Second-line treatment in patients with advanced extra-pulmonary poorly differentiated neuroendocrine carcinoma: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: There is no standard second-line treatment for patients with advanced extra-pulmonary poorly differentiated neuroendocrine carcinoma (EP-PD-NEC). This study explored data evaluating second-line treatment in these patients. METHODS: A search of MEDLINE and EMBASE identified studies reporting survival and/or response data for patients with EP-PD-NEC receiving second-line therapy. Association between various factors (age, gender, ECOG performance status, primary tumour location, morphology, Ki-67, treatment and grade 3/4 haematological toxicity) and response rate (RR), progression-free (PFS) and overall survival (OS) were assessed with a mixed effects meta-regression weighted by individual study sample size. Due to a small sample size, associations were reported quantitatively, based on magnitude of beta coefficient rather than statistical significance. RESULTS: Of 83 identified studies, 19 were eligible, including 4 prospective and 15 retrospective studies. Analysis comprised 582 patients, with a median number of 19 patients in each study (range 5-100). Median age was 59 years (range 53-66). Median RR was 18% (range 0-50; 0% for single-agent everolimus, temozolomide, topotecan; 50% with amrubicin), median PFS was 2.5 months (range 1.15-6.0) and median OS was 7.64 months (range 3.2-22.0). Studies with a higher proportion of patients with a Ki-67>55% had lower RR (β = -0.73) and shorter OS (β = -0.82). CONCLUSION: Second-line therapy for patients with advanced EP-PD-NEC has limited efficacy and the variety of regimens used is diverse. Ki-67>55% is associated with worse outcomes. Prospective randomised studies are warranted to enable exploration of new treatment strategies.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.025 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".