Tumor size as a prognostic factor in patients with advanced gastric cancer in the lower third of the stomach

AIM: To explore the impact of tumor size on outcomes in patients with advanced gastric cancer in the lower third of the stomach. METHODS: We retrospectively analyzed the clinical records of 430 patients with advanced gastric cancer in the lower third of the stomach who underwent distal subtotal gastrectomy and D2 lymphadenectomy in our hospital from January 1998 to June 2004. Receiver-operating characteristic (ROC) curve analysis was used to determine the appropriate cutoff value for tumor size, which was measured as maximum tumor diameter. Based on this cutoff value, patients were divided into two groups: those with large-sized tumors (LSTs) and those with small-sized tumors (SSTs). The correlations between other clinicopathologic factors and tumor size were investigated, and the 5-year overall survival (OS) rate was compared between the two groups. Potential prognostic factors were evaluated by univariate KaplanMeier survival analysis and multivariate Cox's propor-tional hazard model analysis. The 5-year OS rates in the two groups were compared according to pT stage and pN stage. RESULTS: The 5-year OS rate in the 430 patients with advanced gastric cancer in the lower third of the stomach was 53.7%. The mean ± SD tumor size was 4.9 ± 1.9 cm, and the median tumor size was 5.0 cm. ROC analysis indicated that the sensitivity and specificity results for the appropriate tumor size cutoff value of 4.8 cm were 80.0% and 68.2%, respectively (AUC=0.795, 95%CI: 0.751-0.839, P=0.000). Using this cutoff value, 222 patients (51.6%) had LSTs (tumor size ≥ 4.8 cm) and 208 (48.4%) had SSTs (tumor size<4.8 cm). Tumor size was significantly correlated with histological type (P=0.039), Borrmann type (P=0.000), depth of tumor invasion (P=0.000), lymph node metastasis (P=0.000), tumor-nodes metastasis stage (P=0.000), mean number of metastatic lymph nodes (P=0.000) and metastatic lymph node ratio (P=0.000). Patients with LSTs had a significantly lower 5-year OS rate than those with SSTs (37.1% vs 63.3%, P=0.000). Univariate analysis showed that depth of tumor invasion (c 2=69.581, P=0.000), lymph node metastasis (c 2=138.815, P=0.000), tumor size (c 2=78.184, P=0.000) and metastatic lymph node ratio (c 2=139.034, P=0.000) were significantly associated with 5-year OS rate. Multivariate analysis revealed that depth of tumor invasion (P=0.000), lymph node metastasis (P=0.019) and tumor size (P=0.000) were independent prognostic factors. Gastric cancers were divided into 12 subgroups: pT2N0; pT2N1; pT2N2; pT2N3; pT3N0; pT3N1; pT3N2; pT3N3; pT4aN0; pT4aN1; pT4aN2; and pT4aN3. In patients with pT2-3N3 stage tumors and patients with pT4a stage tumors, 5-year OS rates were significantly lower for LSTs than for SSTs (P<0.05 each), but there were no significant differences in the 5-year OS rates in LST and SST patients with pT23N0-2 stage tumors (P > 0.05). CONCLUSION: Using a tumor size cutoff value of 4.8cm, tumor size is a prognostic factor in patients with pN3 stage or pT4a stage advanced gastric cancer located in the lower third of the stomach.

A fast and practical method to pack spheres for mesh generation

Sphere packing is an attractive way to generate high quality mesh. Several algorithms have been proposed in this topic, however these algorithms are not sufficiently fast for large scale problems. The paper presents an efficient sphere packing algorithm which is much faster and appears to be the most practical among all sphere packing methods presented so far for mesh generation. The algorithm packs spheres inside a domain using advancing front method. High efficiency has resulted from a concept of 4R measure, which localizes all the computations involved in the whole sphere packing process.

Size Reduction of Tunable Micromachined Filters for High Speed Operations

The size reduction of tunable micromachined filters is carried out for high-speed wavelength tuning. We fabricated micromachined filters having a miniature structure with an air gap of 300 run and a short cantilever of 45 urn, exhibiting fast response of below 3 us.

Timetable optimization for single bus line based on hybrid vehicle size model

This study proposes a flexible timetable optimization method based on hybrid vehicle size model to tackle the bus demand fluctuations in transit operation. Three different models for hybrid vehicle, large vehicle and small vehicle are built in this study, respectively. With the operation data of Shanghai Transit Route 55 at peak and off-peak hours, a heuristic algorithm was proposed to solve the problem. The results indicate that the hybrid vehicle size model excels the other two modes both in the total time and total cost. The study verifies the rationality of the strategy of hybrid vehicle size model and highlights the importance of the adaptive vehicle size in dealing with the bus demand fluctuation. The main innovation of the study is that unlike traditional timetables, the arrangement of the scheduling interval and the corresponding bus type or size are both involved in the timetable of hybrid vehicle size bus mode, which will be more effective to solve the problem of passenger demand fluctuation. Findings from this research would provide a new perspective to improve the level of regular bus service.

Are China＇s Farms Growing？

China＇s agricultural sector faces challenges because most farms are still small scale. China＇s policy is to encourage the consolidation o f farms and promote farms that are larger in scale. A question that arises is： Are China＇s farms growing？ The goal of the present paper is to determine whether large farms in China have emerged or iffarms remain small. To meet this goal, we systematically document the trends in the operational sizes of China＇s farms and measure the determinants of changes in farm size. Using a nationally representative dataset, the study shows that in 2013 China＇s farming sector was still mostly characterized by small-scale farms. However, at the same time, there is an emerging class of middle-sized and larger-sized farms. Most large farms are being run by households but there is a set of large farms that are companylcooperative-run. Today, farmers on larger farms are younger and better educated than the average farmer.