Integration of radiotherapy and chemotherapy for abdominal lymph node recurrence in gastric cancer.

PURPOSE: Abdominal lymph node (ALN) recurrence in gastric cancer (GC) is rare and usually unresectable. We investigated the effects of integration of radiotherapy (RT) and chemotherapy against ALN recurrence in GC. METHODS: We retrospectively categorized GC patients with ALN recurrence treated between 2005 and 2013 into two groups: those treated with integration of RT and chemotherapy (RCT) and those who received systemic chemotherapy only (CT). The median follow-up period after ALN recurrence for all patients was 20 months. RESULTS: Of 53 patients, 31 and 22 were in the RCT and CT groups, respectively. Isolated distant failure (DF; 35.5%) without local progression (LP) was the dominant pattern of failure (POF) in the RCT group (median DF-free period, 26 months). LP followed by DF (31.8%) was the dominant POF in the CT group; LP (median LP-free period, 8 months) occurred 10 months earlier than DF (median DF-free period, 18 months). RCT patients had significantly longer median progression-free survival (PFS) compared to CT patients (25 vs. 8 months; P = 0.021). On multivariate analysis, treatment (CT vs. RCT) was an independent prognostic factor for PFS (hazard ratio 2.085; 95% confidence interval 1.073-4.050; P = 0.013). CONCLUSIONS: Integration of RT and chemotherapy achieved long-term local control and prolonged PFS in GC patients with ALN recurrence. Local RT is feasible for treating isolated ALN recurrences.

Gene regulatory network identification from the yeast cell cycle based on a neuro-fuzzy system.

Many studies exist for reconstructing gene regulatory networks (GRNs). In this paper, we propose a method based on an advanced neuro-fuzzy system, for gene regulatory network reconstruction from microarray time-series data. This approach uses a neural network with a weighted fuzzy function to model the relationships between genes. Fuzzy rules, which determine the regulators of genes, are very simplified through this method. Additionally, a regulator selection procedure is proposed, which extracts the exact dynamic relationship between genes, using the information obtained from the weighted fuzzy function. Time-series related features are extracted from the original data to employ the characteristics of temporal data that are useful for accurate GRN reconstruction. The microarray dataset of the yeast cell cycle was used for our study. We measured the mean squared prediction error for the efficiency of the proposed approach and evaluated the accuracy in terms of precision, sensitivity, and F-score. The proposed method outperformed the other existing approaches.