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Sequential TKI treatments for iodine-refractory differentiated thyroid carcinomas.

2013· article· en· W2604916132 on OpenAlexaff
Christelle de la Fouchardière, Marie-Hélène Massicotte, Isabelle Borget, Maryse Brassard, M. Claude-Desroches, Anne‐Laure Giraudet, Christine Do Cao, F. Bonichon, C. Chougnet, Sophie Leboulleux, Éric Baudin, Martin Schlumberger

Bibliographic record

VenueJournal of Clinical Oncology · 2013
Typearticle
Languageen
FieldMedicine
TopicThyroid Cancer Diagnosis and Treatment
Canadian institutionsInstitut universitaire de cardiologie et de pneumologie de QuébecUniversité LavalCentre hospitalier universitaire de Québec
Fundersnot available
KeywordsMedicineSorafenibSunitinibInternal medicineRefractory (planetary science)Clinical endpointOncologyVandetanibProgression-free survivalTyrosine-kinase inhibitorGastroenterologyClinical trialCancerOverall survivalHepatocellular carcinoma

Abstract

fetched live from OpenAlex

6092 Background: Tyrosine kinase inhibitors (TKI) are currently used to treat patients with advanced iodine-refractory differentiated thyroid cancers (DTC) but none has been approved by the FDA or the EMA until now. Sometimes, patients are treated with off-label TKI when a clinical trial is not available or in second- and third-line therapy. Methods: We hereby report the efficacy of “off-label” sorafenib and sunitinib treatments as first-, second- and third-line therapy in metastatic DTC patients from the French TUTHYREF (TUmeurs THYroïdiennes REFractaires) network. Primary endpoints were progression free survival (PFS) and tumor response according to sequential TKI treatment. Secondary endpoint was organ-specific metastatic site analysis. Results: 45 patients with advanced iodine-refractory DTC treated with off-label TKI were included in this study (26 men, mean age: 62 years). 22 had papillary, 10 had follicular and 13 had poorly DTC. 24/45 patients were treated with two and 3/45 with three lines of TKIs. Sorafenib was the most frequently used (57%) followed by sunitinib (21.5%) and vandetanib (21.5%). Partial response (PR) rate was of 29% in the 21 patients who received first-line sorafenib therapy whereas PR was observed in 57% of the 7 first-line sunitinib patients. There was no PR with second- (n=24) and third-line (n=3) treatments. However, median progression free survival (PFS) was similar in second- as compared to first-line sorafenib or sunitinib treatment (6.7 vs. 7.6 months, HR 0.85 (95CI 0.45-1.61) p=0.6). Liver metastases were the most responsive to treatment (n=7; mean of -30%), followed by lung (n=57; mean of -19%) and lymph node (n=43; mean of -13%) metastases. Bone (n=14) and pleural (n=9) lesions were the most refractory to treatment (mean of -1% and -5%, respectively). Conclusions: Due to the small number of patients, we could not recommend a specific treatment sequence (sorafenib then sunitinib) over another (sunitinib then sorafenib). But TKI therapy appears to be beneficial in refractory DTC patients even in second- and third-line therapy, with similar PFS and stable disease as best response. Bone and pleural metastases were the most refractory and liver lesions the most responsive to treatment.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.151
GPT teacher head0.456
Teacher spread0.306 · 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 designObservational
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".

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Citations4
Published2013
Admission routes1
Has abstractyes

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