Parenchymal sparing liver resection for cytoreduction of neuroendocrine tumors metastases
Bibliographic record
Abstract
Background Due to unique indolent biology, neuroendocrine tumours (NETs) can be managed for many years with prolonged survival. Goals of NETs therapy differ from other more common solid malignancies. Cytoreductive surgery plays an important role in the multidisciplinary management of NETs. It offers an opportunity to reduce both tumor burden and hormonal load to improve symptom-free survival and quality of life, and spare systemic therapy options. Parenchyma-sparing liver cytoreduction is recommended technique to preserve liver parenchyma for future treatments upon progression or recurrence. Methods This video reviews parenchyma-sparing cytoreductive surgery for NETs liver metastases. Approaches and techniques, and their rationale are reviewed. Results We focus on the management of hepatic metastases in well differentiated low grade intestinal neuroendocrine neoplasm (or NET). The video reviews the steps of parenchyma-sparing liver metastases with enucleation for NETs. Considering the goal for cytoreduction for an indolent disease, wide margins are not aimed for. We highlight the technical aspects of enucleations to avoid anatomical resection and preserve parenchyma, which is critical in minimizing morbidity and optimizing long-term sequencing of therapies for a chronic malignancy. Conclusion We herein illustrate the steps and rationale for hepatic parenchyma-sparing cytoreduction for metastatic NETs. This approach can lead to significant tumoral and hormonal control, with favorable long-term outcomes. Parenchyma-sparing resection should be used over anatomical resection.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".