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Record W1122404497 · doi:10.1089/thy.2013.0225

Differentiated Thyroid Cancer: A New Perspective with Radiolabeled Somatostatin Analogues for Imaging and Treatment of Patients

2013· article· en· W1122404497 on OpenAlexaff
Annibale Versari, Martina Sollini, Andrea Frasoldati, Alessandro Fraternali, Angelina Filice, Armando Froio, Mattia Asti, Federica Fioroni, Nadia Cremonini, Daniel Putzer, Paola Anna Erba

Bibliographic record

VenueThyroid · 2013
Typearticle
Languageen
FieldMedicine
TopicThyroid Cancer Diagnosis and Treatment
Canadian institutionsInstitute of Cancer Research
Fundersnot available
KeywordsMedicineSomatostatin receptorRadionuclide therapyNuclear medicinePositron emission tomographyThyroid cancerProgressive diseaseSomatostatinToxicityThyroidAdverse effectInternal medicineChemotherapy

Abstract

fetched live from OpenAlex

BACKGROUND: The expression of somatostatin receptors (SSTR) in thyroid cells may offer the possibility to identify metastatic lesions and to select patients for peptide receptor radionuclide therapy (PRRT). We investigated (68)Ga-DOTATOC positron emission tomography/computed tomography (PET/CT) to select patients with progressive differentiated thyroid cancer (DTC) for PRRT as well as treatment response and toxicity in treated patients. METHODS: We enrolled 41 patients with progressive radioiodine-negative DTC (24 women and 17 men; mean age=54.3 years, median=59 years, range=19-78 years). In all patients, [(18)F]FDG-PET/CT was performed to determine recurrent disease with enhanced glucose metabolism, and (68)Ga-DOTATOC PET/CT was used to identify SSTR expression. Dosimetric evaluation was performed with (111)In-DOTATOC scintigraphy. Eleven patients were treated with PRRT receiving a fractionated injection of 1.5-3.7 GBq (90)Y-DOTATOC/administration. Serial (68)Ga-DOTATOC PET/CT scans were performed in all treated patients to evaluate treatment response. Parameters provided by (68)Ga-DOTATOC PET/CT were analyzed as potential therapeutic predictors to differentiate responding from nonresponding. In all treated patients, adverse events and toxicity were recorded. RESULTS: (68)Ga-DOTATOC PET/CT were positive in 24/41 of radioiodine-negative DTC patients. Based on the high expression of SSTR detected by (68)Ga-DOTATOC PET/CT, 13 patients were suitable for PRRT. Two out of 13 patients were not treated due to the lack of fulfillment of other study inclusion criteria. PRRT induced disease control in 7/11 patients (two partial response and five stabilization) with a duration of response of 3.5-11.5 months. Objective response was associated with symptoms relief. Functional volume (FV) over time obtained by PET/CT was the only parameter demonstrating a significant difference between lesions responding and nonresponding to PRRT (p=0.001). Main PRRT adverse events were nausea, asthenia, and transient hematologic toxicity. One patient experienced permanent renal toxicity. CONCLUSIONS: In our series, SSTR imaging provided positive results in more than half of the cases with radioiodine-negative DTC, and about one third of patients were eligible for PRRT. (68)Ga-DOTATOC PET/CT seems a reliable tool both for patient selection and evaluation of treatment response. In our experience, FV determination over time seems to represent a reliable parameter to determine tumor response to PRRT, although further investigations are needed to better define its role.

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)0.004
metaresearch head score (Gemma)0.002
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 score0.004
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.260
Teacher spread0.249 · 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

Citations89
Published2013
Admission routes1
Has abstractyes

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