RESUMEN
BACKGROUND: Hiatal and paraesophageal hernia (HH/PEH) recurrence is the most common cause of failure after gastroesophageal anti-reflux surgery. Crural reinforcement with mesh has been suggested to address this issue, but its efficacy remains debated. In this study, we aimed to determine the impact of biosynthetic mesh reinforcement compared to suture cruroplasty on anatomic and symptomatic hernia recurrence. METHOD: Data of patients who underwent robotic HH/PEH repair with suture cruroplasty with or without biosynthetic mesh reinforcement between January 2012 and April 2024 were retrospectively reviewed. Gastroesophageal reflux disease symptoms and anatomic hernia recurrence were assessed at short-term (3 months to 1 year) and longer-term (≥ 1 year) follow-up. Symptomatic hernia recurrence was defined as having both anatomic recurrence and symptoms. RESULTS: Out of the 503 patients in the study, 308 had undergone biosynthetic mesh repair, while 195 had suture-only repair. After the surgery, both groups demonstrated comparable improvements in symptoms. Short-term anatomic hernia recurrence rates were 11.8% and 15.6% for mesh and suture groups, respectively (p = 0.609), while longer-term rates were 24.7% and 44.9% (p = 0.015). The rates of symptomatic hernia recurrence in the same group were 8.8% and 14.6% in the short-term (p = 0.256), and 17.2% and 42.2% in longer-term follow-ups (p = 0.003). In the repair of medium and large-size hernias, mesh reinforcement resulted in a 50.0% relative risk reduction in anatomic hernia recurrences and a 59.2% reduction in symptomatic hernia recurrences at ≥ 1-year follow-up. CONCLUSION: After more than a year of follow-up, it has been found that using biosynthetic mesh for medium and large hiatal or paraesophageal hernia repair significantly reduces the likelihood of both anatomic and symptomatic recurrence compared to using only suture cruroplasty. These findings strongly support the use of biosynthetic mesh to manage larger hernias. However, further long-term multicenter randomized studies are needed to provide more conclusive evidence.
RESUMEN
CONTEXT: Pancreatic neuroendocrine tumors (PNETs) exhibit a wide range of behavior from localized disease to aggressive metastasis. A comprehensive transcriptomic profile capable of differentiating between these phenotypes remains elusive. OBJECTIVE: Use machine learning to develop predictive models of PNET metastatic potential dependent upon transcriptomic signature. METHODS: RNA-sequencing data were analyzed from 95 surgically-resected primary PNETs in an international cohort. Two cohorts were generated with equally balanced metastatic PNET composition. Machine learning was used to create predictive models distinguishing between localized and metastatic tumors. Models were validated on an independent cohort of 29 formalin-fixed, paraffin-embedded samples using NanoString nCounter®, a clinically-available mRNA quantification platform. RESULTS: Gene expression analysis identified concordant differentially expressed genes between the two cohorts. Gene set enrichment analysis identified additional genes that contributed to enriched biologic pathways in metastatic PNETs. Expression values for these genes were combined with an additional 7 genes known to contribute to PNET oncogenesis and prognosis, including ARX and PDX1. Eight specific genes (AURKA, CDCA8, CPB2, MYT1L, NDC80, PAPPA2, SFMBT1, ZPLD1) were identified as sufficient to classify the metastatic status with high sensitivity (87.5% - 93.8%) and specificity (78.1% - 96.9%). These models remained predictive of the metastatic phenotype using NanoString nCounter® on the independent validation cohort, achieving a median AUROC of 0.886. CONCLUSIONS: We identified and validated an eight-gene panel predictive of the metastatic phenotype in PNETs, which can be detected using the clinically-available NanoString nCounter® system. This panel should be studied prospectively to determine its utility in guiding operative versus non-operative management.