Efficacy of 177Lu Peptide Receptor Radionuclide Therapy for the Treatment of Neuroendocrine Tumors
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to assess the efficacy of Lu-labeled peptide receptor radionuclide therapy (PRRT) induction treatments for patients with unresectable metastatic neuroendocrine tumors. METHODS: MEDLINE, EMBASE, and Ovid were systematically searched with keywords "lutetium," "Lu-177," "PRRT," "neuroendocrine," and "prognosis." Studies evaluating treatment with Lu-labeled PRRT were assessed for disease response and/or disease control rate by Response Evaluation Criteria in Solid Tumors (RECIST) 1.0 or 1.1, modified RECIST, Southwest Oncology Group (SWOG), or modified SWOG criteria. Pooled proportions of disease response and control rates were calculated for both fixed- and random-effects models. RESULTS: Eighteen studies with 1920 patients were included (11 with 1268 patients using RECIST and 6 with 804 patients using SWOG). By RECIST criteria, the pooled disease response rate by random-effects model was 29.1% (95% confidence interval [CI], 20.2%-38.9%), and disease control rate was 74.1% (95% CI, 67.8%-80.0%). By SWOG criteria, the pooled disease response rate by random-effects model was 30.6% (95% CI, 20.7%-41.5%), and disease control rate was 81.1% (95% CI, 76.4%-85.4%). CONCLUSIONS: Induction therapy, typically 4 treatments, with Lu PRRT is an effective method of treating unresectable metastatic neuroendocrine tumors with significant disease response and control rates.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".