Survival predictors of 177Lu-Dotatate peptide receptor radionuclide therapy (PRRT) in patients with progressive well-differentiated neuroendocrine tumors (NETS)
Bibliographic record
Abstract
Abstract Purpose 177Lu-Dotatate is an emerging treatment modality for patients with unresectable or metastatic well-differentiated NETs. This study examines survival predictors in patients who received 177Lu-Dotatate. Methods A retrospective single-center review was conducted, examining 47 individuals with progressive well-differentiated NETs treated with 177Lu-Dotatate (four induction cycles of 5.5 GBq at 10-week intervals followed by eight maintenance cycles of 3.7 GBq at 6-month intervals). Results Median follow-up was 63.1 months with a median progression-free survival (PFS) of 34.1 months. However, median overall survival (OS) was not reached at the time of analysis. The presence of ≥ 5 bone metastases (hazard ratio HR 4.33; p = 0.015), non-gastroenteropancreatic (non-GEP) NETs (HR 3.22; p = 0.025) and development of interim ascites (HR 3.15; p = 0.047) independently predicted a worse OS. Patients with chromogranin A of ≥ 4 × upper limit of normal (ULN) had shorter OS (p < 0.001) and PFS (p = 0.004). Similarly, those with pre-existing ascites demonstrated a worse OS (p = 0.009) and PFS (p = 0.026). Liver metastases involving greater than 50% liver volume and the existence of unusual metastatic locations had a negative impact on OS (p = 0.033) and PFS (p = 0.026), respectively. Conclusion High burden of skeletal and hepatic metastases, non-GEP-NETs, chromogranin A of ≥ 4 × ULN, unusual metastatic sites, pre-existing and interim ascites are predictors of poor outcomes in patients treated with 177Lu-Dotatate. These common indicators can be used for the risk stratification and identification of patients most likely to benefit from PRRT. Trial registration ClinicalTrials.gov identifier: NCT02236910, Retrospectively registered on September, 2014.
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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".