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Record W1880365866

A CT method to measure hemodynamics in brain tumors: validation and application of cerebral blood flow maps.

2000· article· en· W1880365866 on OpenAlexaff
Aleksa Cenic, Darius G. Nabavi, Rosemary A. Craen, Adrian W. Gelb, Ting‐Yim Lee

Bibliographic record

VenuePubMed · 2000
Typearticle
Languageen
FieldMedicine
TopicMRI in cancer diagnosis
Canadian institutionsRobarts Clinical Trials
Fundersnot available
KeywordsMedicineCerebral blood flowPerfusionHemodynamicsNormocapniaNuclear medicineBlood flowPerfusion scanningRadiation therapyWhite matterBrain tumorMagnetic resonance imagingRadiologyPathologyHypercapniaCardiologyInternal medicineRespiratory system
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND AND PURPOSE: CT is an imaging technique that is routinely used for evaluating brain tumors. Nonetheless, imaging often cannot show the distinction between radiation necrosis and neoplastic growth among patients with recurrent symptoms after radiation therapy. In such cases, a diagnostic tool that provides perfusion measurements with high anatomic detail would show the separation between necrotic areas, which are characterized by low perfusion, from neoplastic areas, which are characterized by elevated CBF. We attempted to validate a dynamic contrast-enhanced CT method for the measurement of regional CBF in brain tumors, and to apply this method by creating CBF maps. METHODS: We studied nine New Zealand White rabbits with implanted brain tumors. We obtained dynamic CT measurements of CBF, cerebral blood volume (CBV), and permeability surface (PS) from the tumor, peritumor, and contralateral normal tissue regions. In all nine rabbits (two studies per rabbit), we compared CT-derived CBF values with those simultaneously obtained by the standard of reference ex vivo microsphere technique. Using CT, we examined three rabbits to assess the variability of repeated CBF and CBV measurements; we examined the other six to evaluate regional CBF reactivity to arterial carbon dioxide tensions. Finally, CT CBF maps were obtained from a rabbit with a brain tumor during normocapnia and hypocapnia. RESULTS: We found a significant linear correlation (r = 0.847) between the regional CT-and microsphere-derived CBF values, with a slope not significantly different from unity (0.99+/-0.03, P>.01). The mean difference between regional CBF measurements obtained using both methods did not significantly deviate from zero (P>.10). During normocapnia, tumor had significantly higher CBF, CBV, and PS values (P<.05) than did peritumor and normal tissues. The variability in CT-derived CBF and CBV measurements in the repeated studies was 13% and 7%, respectively. CT revealed no significantly different CBF CO2 reactivity from that determined by the microsphere method (P>.10). The CBF map of tumor regions during normocapnia showed much higher flow than normal regions manifested, and this difference was reduced on the hypocapnia CBF map. CONCLUSION: The dynamic CT method presented herein provides absolute CBF measurements in brain tumors that are accurate and precise. Preliminary CBF maps derived with this method demonstrate their potential for depicting areas of different blood flow within tumors and surrounding tissue, indicating its possible use in the clinical setting.

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)0.002
metaresearch head score (Gemma)0.005
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: none
Teacher disagreement score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.016
GPT teacher head0.264
Teacher spread0.249 · 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

Citations229
Published2000
Admission routes1
Has abstractyes

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