Efficacy of 177Lu-Dotatate Induction and Maintenance Therapy of Various Types of Neuroendocrine Tumors: A Phase II Registry Study
Bibliographic record
Abstract
Peptide receptor radionuclide therapy (PRRT) has been recently established as a treatment option for progressive gastro-entero-pancreatic neuroendocrine tumors (NETs) including four 200 mCi induction cycles. The purpose of this phase 2 trial is to expand use of PRRT to different types of NETs with the application of dose adjustment and evaluate value of maintenance therapy in patients who had disease control on induction therapy. Forty-seven PRRT naïve NET patients with different primary origin received 177Lu-DOTATATE induction therapy, ranging from 75 to 150 mCi per cycle, based on patients’ clinical status such as liver and renal function, extent of metastases, and previous therapies. Thirty-four patients underwent additional maintenance therapy (50–100 mCi per cycle) following induction course until they developed disease progression. The estimated median progression-free survival (PFS) was 36.1 months. The median PFS in our MNET subgroup was 47.7 months, which is markedly longer than NETTER-1 trial with median PFS of 28.4 months. The median PFS was significantly longer in patients who received PRRT as first-line treatment after disease progression on somatostatin analogs compared to patients who received other therapies first (p-value = 0.04). The total disease response rate (DRR) and disease control rate (DCR) was 32% and 85% based on RECIST 1.1 and 45% and 83% based on Choi criteria. This trial demonstrates longer PFS with the addition of low dose maintenance therapy to induction therapy compared to NETTER-1 trial that only included induction therapy. Also, we observed considerable efficacy of PRRT in various types of advanced NETs.
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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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".