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Record W4394962110

[A review on the application of image fusion technology in prostate cancer radiotherapy based on gold fiducial biomarker].

2023· article· en· W4394962110 on OpenAlexaff
Jiancheng Zhao, Long Tian

Bibliographic record

VenuePubMed · 2023
Typearticle
Languageen
FieldMedicine
TopicRadiomics and Machine Learning in Medical Imaging
Canadian institutionsNorthern College
Fundersnot available
KeywordsFiducial markerProstate cancerGold standard (test)Radiation therapyMedicineImage fusionImage-guided radiation therapyBiomarkerProstateMedical physicsCancerRadiologyOncologyNuclear medicineInternal medicineComputer scienceArtificial intelligenceImage (mathematics)Chemistry
DOInot available

Abstract

fetched live from OpenAlex

Image fusion technology had been widely applied in image guided radiotherapy (IGRT) for prostate cancer (PCa) based on the gold fiducial mark (GFM). Image fusion technology included the fusion of CT image, magnetic resonance image, and ultrasound image internally or externally. The application of image fusion technology had improved the identification accuracy of GFM and was helpful for the plan design of PCa radiotherapy. This article provided a systematic review of the application of fusion of various medical images in PCa IGRT in recent years. Among them, the application and result of image fusion technology in GFM identification and the impact on the plan design for PCa radiotherapy were emphasized. It hoped that this review could provide some theoretical reference for the deeper integration of image fusion technology with PCa IGRT.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0050.005
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.016
GPT teacher head0.307
Teacher spread0.291 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2023
Admission routes1
Has abstractyes

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