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Record W4408957611 · doi:10.1002/pros.24893

Developments in Ultrasound‐Based Imaging for Prostate Cancer Detection

2025· review· en· W4408957611 on OpenAlexaff
Reid Vassallo, Miles P. Mannas, Septimiu E. Salcudean, Peter Black

Bibliographic record

VenueThe Prostate · 2025
Typereview
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsProstate cancerMedicineProstateCancer detectionUltrasoundProstate diseaseCancerRadiologyGynecologyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Prostate cancer is a significant health issue worldwide, but methods to screen for and diagnose this disease have significant inherent limitations. Some efforts to address these limitations have involved the use of ultrasound-based imaging methods. METHODS: This narrative review paper focuses on recent developments in the use of medical imaging, with a focus on ultrasound and related methods, to improve the diagnosis of prostate cancer. These methods include: elastography, contrast-enhanced ultrasound, targeted contrast agents, quantitative ultrasound, multiparametric ultrasound, micro-ultrasound, and photoacoustic imaging. RESULTS: This paper provides an update on clinically relevant imaging technologies which are in the technical and preclinical literature. CONCLUSION: Novel methods and their performance are highlighted, including how they address limitations in current clinical care.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.995
Threshold uncertainty score0.937

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.015
GPT teacher head0.289
Teacher spread0.274 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations4
Published2025
Admission routes1
Has abstractyes

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