A Systematic Review of Surgical Management Strategies in the Treatment of Peritoneal Carcinomatosis of Neuroendocrine Origin
Bibliographic record
Abstract
Cytoreductive surgery (CRS) represents the cornerstone of surgical management for peritoneal carcinomatosis (PC) and involves peritonectomy procedures aimed at complete peritoneal tumour resection. Frequently, CRS is combined with hyperthermic intraperitoneal chemotherapy (HIPEC). The combination of CRS + HIPEC is now considered the standard of care in patients with colorectal and ovarian PC. However, the role of this multi-modality treatment approach in patients with PC of neuroendocrine tumour origin (NET-PC) is less well understood. This systematic review provides a summary of available evidence on management strategies for patients with NET-PC. A systematic literature search was performed using Ovid Medline, EMBASE and Cochrane Library databases to identify studies reporting outcomes for patients with NET-PC undergoing surgical treatment. Eligible studies were assessed for methodological quality and design and evaluated for a method of surgical treatment, method of HIPEC delivery, oncological outcomes, and treatment-related morbidity. Eight studies, including a total of 1240 patients with NET-PC, met predefined inclusion criteria and have been included in this review. In three of the included studies, CRS alone was performed for patients with NET-PC, while five studies reported outcomes with combined treatment using CRS plus HIPEC. All studies were performed at tertiary peritoneal malignancy centres. Only one study directly compared outcomes in patients with NET-PC undergoing CRS plus HIPEC compared with CRS in isolation, with no significant difference in overall survival reported. Carefully selected patients with NET-PC may benefit from aggressive surgical treatment in the form of CRS +/- HIPEC. These procedures are best undertaken at centres with expertise in the management of both neuroendocrine tumours and peritoneal malignancy, as both are conditions that require tertiary-level care. The additional benefit of the HIPEC component in this group of patients remains unclear and warrants further investigation in clinical trials. Overall, the quality of data on this subject is restricted by the low number of studies and the variability in treatment methods employed. A multi-national data registry for patients with NET-PC may offer the opportunity to better define treatment algorithms. Translational research efforts in parallel should focus on developing a better biological understanding of NET-PC, with a view to identifying more effective intraperitoneal cytocidal agents.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".