Search

BackgroundPancreatic ductal adenocarcinoma (PDAC) is frequently diagnosed in advanced stages, necessitating pancreaticoduodenectomy (PD) as a primary therapeutic approach. However, PD surgery can engender intricate complications. Thus, understanding the factors influencing postoperative complications documented in electronic medical records and their impact on survival rates is crucial for improving overall patient outcomes.MethodsA total of 749 patients were divided into two groups: 598 (79.84%) chose the RPD (Robotic pancreaticoduodenectomy) procedure and 151 (20.16%) chose the LPD (Laparoscopic pancreaticoduodenectomy) procedure. We used correlation analysis, survival analysis, and decision tree models to find the similarities and differences about postoperative complications and prognostic survival.ResultsPancreatic cancer, known for its aggressiveness, often requires pancreaticoduodenectomy as an effective treatment. In predictive models, both BMI and surgery duration weigh heavily. Lower BMI correlates with longer survival, while patients with heart disease and diabetes have lower survival rates. Complications like delayed gastric emptying, pancreatic fistula, and infection are closely linked post-surgery, prompting conjectures about their causal mechanisms. Interestingly, we found no significant correlation between nasogastric tube removal timing and delayed gastric emptying, suggesting its prompt removal post-decompression.ConclusionThis study aimed to explore predictive factors for postoperative complications and survival in PD patients. Effective predictive models enable early identification of high-risk individuals, allowing timely interventions. Higher BMI, heart disease, or diabetes significantly reduce survival rates in pancreatic cancer patients post-PD. Additionally, there’s no significant correlation between DGE incidence and postoperative extubation time, necessitating further investigation into its interaction with pancreatic fistula and infection.

Cancer metabolism has emerged as a pivotal area of research recently. The ability to visualize and comprehend the metabolic processes of cancer holds immense clinical value, particularly in the diagnosis of malignant tumors and the assessment of treatment responses. Deuterium Metabolic Imaging (DMI), as a robust, simple, and versatile MR spectroscopic imaging tool, demonstrates promise in tumor diagnosis and treatment efficacy assessment. This review explored the latest developments and applications of DMI in oncology across various tumor metabolic axes, with a specific emphasis on its potential for clinical translation. DMI offers invaluable insights into tumor biology, treatment responses, and prognostic outcomes. Notably, DMI can identify early responses to immunotherapy, a prominent area of current research interest. In conclusion, DMI harbors the potential to evolve into a convenient and efficient imaging technique in clinical practice, thereby advancing precision medicine and improving the diagnosis and evaluation of cancer treatments.