Perioperative outcomes and long-term survival of cryosurgery on unresectable pancreatic cancer: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Pancreatic cancer (PC) continues to rank as one of the deadliest forms of cancer, with the majority of patients being diagnosed with unresectable disease. The implementation of cryoablation-based comprehensive treatment is expected to lead to a reduction in tumour size and potentially achieve complete resection. However, the availability of systematic and evidence-based treatment guidelines for cryoablation in pancreatic cancer is currently limited. MATERIALS AND METHODS: A computerized search was conducted in the Medline, Embase, Cochrane Library databases, and Clinicaltrials to retrieve studies published from August 1987 to June 2023. The perioperative outcomes and long-term survival of patients with locally advanced pancreatic cancer (LAPC) or metastatic pancreatic cancer (mPC) who underwent cryosurgery were systematically collected. Heterogeneity was assessed using Cochran's Q test, and a funnel plot was implemented to evaluate publication bias. Data were pooled and analyzed using Stata 14.0 software. The Newcastle-Ottawa Quality Assessment Form tool was employed for quality evaluation. RESULTS: Nine studies, including 368 patients, were identified. The pooled overall incidence rate was 4% (95% CI: 0-14%; I²=54.06%) for acute pancreatitis, 0% (95% CI: 0-0%; I²=0.0%) for delayed gastric emptying, 0% (95% CI: 0-1%; I²=0.0%) for postoperative pancreatic fistula (POPF) or bile leakage, 2% (95% CI: 0-5%; I²=0.0%) for bleeding, and 4% (95% CI: 0-12%; I²=70.46%) for fever. The subgroup analysis revealed that the percutaneous treatment group had a lower incidence of complications compared to laparotomy but had higher incidences of POPF, bleeding, and pancreatitis than the intraoperative group. Median overall survival (OS) varied from 5 to 16.2 months. The median survival rates at 6, 12, 24, and 36 months for patients who underwent cryosurgery were as follows: 94.9%, 64.8% (range: 4.8-63.1%), 22.80%, and 9.5%, respectively. CONCLUSION: With the advancements in cryoablation and anti-tumour therapy, cryoablation has emerged as a promising treatment modality for patients with unresectable pancreatic cancer. According to existing literature, the combination of cryoablation with radioactive particle implantation or immunotherapy has demonstrated considerable efficacy and safety. However, while the majority of included data is derived from retrospective studies, it is imperative to conduct larger sample sizes or prospective trials for further validation.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.016 | 0.004 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| 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 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".