The Role of Laparoscopy and Ultrasonography in Pancreatic Head Carcinoma

Objective: The authors performed a prospective evaluation of staging laparoscopy with laparoscopic ultrasonography in predicting surgical resectability in patients with carcinomas of the pancreatic head and periampullary region

Minimally invasive and endoscopic versus open necrosectomy for necrotising pancreatitis: a pooled analysis of individual data for 1980 patients

Minimally invasive surgical necrosectomy and endoscopic necrosectomy, compared with open necrosectomy, might improve outcomes in necrotising pancreatitis, especially in critically ill patients. Evidence from large comparative studies is lacking. We combined original and newly collected data from 15 published and unpublished patient cohorts (51 hospitals; 8 countries) on pancreatic necrosectomy for necrotising pancreatitis. Death rates were compared in patients undergoing open necrosectomy versus minimally invasive surgical or endoscopic necrosectomy. To adjust for confounding and to study effect modification by clinical severity, we performed two types of analyses: logistic multivariable regression and propensity score matching with stratification according to predicted risk of death at baseline (low: <5%; intermediate: ≥5% to <15%; high: ≥15% to  <35%; and very high: ≥35%). Among 1980 patients with necrotising pancreatitis, 1167 underwent open necrosectomy and 813 underwent minimally invasive surgical (n=467) or endoscopic (n=346) necrosectomy. There was a lower risk of death for minimally invasive surgical necrosectomy (OR, 0.53; 95% CI 0.34 to 0.84; p=0.006) and endoscopic necrosectomy (OR, 0.20; 95% CI 0.06 to 0.63; p=0.006). After propensity score matching with risk stratification, minimally invasive surgical necrosectomy remained associated with a lower risk of death than open necrosectomy in the very high-risk group (42/111 vs 59/111; risk ratio, 0.70; 95% CI 0.52 to 0.95; p=0.02). Endoscopic necrosectomy was associated with a lower risk of death than open necrosectomy in the high-risk group (3/40 vs 12/40; risk ratio, 0.27; 95% CI 0.08 to 0.88; p=0.03) and in the very high-risk group (12/57 vs 28/57; risk ratio, 0.43; 95% CI 0.24 to 0.77; p=0.005). In high-risk patients with necrotising pancreatitis, minimally invasive surgical and endoscopic necrosectomy are associated with reduced death rates compared with open necrosectom

Google Goes Cancer: Improving Outcome Prediction for Cancer Patients by Network-Based Ranking of Marker Genes

Predicting the clinical outcome of cancer patients based on the expression of marker genes in their tumors has received increasing interest in the past decade. Accurate predictors of outcome and response to therapy could be used to personalize and thereby improve therapy. However, state of the art methods used so far often found marker genes with limited prediction accuracy, limited reproducibility, and unclear biological relevance. To address this problem, we developed a novel computational approach to identify genes prognostic for outcome that couples gene expression measurements from primary tumor samples with a network of known relationships between the genes. Our approach ranks genes according to their prognostic relevance using both expression and network information in a manner similar to Google's PageRank. We applied this method to gene expression profiles which we obtained from 30 patients with pancreatic cancer, and identified seven candidate marker genes prognostic for outcome. Compared to genes found with state of the art methods, such as Pearson correlation of gene expression with survival time, we improve the prediction accuracy by up to 7%. Accuracies were assessed using support vector machine classifiers and Monte Carlo cross-validation. We then validated the prognostic value of our seven candidate markers using immunohistochemistry on an independent set of 412 pancreatic cancer samples. Notably, signatures derived from our candidate markers were independently predictive of outcome and superior to established clinical prognostic factors such as grade, tumor size, and nodal status. As the amount of genomic data of individual tumors grows rapidly, our algorithm meets the need for powerful computational approaches that are key to exploit these data for personalized cancer therapies in clinical practice