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 refined representation method of the particle size distribution curve based on grading entropy

The particle size distribution(PSD)curve is an important expression of the basic properties of the soil.The characteristic parameters such as median particle size D_(50),effective particle size D_(10),and some combinations of characteristic parameters can only represent some points of the PSD curve,failing to represent all information of the PSD curve.In this paper,a fraction characteristic parameter F that could reflect the fraction size variation of the PSD curve was introduced based on grading entropy.And a new presentation method of the A-B-F three parameters was proposed for a refined representation of the PSD curve.It was found that the newly constructed model not only better represents the differences in the width of PSD curves but also has a higher sensitivity than the A-B two-parameter model proposed by Lőrincz.For PSD curves with similar distributions,the new presentation method has a higher degree of discrimination than that of the D_(50)-C_(u)-C_(c) three-parameter or the four-parameter combination G_(c) proposed by Arshad.Finally,the application of this new method in describing the spatial nonuniformity distribution of depositions and predicting the soil hydraulic conductivity was discussed.The research results can provide a reference for the refined representation of PSD curves.

Development of a drill energy utilization index for aiding selection of drill machines in surface mines

Drill machines used in surface mines, particularly in coal, is characterized by a very poor utilization （around 40%） and low availability （around 60%）. The main purpose of this study is to develop a drill selec- tion methodology and simultaneously a performance evaluation technique based on drill cuttings produced and drilling rate achieved. In all 28 blast drilled through were investigated. The drilling was accomplished by 5 different drill machines of Ingersoll-Rand and Revathi working in coal mines of Sonepur Bazari （SECL） and Block-II （BCCL）. The drills are Rotary and Rotary Percussive type using tri- cone rock roller bits. Drill cuttings were collected and sieve analysis was done in the laboratory. Using Rosin Ramler Diagram, coarseness index （CI）, mean chip size （d）, specific-st trace area （SSA） and charac- teristic particle size distribution curves for all the holes drilled were plotted. The predictor equation for drill penetration rate established through multiple regressions was found to have a very good correlation with an index of determination of 0.85. A comparative analysis of particle size distribution curves was used to evaluate the drill efficiency. The suggested approach utilises the area under the curve, after the point of trend reversal and brittleness ratio of the respective bench to arrive at drill energy utilization index （DEUI）, for mapping of drill machine to bench, The developed DEU1 can aid in selecting or mapping a right machine to right bench for achieving higher penetration rate and utilizations.

Auto-Regressive Models of Non-Stationary Time Series with Finite Length

To analyze and simulate non-stationary time series with finite length, the statistical characteris- tics and auto-regressive (AR) models of non-stationary time series with finite length are discussed and stud- ied. A new AR model called the time varying parameter AR model is proposed for solution of non-stationary time series with finite length. The auto-covariances of time series simulated by means of several AR models are analyzed. The result shows that the new AR model can be used to simulate and generate a new time series with the auto-covariance same as the original time series. The size curves of cocoon filaments re- garded as non-stationary time series with finite length are experimentally simulated. The simulation results are significantly better than those obtained so far, and illustrate the availability of the time varying parameter AR model. The results are useful for analyzing and simulating non-stationary time series with finite length.