Early detection of pancreatic cancer is promising for improving clinical outcome; however, no effective biomarker has yet been identified. Here, we detected 61 clinical serum parameters in 200 healthy controls (Ctrls), 163 pancreatic ductal adenocarcinoma (PDAC) patients and 109 benign pancreatitis patients (Benign) in the training group. A metropolis algorithm with Monte Carlo simulation was used for identifying parameter panels. Sera from 183 Ctrl, 129 PDAC and 95 Benign individuals were used for cross-validation. Samples from 77 breast, 72 cervical, 101 colorectal, 138 gastric, 108 prostate and 132 lung cancer patients were collected for evaluating cancer selectivity. A panel consisting of carbohydrate antigen (CA)19-9, albumin (ALB), C-reactive protein (CRP) and interleukin (IL)-8 had the highest diagnostic value for discriminating between PDAC and Ctrl. The sensitivity (SN) was 99.39% for all-stage, 96.10% for early-stage and 98.80% for advanced-stage PDAC at 90% specificity (SP). In the validation group, the sensitivities were 93.80, 93.10 and 94.40%, respectively, at 90% SP. This panel also identified 80.52% of the breast cancer, 66.67% cervical cancer, 86.14% colorectal cancer, 89.86% gastric cancer, 71.30% prostate cancer and 93.85% lung cancer samples as non-PDAC. The panel consisting of CA19-9, carbon dioxide, CRP and IL-6 panel had the highest diagnostic value for discriminating between PDAC and Benign. The SN was 74.23% for all-stage, 75.30% for early-stage and 74.40% for advanced-stage PDAC at 90% SP. In the validation group, the sensitivities were 72.10, 76.10 and 67.20%, respectively, at 90% SP. Our parameter panels may aid in the early detection of PDAC to improve clinical outcome.
Keywords: Monte Carlo simulation; early detection; metropolis algorithm; pancreatic cancer; serum.
© 2013 UICC.