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Positron Emission Tomography Imaging of Tumor Response after Photodynamic Therapy

2006· review· en· W2006290407 on OpenAlexaff
Véronique Bérard, Roger Lecomte, Johan E. van Lier

Bibliographic record

VenueJournal of Environmental Pathology Toxicology and Oncology · 2006
Typereview
Languageen
FieldMedicine
TopicPhotodynamic Therapy Research Studies
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsPhotodynamic therapyPositron emission tomographyMolecular imagingMedicineMedical physicsCancer therapyCancer treatmentPreclinical imagingCancerIn vivoNuclear medicineInternal medicineChemistry

Abstract

fetched live from OpenAlex

Positron emission tomography (PET) imaging is a powerful noninvasive tool allowing physiological and biochemical processes to be investigated in vivo at the molecular level. In the clinics, it is currently being used to detect and stage cancer and to assess tumor response following therapy. In cancer research, at the preclinical level, PET in conjunction with a dedicated high-resolution small animal scanner can play an important role in drug development as well as in the evaluation of novel treatment protocols. In this paper, we review the use of PET in assessing tumor response to photodynamic therapy (PDT) and discuss its potential role in the development of novel photosensitizers. This molecular imaging modality is particularly promising for the real-time evaluation of tumor response to therapy both in terms of treatment efficacy and action mechanism.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.769
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.013
GPT teacher head0.336
Teacher spread0.322 · 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 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

Citations9
Published2006
Admission routes1
Has abstractyes

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