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Contrasting the vascular response to sunitinib as measured by DCE-CT, DCE-MRI, and DCE-US.

2013· article· en· W2590484520 on OpenAlexaff
John M. Hudson, Ross Williams, Colleen Bailey, Alex Kiss, Laurent Milot, Mostafa Atri, Greg J. Stanisz, Peter N. Burns, Georg A. Bjarnason

Bibliographic record

VenueJournal of Clinical Oncology · 2013
Typearticle
Languageen
FieldMedicine
TopicMRI in cancer diagnosis
Canadian institutionsUniversity Health NetworkHealth Sciences CentreSunnybrook Health Science CentreUniversity of Toronto
Fundersnot available
KeywordsMedicineSunitinibNuclear medicineMagnetic resonance imagingDynamic contrastRadiologyRenal cell carcinomaInternal medicine

Abstract

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378 Background: Medical imaging (DCE-MRI, DCE-CT, DCE-US) provides localized information about the integrity and hemodynamics of the tumor microvasculature. Methods: 34 treatment (Rx) naive pts with mRCC and an abdominal tumor suitable for imaging received Sunitinib 50 mg on a 4wk-on/2wk-off schedule. DCE-US, DCE-CT, and DCE-MRI were done at baseline, during the first course of Rx and after 2wks off Rx. Imaging parameters obtained included vessel permeability (Ktrans), extracellular volume fraction (ve) and Ktrans/ve=Kep (by DCE-MRI), permeability surface product (PS, by DCE-CT), blood volume (BV, by DCE-CT and DCE-US), and blood flow (by DCE-US). We also developed a morphology parameter (MP) that relates the flow kinetics of an intravascular microbubble contrast agent to tumor vascular morphology using DCE-US. Results: A range of imaging parameters that predicted for progression free survival (PFS) were identified in responding pts (N = 26). Baseline imaging parameters correlated with PFS: Ktrans by DCE-MR (r = 0.53, p = 0.01, N = 24), BV by DCE-CT (r = 0.48, p = 0.02, N = 25) and disorganized vessel morphology (large MP) by DCE-US (Spearman r = 0.-0.45, p = 0.02, N = 24). Changes from baseline imaging parameters correlated with PFS: BV by DCE-US at 2 wks (r = -0.46, p = 0.02, N = 24), Kep by DCE-MR at 2 wks (r = -0.45, p = 0.03, N = 24) and MP at 1wk (r = 0.67, p = 0.02, N = 12). There was a correlation between imaging methods: BV measured by DCE-CT correlated with BV by DCE-US (r = 0.46, p = 0.03, N = 23) and Kep by DCE-MR (r = 0.59, p = 0.003, N = 22); Permeability measured by DCE-MR (Ktrans) and DCE-CT (PS) correlated at 2wks (r = 0.56, p = 0.01, N = 21). Conclusions: This is the first study to contrast DCE-US, DCE-CT, and DCE-MRI imaging in pts receiving antiangiogenic therapy. Baseline parameters for all three methods can predict for PFS. Changes from baseline in DCE-US and DCE-MRI parameters also predict for PFS. There is a correlation between imaging methods in parameters that measure BV and permeability. A novel DCE-US parameter was developed (MP) that quantifies the degree of tumor vascular disorganization. Baseline values in MP and changes during Rx correlate with PFS. Clinical trial information: OCT1205, SU Timing RCC.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.115
GPT teacher head0.461
Teacher spread0.346 · 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 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".

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Citations3
Published2013
Admission routes1
Has abstractyes

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