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Record W2498590714 · doi:10.1371/journal.pone.0140892

Estimation of Tumor Interstitial Fluid Pressure (TIFP) Noninvasively

2016· article· en· W2498590714 on OpenAlexafffund
Long Jian Liu, Stephen L. Brown, James R. Ewing, Kenneth Schneider, Mordechay Schlesinger

Bibliographic record

VenuePLoS ONE · 2016
Typearticle
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsWindsor Regional HospitalUniversity of Windsor
FundersWindsor Cancer Centre FoundationKræftens Bekæmpelse
KeywordsInterstitial fluidFluid pressureMedicinePathologyPhysicsMechanics

Abstract

fetched live from OpenAlex

Tumor interstitial fluid pressure (TIFP), is a physiological parameter with demonstrated predictive value for a tumor's aggressiveness, drug delivery, as well as response to treatments such as radiotherapy and chemotherapy. Despite its utility, measurement of TIFP has been limited by the need for invasive procedures. In this work, the theoretical basis for approaching the absolute value of TIFP and the experimental method for noninvasively measuring TIFP are presented. Given specific boundary and continuity conditions, we convert theoretical variables into measurable variables by applying MRI technology. The work shows that TIFP in the central region of the tumor can be estimated by an analysis of the variation of tissue fluid motion in the tumor rim and surrounding tissue. It is determined from three noninvasive measurable parameters: i) an estimate of the velocity of the tumor interstitial fluid at the tumor surface, which is maximal, ii) a measurement of the distance from the tumor surface to where the tumor exudates are absorbed (or normalized), and iii) an estimate of the hydraulic conductivity of the interstitium through which the tumor exudate travels. We experimentally show that the fluid flow within the tumor rim is not uniform, even for a round shaped tumor, and demonstrate the procedures for the noninvasive measurement of TIFP.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.257

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 head0.019
GPT teacher head0.192
Teacher spread0.173 · 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 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

Citations38
Published2016
Admission routes2
Has abstractyes

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