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Record W1784214565 · doi:10.14740/jem.v5i4.300

18 F-Choline PET/CT as a New Tool for Functional Imaging of Non-Proliferating Secreting Neuroendocrine Tumors

2015· article· en· W1784214565 on OpenAlexvenueno aff
Bernies van der Hiel, Marcel P. M. Stokkel, Wieneke A. Buikhuisen, Hans Janßen, Marie‐Louise F. van Velthuysen, Robert J. Rhodius, Wouter V. Vogel

Bibliographic record

VenueJournal of Endocrinology and Metabolism · 2015
Typearticle
Languageen
FieldMedicine
TopicNeuroendocrine Tumor Research Advances
Canadian institutionsnot available
Fundersnot available
KeywordsCholineMedicineImmunostainingNeuroendocrine tumorsPET-CTPathologyNeuroendocrine differentiationProliferation indexCancerCancer researchProstate cancerPositron emission tomographyNuclear medicineInternal medicineImmunohistochemistry

Abstract

fetched live from OpenAlex

Choline is an essential component for the formation of new cell membrane and the current understanding is that increased choline uptake in cancer lesions is explained by - and roughly correlates with - cellular proliferation. 18 F-fluoromethylcholine, a radiolabeled PET-tracer, is increasingly used for the detection of proliferating cancers with PET/CT (choline PET), for example for restaging of recurrent prostate carcinoma. However, clinical findings have suggested that choline uptake may not always be related to proliferation. We present three cases with a carcinoid of the lung with high uptake of radiolabeled choline on PET/CT and a low Ki-67 proliferation index as demonstrated by immunostaining. Although the mechanism behind the enhanced uptake in well-differentiated neuroendocrine tumors is not yet fully understood, for clinical practice this finding means that not every highly choline-avid lesion represents an aggressive cancer; the diagnosis of a functional neuroendocrine tumor should be considered as well. J Endocrinol Metab. 2015;5(4):267-271 doi: http://dx.doi.org/10.14740/jem300w

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.545
Threshold uncertainty score0.866

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.005
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 integrity0.0000.001
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 head0.038
GPT teacher head0.342
Teacher spread0.304 · 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 designCase report
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

Citations8
Published2015
Admission routes1
Has abstractyes

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