Circulating tumor cells criteria (CyCAR) versus standard RECIST criteria for treatment response assessment in metastatic colorectal cancer patients

The use of circulating tumor cells (CTCs) as indicators of treatment response in metastatic colorectal cancer (mCRC) needs to be clarified. The objective of this study is to compare the Response Evaluation Criteria in Solid Tumors (RECIST) with the Cytologic Criteria Assessing Response (CyCAR), based on the presence and phenotypic characterization of CTCs, as indicators of FOLFOX–bevacizumab treatment response. We observed a decrease of CTCs (42.8 vs. 18.2%) and VEGFR positivity (69.7% vs. 41.7%) after treatment. According to RECIST, 6.45% of the patients did not show any clinical benefit, whereas 93.55% patients showed a favorable response at 12 weeks. According to CyCAR, 29% had a non-favorable response and 71% patients did not. No significant differences were found between the response assessment by RECIST and CyCAR at 12 or 24 weeks. However, in the multivariate analysis, RECIST at 12 weeks and CyCAR at 24 weeks were independent prognostic factors for OS (HR: 0.1, 95% CI 0.02–0.58 and HR: 0.35, 95% CI 0.12–0.99 respectively). CyCAR results were comparable to RECIST in evaluating the response in mCRC and can be used as an alternative when the limitation of RECIST requires additional response analysis techniques.This work was supported by Roche Spain and a Ph.D. grant from the University of Granada

A Feedback Mechanism Based on Granular Computing to Improve Consensus in GDM

Group decision making is an important task in real world activities. It consists in obtaining the best solution to a particular problem according to the opinions given by a set of decision makers. In such a situation, an important issue is the level of consensus achieved among the decision makers before making a decision. For this reason, different feedback mechanisms, which help decision makers for reaching the highest degree of consensus possible, have been proposed in the literature. In this contribution, we present a new feedback mechanism based on granular computing to improve consensus in group decision making problems. Granular computing is a framework of designing, processing, and interpretation of information granules, which can be used to obtain a required flexibility to improve the level of consensus within the group of decision makers