after the PET scan on day 0 and 7. The animals were scanned on day 0, 1, 7 and 14 with MRI, immediately followed by 18F-FDG PET, using a Varian 7T animal scanner for MRI and the Sherbrooke avalanche photodiode animal PET scanner. Adequate fusion of the sequential MRI and PET scans was achieved by using the same molded bed featuring 3 separate 20-µL markers of Gd-DTPA and 18F-FDG. Gd-DTPA was injected to evaluate the tumor vessel permeability for MRI. 750 µCi of 18F-FDG was injected for the whole-body PET scan. Image fusion was performed with an in-house registration software using mutual information maximization. The fiducial markers served to assess the performance of the registration algorithm. Results: Concurrent imaging and fusion of perfusion and metabolism data allow more comprehensive assessment of tumor behavior during chemotherapy. In control mice, tumor progression with variable degree of necrosis was observed. The tumor uptake of 18F-FDG drastically increased in the control group compared to the treated group on days 7 and 14. Meanwhile, a drastic decrease in perfusion was observed between days 7 and 14, with necrosis appearing both in the core and at the periphery of the tumors. In treated mice, the tumor 18F-FDG uptake showed a small increase on day 7, and then either stabilized or weakly increased at day 14, but to a much lower degree than untreated animals. Tumor size and perfusion, however, was observed to remain low throughout the protocol. Conclusions: Whole-body MRI and 18F-FDG PET fusion imaging allows tracking changes in the microcirculation and metabolic activity of tumors during doxorubicin treatment and provided information that was not available by either one of the techniques taken separately. Research Support (if any): Canadian Institutes of Health Research Canadian Breast Cancer Research Alliance

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)0.001
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.008
GPT teacher head0.271
Teacher spread0.263 · 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

Citations0
Published2006
Admission routes2
Has abstractyes

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