BRAF/MEK Inhibitors in Downstaging <i>BRAF</i><sup>V600E</sup> Mutated Papillary Thyroid Cancer to Allow Resection: Case Report and Literature Review
Bibliographic record
Abstract
Well-differentiated thyroid cancer is managed with surgical resection, and adjuvant radioactive iodine (RAI) treatment reserved for moderate to high-risk patients.However, some patients with locally advanced disease are not candidates for upfront surgical resection.Within this rare patient population, VEGF tyrosine kinase inhibitors (TKI) and BRAF inhibitors have shown to successfully control and even reduce the size of RAI resistant thyroid cancers.In this case report, we elucidate the efficacy of a tumor agnostic strategy in facilitating the surgical resection of a locally advanced papillary thyroid cancer (PTC) with carotid involvement.A 60-year-old female presented with a large right sided papillary thyroid carcinoma with a BRAF V600E mutation.Initial stability was achieved through the use of Lenvatinib and subsequent use of dabrafenib and trametinib resulted in significant partial response.Following the aforementioned treatments, the patient successfully advanced to definitive surgery and RAI.BRAF/MEK inhibitors can be used in the neo-adjuvant setting to ensure resection in patients with locally advanced/unresectable well-differentiated thyroid cancer.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".