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Record W4407161206 · doi:10.1093/bjr/tqaf020

Clinical and research applications of fibroblast activation protein-α inhibitor tracers: a review

2025· review· en· W4407161206 on OpenAlexaff
Mélanie Desaulniers, Étienne Rousseau, Kim M. Pabst

Bibliographic record

VenueBritish Journal of Radiology · 2025
Typereview
Languageen
FieldMedicine
TopicPeptidase Inhibition and Analysis
Canadian institutionsCentre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-JeanCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalCentre Hospitalier Universitaire de SherbrookeUniversité de Sherbrooke
Fundersnot available
KeywordsFibroblast activation protein, alphaMedicineNuclear medicineLesionClinical trialRadioligandPET-CTRadiologyCancer researchCancerPathologyPositron emission tomographyInternal medicine

Abstract

fetched live from OpenAlex

In the last decade, fibroblast activation protein-α inhibitors (FAPIs), which target the cancer-associated fibroblasts of the tumour microenvironment, have become a topic of great interest. In oncology, FAPI PET/CT imaging has repeatedly demonstrated a higher lesion detection rate than other conventional imaging modalities such as CT or 18F-FDG PET/CT for several tumours. In some cases, the initial staging and therapeutic management may even change. Some FAPI radioligands may also be labelled with therapeutic radionuclides for theranostic applications. It is thus possible to treat certain metastatic cancers with FAPI radioligand therapy (FAPI-RLT), which is generally well tolerated with little toxicity. Recently, new FAPIs have been developed with the particularity of having a higher binding affinity for the target, which further improves the lesion detection rate on PET/CT and clinical outcomes following FAPI-RLT. This review provides recent updates in the clinical use of FAPI PET/CT and FAPI-RLT and discusses potential emergent applications, including in inflammation imaging.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.895
Threshold uncertainty score0.636

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.078
GPT teacher head0.454
Teacher spread0.376 · 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 designOther design
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

Citations13
Published2025
Admission routes1
Has abstractyes

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