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Record W1796639951 · doi:10.1002/jmri.24433

Longitudinal anatomical and metabolic MRI characterization of orthotopic xenograft prostate tumors in nude mice

2013· article· en· W1796639951 on OpenAlexaff
Christiane L. Mallett, Heeseung Lim, Kundan Thind, Yuhua Chen, Emeline J. Ribot, Francisco M. Martínez, Timothy J. Scholl, Paula J. Foster

Bibliographic record

VenueJournal of Magnetic Resonance Imaging · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Research and Treatments
Canadian institutionsRobarts Clinical TrialsWestern University
Fundersnot available
KeywordsMedicineProstate cancerMagnetic resonance imagingLymphProstateMetastasisNuclear medicinePathologyDynamic contrast-enhanced MRIMultiparametric MRICancerRadiologyInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE: To assess anatomic and functional magnetic resonance imaging (MRI) for monitoring of tumor volume and metabolism of orthotopic xenograft prostate cancer tumors. MATERIALS AND METHODS: Human-derived PC-3M cells were implanted into the prostate in 22 nude mice. Tumor volume and MRI appearance were monitored for up to 29 days. Histology was performed to detect metastases. Hyperpolarized [1-(13) C]pyruvate MRI was used to measure tumor metabolism on day 22. RESULTS: Tumors were visible by MRI 9 days after tumor cell implantation. Tumor volume increased to 720 ± 190 mm(3) on day 29 of imaging. Metastasis was seen in the iliac lymph nodes at all timepoints, and in more distant lymph nodes at later timepoints, but was not detectable by MRI. Regions with low pyruvate uptake corresponded to regions with necrosis and had a higher lactate/pyruvate ratio (0.98 ± 0.4 vs. 1.6 ± 1.1). CONCLUSION: MRI using the balanced steady-state free precession (bSSFP) sequence can be used to monitor tumor growth in orthotopic PC-3M tumors as early as 9 days post-injection. Hyperpolarized pyruvate MRI has potential to assess tumor metabolism and necrosis.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.390
Threshold uncertainty score0.314

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
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)0.0000.000

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 head0.006
GPT teacher head0.252
Teacher spread0.246 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations6
Published2013
Admission routes1
Has abstractyes

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