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Record W2069029133 · doi:10.1002/jmri.23885

Changes in apparent diffusion coefficient and T<sub>2</sub> relaxation during radiotherapy for prostate cancer

2012· article· en· W2069029133 on OpenAlexaff
Warren D. Foltz, Andy Wu, Peter Chung, Charles Catton, Andrew Bayley, Michael Milosevic, Robert G. Bristow, Padraig Warde, Anna Simeonov, David A. Jaffray, Masoom A. Haider, Cynthia Ménard

Bibliographic record

VenueJournal of Magnetic Resonance Imaging · 2012
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsMedicineProstate cancerEffective diffusion coefficientProstateNuclear medicineRadiation therapyRepeatabilityDiffusion MRIProspective cohort studyUrologyCancerRadiologyMagnetic resonance imagingInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE: To evaluate regional and temporal changes in apparent diffusion coefficient (ADC) and T2 relaxation during radiation therapy (RT) in patients with low and intermediate risk localized prostate cancer. MATERIALS AND METHODS: Seventeen patients enrolled on a prospective clinical trial where MRI was acquired every 2 weeks throughout eight weeks of image-guided prostate IMRT (78 Gy/39 fractions). ADC and T2 quantification used entire prostate, central gland, benign peripheral zone, and tumor-dense regions-of-interest, and mean values were evaluated for common response trends. RESULTS: Overall, the RT responses were greater than volunteer measurement repeatability, and week 6 appeared to be an optimum time-point for early detection. RT effects on the entire prostate were best detected using ADC (5-7% by week 2, P < 0.0125), effects on peripheral zone were best detected using T2 (19% reduction at week 6; P = 0.004) and effects on tumors were best detected using ADC (14% elevation at week 6; P = 0.004). CONCLUSION: ADC and T2 may be candidate biomarkers of early response to RT warranting further investigation against clinical outcomes.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.446
Threshold uncertainty score0.416

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 head0.010
GPT teacher head0.267
Teacher spread0.257 · 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 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

Citations83
Published2012
Admission routes1
Has abstractyes

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