Selumetinib Plus Adjuvant Radioactive Iodine in Patients With High-Risk Differentiated Thyroid Cancer: A Phase III, Randomized, Placebo-Controlled Trial (ASTRA)
Bibliographic record
Abstract
PURPOSE Selumetinib can increase radioactive iodine (RAI) avidity in RAI-refractory tumors. We investigated whether selumetinib plus adjuvant RAI improves complete remission (CR) rates in patients with differentiated thyroid cancer (DTC) at high risk of primary treatment failure versus RAI alone. METHODS ASTRA (ClinicalTrials.gov identifier: NCT01843062 ) is an international, phase III, randomized, placebo-controlled, double-blind trial. Patients with DTC at high risk of primary treatment failure (primary tumor > 4 cm; gross extrathyroidal extension outside the thyroid gland [T4 disease]; or N1a/N1b disease with ≥ 1 metastatic lymph node(s) ≥ 1 cm or ≥ 5 lymph nodes [any size]) were randomly assigned 2:1 to selumetinib 75 mg orally twice daily or placebo for approximately 5 weeks (no stratification). On treatment days 29-31, recombinant human thyroid-stimulating hormone (0.9 mg)–stimulated RAI ( 131 I; 100 mCi/3.7 GBq) was administered, followed by 5 days of selumetinib/placebo. The primary end point (CR rate 18 months after RAI) was assessed in the intention-to-treat population. RESULTS Four hundred patients were enrolled (August 27, 2013-March 23, 2016) and 233 randomly assigned (selumetinib, n = 155 [67%]; placebo, n = 78 [33%]). No statistically significant difference in CR rate 18 months after RAI was observed (selumetinib n = 62 [40%]; placebo n = 30 [38%]; odds ratio 1.07 [95% CI, 0.61 to 1.87]; P = .8205). Treatment-related grade ≥ 3 adverse events were reported in 25/154 patients (16%) with selumetinib and none with placebo. The most common adverse event with selumetinib was dermatitis acneiform (n = 11 [7%]). No treatment-related deaths were reported. CONCLUSION Postoperative pathologic risk stratification identified patients with DTC at high risk of primary treatment failure, although the addition of selumetinib to adjuvant RAI failed to improve the CR rate for these patients. Future strategies should focus on tumor genotype–tailored drug selection and maintaining drug dosing to optimize RAI efficacy.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".