Preoperative neoadjuvant targeted therapy with lenvatinib for inoperable thyroid cancer
Bibliographic record
Abstract
Rationale: Neoadjuvant chemotherapy (NAC) with lenvatinib for inoperable thyroid cancer has not been established. We have previously reported a case of NAC in 2020. Here, we report 5 cases of thyroid cancer treated with NAC from 2019 to 2021, including 3 cases of inoperable differentiated thyroid cancer, 1 case of anaplastic thyroid cancer, and 1 case of concomitant differentiated thyroid cancer and anaplastic thyroid cancer. Patient concerns and diagnosis: Four patients were pathologically diagnosed with thyroid cancer by cytology or biopsy, and 1 patient was diagnosed with follicular neoplasm. These patients had pleural, laryngeal, and esophageal invasion, and radical surgery would have required an extended operation and increased surgical risks. Interventions: Five patients with thyroid cancer displaying invasion of adjacent organs who were inoperable were preoperatively treated with lenvatinib alone; they underwent surgery when the tumor stopped shrinking. Outcomes: The best response was achieved after 1.7 to 4.7 months (average 2.9 months) of lenvatinib treatment, and the dose was reduced due to the occurrence of adverse events. Surgery was successfully performed when the tumor stopped shrinking. Conclusion: The prognosis of patients with inoperable thyroid cancer is poor; however, patients can undergo surgery safely after NAC treatment with lenvatinib. We hope that NAC will be considered as a treatment option for advanced thyroid cancers and serve as a precedent for safe treatment. This can be a new treatment option to eliminate unresectable thyroid cancers.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".