Emerging Therapeutics for Radioiodide-Refractory Thyroid Cancer
Bibliographic record
Abstract
Although uncommon, thyroid cancer constitutes the main endocrine neoplasia with an incidence rate that has been increasing steadily over the past decades. Recently, remarkable advances have occurred in understanding the biology of thyroid cancer. Novel germline and somatic point mutations as well as somatic chromosomal rearrangements associated with thyroid carcinogenesis have been discovered. Strikingly, acquired knowledge in the genetics of thyroid cancer has been translated into clinical practice, offering better diagnostic and prognostic accuracy and enabling the development of novel compounds for the treatment of advanced thyroid carcinomas. Even after 70 years, radioiodide therapy remains as the central treatment for advanced or metastatic differentiated thyroid cancer. However, the mechanisms leading to reduced radioiodide accumulation in the tumor cell remain partially understood. Radioiodide-refractory thyroid cancer metastasis constitutes a central problem in the management of thyroid cancer patients. In recent years, the antiangiogenic tyrosine kinase inhibitors sorafenib and lenvatinib have been approved for the treatment of advanced radioiodide-refractory thyroid carcinoma. Moreover, still on clinical phase of study, oncogene-specific and oncogene-activated signaling inhibitors have shown promising effects in recovering radioiodide accumulation in radioiodide-refractory thyroid cancer metastasis. Further clinical trials of these therapeutic agents may soon change the management of thyroid cancer. This review summarizes the latest advances in the understanding of the molecular basis of thyroid cancer, the mechanisms leading to reduced radioiodide accumulation in thyroid tumors and the results of clinical trials assessing emerging therapeutics for radioiodide-refractory thyroid carcinomas in the era of targeted therapies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".