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Record W1923837660 · doi:10.1002/jmri.24028

Incorporating endorectal MR elastography into multi‐parametric MRI for prostate cancer imaging: Initial feasibility in volunteers

2013· article· en· W1923837660 on OpenAlexafffund
Arvin Arani, Michael Da Rosa, Elizabeth Ramsay, Don B. Plewes, Masoom A. Haider, Rajiv Chopra

Bibliographic record

VenueJournal of Magnetic Resonance Imaging · 2013
Typearticle
Languageen
FieldMedicine
TopicUltrasound Imaging and Elastography
Canadian institutionsUniversity Health NetworkUniversity of TorontoSunnybrook Health Science Centre
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchMinistry of Education, India
KeywordsProstate cancerMedicineProstateElastographyTolerabilityMagnetic resonance elastographyMagnetic resonance imagingRadiologyNuclear medicineCancerBiomedical engineeringUltrasoundPathologyInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE: To investigate the tolerability and technical feasibility of performing endorectal MR elastography (eMRE) in human volunteers within the representative age group commonly affected by prostate cancer. MATERIALS AND METHODS: Endorectal MRE was conducted on seven volunteers in a 1.5 Tesla (T) MR imager using a rigid endorectal coil. Another five volunteers were imaged on a 3T MR imager using an inflatable balloon type endorectal coil. Tolerability was accessed for vibration amplitudes of ±1-50 μm and for frequencies of 100-300 Hz. RESULTS: All 12 volunteers tolerated the displacements necessary to successfully perform eMRE. Shear waves with frequencies up to 300 Hz could propagate across the entire prostate using both coil designs. CONCLUSION: The results of this study motivate further investigation of eMRE in prostate cancer patients to help determine if there is an added value of integrating eMRE into existing multi-parametric prostate MRI exams.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.153
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.012
GPT teacher head0.293
Teacher spread0.281 · 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.

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

Citations32
Published2013
Admission routes2
Has abstractyes

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