Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
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Journal of robotic surgery
Robot-assisted versus laparoscopic distal pancreatectomy: an updated systematic review and meta-analysis including patient subgroups and meta-regression analyses
Robot-assisted distal pancreatectomy (RDP) was developed to overcome technical limitations of laparoscopic distal pancreatectomy (LDP), yet uncertainty persists regarding oncologic adequacy, learning-curve effects, and outcomes in high-risk subgroups. We synthesized current evidence to address these gaps. We systematically searched PubMed and EMBASE, in accordance with PRISMA guidelines, from inception to 2025 to identify comparative studies of RDP versus LDP. Using random-effects models, we calculated weighted mean differences (WMDs) for continuous outcomes and risk ratios (RRs) for dichotomous outcomes, and performed subgroup analyses, including pancreatic ductal adenocarcinoma (PDAC), along with meta-regression to explore heterogeneity sources. Sixty-four studies comprising 15,790 patients (5,723 RDP; 10,067 LDP; mean age 60.5 years; BMI 26.1 kg/m²) were included. RDP resulted in lower blood loss (WMD - 52.0 mL; p < 0.00001), fewer conversions (RR 0.49; p < 0.00001), and fewer unplanned splenectomies (RR 0.59; p < 0.0001). Operative time was longer (WMD + 24.06 min; p < 0.00001). Postoperative morbidity, POPF, PPH, infection, reintervention, and mortality were comparable. Length of stay was shorter with RDP (WMD - 0.57 days; p < 0.00001). Although lymph node yield appeared higher with LDP in the overall and PDAC cohorts, this difference was no longer significant in a sensitivity analysis, and R0 resection rates remained comparable. Costs were higher with RDP, with substantial heterogeneity. RDP and LDP demonstrate comparable safety and oncologic outcomes. RDP reduces blood loss, conversions, and splenectomy but increases operative time and cost. The operative time disadvantage likely reflects learning-curve. Selective use in high-risk and complex resections is supported; cost-effectiveness warrants further study.
15 May 2026
Read appraisal →Abdominal radiology (New York)
Deep learning-based combined noise reduction and contrast enhancement for post-neoadjuvant pancreatic cancer CT: does improved image quality translate to better resectability assessment?
PURPOSE: To evaluate whether deep learning-based combined noise reduction and contrast enhancement reconstruction (DLR) improves image quality and resectability prediction accuracy compared to conventional iterative reconstruction (IR) in post-neoadjuvant pancreatic cancer CT assessment. METHODS: This retrospective study included 114 patients with pancreatic cancer following neoadjuvant therapy. Contrast-enhanced CT images were reconstructed using conventional IR and vendor-neutral ClariACE. Three abdominal radiologists independently assessed image quality (based on 8 parameters: tumor conspicuity, tumor margin, image noise, sharpness of the main pancreatic duct, arterial depiction, venous depiction, plasticity and overall image quality) and determined tumor resectability with confidence levels. Quantitative analysis included aortic and portal venous attenuation measurements and pancreas-to-tumor contrast-to-noise ratio (CNR). Diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC) with DeLong's test. Sensitivity, specificity, accuracy, and reader confidence were compared using McNemar's test. RESULTS: DLR demonstrated significantly superior vessel enhancement (p < 0.001) and improved CNR (p < 0.001) versus conventional IR. Two readers consistently rated DLR images higher across all qualitative categories (p < 0.001) except plasticity, while the third reader favored DLR in five of eight parameters (p < 0.001 to 0.020). However, all readers noted increased artificial appearance in DLR images (p < 0.001). Despite image quality improvements, no significant differences were observed in resectability assessment accuracy (62.3%-65.8%), AUC values (0.485-0.520), or high-confidence diagnosis rates between reconstruction methods. CONCLUSION: Although deep learning-based combined noise reduction and contrast enhancement reconstruction significantly improved quantitative and subjective image quality metrics, it did not enhance diagnostic accuracy for predicting R0 resectability in post-neoadjuvant pancreatic cancer patients.
5 May 2026
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