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Record W1971042025 · doi:10.1148/radiol.2363041293

Functional CT for Quantifying Tumor Perfusion in Antiangiogenic Therapy in a Rat Model

2005· article· en· W1971042025 on OpenAlexaff
Zuxing Kan, Sith Phongkitkarun, Satoshi Kobayashi, Yi Tang, Lee M. Ellis, Ting Y. Lee, Chusilp Charnsangavej

Bibliographic record

VenueRadiology · 2005
Typearticle
Languageen
FieldMedicine
TopicMRI in cancer diagnosis
Canadian institutionsSt Joseph's Health CentreLawson Health Research Institute
FundersNational Cancer Institute
KeywordsMedicinePerfusionAntiangiogenic therapyPerfusion scanningNuclear medicineRadiologyAngiogenesisInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE: To determine the histologic basis of perfusion parameters measured at functional computed tomography (CT) and to examine the relationship between changes in perfusion and changes in histologic parameters after antiangiogenic therapy in a rat model. MATERIALS AND METHODS: This study had institutional animal care and use committee approval. Among 20 Fischer rats with implanted FN13762 tumors in the liver, 10 were treated with SU5416, a tyrosine kinase inhibitor of vascular endothelial growth factor receptor, and 10 were treated with the diluent only as control rats. Six rats chosen at random from each group underwent functional CT for the measurement of tumor blood flow, blood volume, mean transit time, and permeability-surface area product. Tumor tissue slides corresponding to functional CT sections were examined to measure tumor microvascular density, number of luminal vessels, vascular perimeter, and vascular area. Two-tailed Student t testing was used to determine differences in growth, numbers of metastases to major organs, vascularity, and perfusion between SU5416-treated and control tumors. Pearson correlation coefficients were used to investigate relationships between vascular parameters. RESULTS: Mean tumor volume and number of metastases, respectively, were lower in SU5416-treated rats than in control rats (1580 mm3 +/- 830 [standard deviation] vs 2330 mm3 +/- 960 and 22.4 +/- 11.0 vs 35.2 +/- 17.3); however, these differences were not significant (P = .084 and P = .079). Mean tumor microvascular density was significantly lower in SU5416-treated rats than in control rats (6.4 vessels per field +/- 4.6 vs 17.2 vessels per field +/- 7.5, P < .001); however, vessel perimeter and vessel area, respectively, were significantly larger in treated rats than in control rats (470 microm per field +/- 320 vs 360 microm per field +/- 270, P = .02; and 4010 microm2 per field +/- 2990 vs 2230 microm2 per field +/- 1750, P = .001). Significant correlations were observed between microvascular density and vessel perimeter and area (r = 0.59 and r = 0.25, respectively; P < .01 for both) in SU5416-treated tumors but not control tumors. Blood flow, blood volume, and permeability-surface area product at functional CT were significantly higher in SU5416-treated tumors than in control tumors (P < .001 for all). CONCLUSION: These results validate the idea that functional CT can help quantify the perfusion function of mature vessels but not changes in microvessel density in antiangiogenic therapy.

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 score0.002
Threshold uncertainty score0.007

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.084
GPT teacher head0.337
Teacher spread0.253 · 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

Citations119
Published2005
Admission routes1
Has abstractyes

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