A pilot study of everolimus and radiation for neuroendocrine liver metastases
Bibliographic record
Abstract
Liver metastases are common in patients with neuroendocrine tumours. For patients, management must balance disease control with consideration of toxicity, given limited treatment options. Everolimus has demonstrated effectiveness in neuroendocrine neoplasms. Given emerging data of a synergistic effect with radiation therapy, we evaluated combined everolimus and radiation for neuroendocrine liver metastases. This single-arm, single-centre prospective pilot study evaluated the safety and efficacy of combined everolimus and radiotherapy for well-differentiated neuroendocrine liver metastases. Patients with unresectable liver metastases received everolimus for 30 days, followed by concurrent everolimus and liver radiotherapy, then a further 14 days of everolimus. Tolerability was evaluated using the CTCAE v.4.03. Individual metastasis response rate and local control were measured by RECIST v1.1. Overall survival, progression-free survival and freedom from a change in systemic therapy were estimated by the Kaplan-Meier method. Forty metastases were treated in 14 patients. No grade 3 or higher toxicities were identified in the concurrent treatment phase; one patient developed grade 3 toxicity in the post-radiation phase. Overall response rate was 38%. One- and 2-year local control were 97% and 71%. Median progression-free survival was 12 months. One- and 2-year overall survival were 100% and 92%. In conclusion, combined everolimus and radiation are well-tolerated for neuroendocrine liver metastases and are associated with excellent local control. The approach of selective local ablation of oligometastatic or oligoprogressive disease warrants further evaluation in this patient population.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".