Tyrosine kinase inhibitor treatments in patients with metastatic thyroid carcinomas: a retrospective study of the TUTHYREF network
Bibliographic record
Abstract
OBJECTIVE: Tyrosine kinase inhibitors (TKIs) are used to treat patients with advanced thyroid cancers. We retrospectively investigated the efficacy of TKIs administered outside of clinical trials in metastatic sites or locally advanced thyroid cancer patients from five French oncology centers. DESIGN AND METHODS: THERE WERE 62 PATIENTS (37 MEN, MEAN AGE: 61 years) treated with sorafenib (62%), sunitinib (22%), and vandetanib (16%) outside of clinical trials; 22 had papillary, five had follicular, five had Hürthle cell, 13 had poorly differentiated, and 17 had medullary thyroid carcinoma (MTC). Thirty-three, 25, and four patients were treated with one, two, and three lines of TKIs respectively. Primary endpoints were objective tumor response rate and progression-free survival (PFS). Sequential treatments and tumor response according to metastatic sites were secondary endpoints. RESULTS: Among the 39 sorafenib and 12 sunitinib treatments in differentiated thyroid carcinoma (DTC) patients, partial response (PR) rate was 15 and 8% respectively. In the 11 MTC patients treated with vandetanib, 36% had PR. Median PFS was similar in second-line compared with first-line sorafenib or sunitinib therapy (6.7 vs 7.0 months) in DTC patients, but there was no PR with second- and third-line treatments. Bone and pleural lesions were the most refractory sites to treatment. CONCLUSIONS: This is the largest retrospective study evaluating TKI therapies outside of clinical trials. DTC patients treated with second-line therapy had stable disease as best response, but had a similar median PFS compared with the first-line treatment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".