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Record W2154887990 · doi:10.1117/1.jbo.19.5.056008

Optoacoustic characterization of prostate cancer in an <i>in vivo</i> transgenic murine model

2014· article· en· W2154887990 on OpenAlexafffund
Michelle P. Patterson, Christopher B. Riley, Michael C. Kolios, William M. Whelan

Bibliographic record

VenueJournal of Biomedical Optics · 2014
Typearticle
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsToronto Metropolitan UniversityUniversity of Prince Edward Island
FundersAtlantic Veterinary CollegeNatural Sciences and Engineering Research Council of CanadaAtlantic Canada Opportunities AgencyCanada Research Chairs
KeywordsProstate cancerProstateIn vivoMedicinePathologyPreclinical imagingCancerNuclear medicineBiomedical engineeringBiologyInternal medicine

Abstract

fetched live from OpenAlex

Optoacoustic (OA) imaging was employed to distinguish normal from neoplastic tissues in a transgenic murine model of prostate cancer. OA images of five tumor-bearing mice and five age-matched controls across a 14 mm × 14 mm region of interest (ROI) on the lower abdomen were acquired using a reverse-mode OA imaging system (Seno Medical Instruments Inc., San Antonio, Texas). Neoplastic prostate tissue was identified based on the OA signal amplitude in combination with spectral analysis of the OA radio frequency (RF) data. Integration of the signal amplitude images was performed to construct two-dimensional images of the ROI. The prostate tumors generated higher amplitude signals than those of the surrounding tissues, with contrast ratios ranging from 31 to 36 dB. The RF spectrum analysis showed significant differences between the tumor and the control mice. The midband fit was higher by 5 dB (62%), the intercept higher by 4 dB (57%) and the spectral slope higher by 0.4 dB/MHz (50%) for neoplastic prostate tissue compared to normal tissues in the control mice. The results demonstrate that OA offers high contrast imaging of prostate cancer in vivo.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.794
Threshold uncertainty score0.379

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 head0.006
GPT teacher head0.230
Teacher spread0.224 · 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 designSimulation or modeling
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

Citations25
Published2014
Admission routes2
Has abstractyes

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