Serum CA 19-9 as a Marker of Resectability and Survival in Patients with Potentially Resectable Pancreatic Cancer Treated with Neoadjuvant Chemoradiation

Purpose The role of carbohydrate antigen (CA) 19-9 in the evaluation of patients with resectable pancreatic cancer treated with neoadjuvant therapy prior to planned surgical resection is unknown. We evaluated CA 19-9 as a marker of therapeutic response, completion of therapy, and survival in patients enrolled on two recently reported clinical trials. Patients and Methods We analyzed patients with radiographically resectable adenocarcinoma of the head/uncinate process treated on two phase II trials of neoadjuvant chemoradiation. Patients without evidence of disease progression following chemoradiation underwent pancreaticoduodenectomy (PD). CA 19-9 was evaluated in patients with a normal bilirubin level. Results We enrolled 174 patients, and 119 (68%) completed all therapy including PD. Pretreatment CA 19-9 <37 U/ml had a positive predictive value (PPV) for completing PD of 86% but a negative predictive value (NPV) of 33%. Among patients without evidence of disease at last follow-up, the highest pretreatment CA 19-9 was 1,125 U/ml. Restaging CA 19-9 <61 U/ml had a PPV of 93% and a NPV of 28% for completing PD among resectable patients. The area under the receiver-operating characteristics curve of pretreatment and restaging CA 19-9 levels for completing PD was 0.59 and 0.74, respectively. We identified no association between change in CA 19-9 and histopathologic response (P = 0.74). Conclusions Although the PPV of CA 19-9 for completing neoadjuvant therapy and undergoing PD was high, its clinical utility was compromised by a low NPV. Decision-making for patients with resectable PC should remain based on clinical assessment and radiographic staging.PublishedN/

Pattern recognition for bivariate process mean shifts using feature-based artificial neural network

In multivariate quality control, the artificial neural networks (ANN)-based pattern recognition schemes generally performed better for monitoring bivariate process mean shifts and provided more efficient information for diagnosing the source variable(s) compared to the traditional multivariate statistical process control charting. However, these schemes revealed disadvantages in term of reference bivariate patterns in identifying the joint effect and excess false alarms in identifying stable process condition. In this study, feature-based ANN scheme was investigated for recognizing bivariate correlated patterns. Feature-based input representation was utilized into an ANN training and testing towards strengthening discrimination capability between bivariate normal and bivariate mean shift patterns. Besides indicating an effective diagnosis capability in dealing with low correlation bivariate patterns, the proposed scheme promotes a smaller network size and better monitoring capability as compared to the raw data-based ANN scheme