PROJECT TITLE :

Fuzzy Logic-Based Prognostic Score for Outcome Prediction in Esophageal Cancer

ABSTRACT:

Given the poor prognosis of esophageal cancer and the invasiveness of combined modality treatment, improved prognostic scoring systems are needed. We developed a fuzzy logic-based system to improve the predictive performance of a risk score based on the serum concentrations of C-reactive protein (CRP) and albumin in a cohort of 271 patients with esophageal cancer before radiotherapy. Univariate and multivariate survival analyses were employed to validate the independent prognostic value of the fuzzy risk score. To further compare the predictive performance of the fuzzy risk score with other prognostic scoring systems, time-dependent receiver operating characteristic curve analysis was used. Application of Fuzzy Logic to the serum values of CRP and albumin increased predictive performance for one-year overall survival (AUC = 0.773) compared with that of a single marker (AUC = 0.743 and 0.700 for CRP and albumin, respectively), where the AUC denotes the area under curve. This fuzzy logic-based approach also performed consistently better than the Glasgow prognostic score (AUC = 0.745). Thus, application of fuzzy logic to the analysis of serum markers can more accurately predict the outcome for patients with esophageal cancer.