AIM To investigate the relationship between coronary calcium score(CCS) and vulnerable plaque/significant stenosis using coronary computed tomographic angiography(CCTA). METHODS CCTA was performed in 651 patients and ...AIM To investigate the relationship between coronary calcium score(CCS) and vulnerable plaque/significant stenosis using coronary computed tomographic angiography(CCTA). METHODS CCTA was performed in 651 patients and these patients were divided into the four groups(CCS 0, 1-100, 101-400 and > 400). We studied the incidence of high-risk plaque, including positive remodeling, low attenuation plaque, spotty calcification, and napkin-ring sign, and significant stenosis in each group. RESULTS High-risk plaque was found in 1.3%, 10.1%, 13.3% and 13.4% of patients with CCS 0, 1-100, 101-400 and > 400, respectively(P < 0.001). The difference was only significant for patients with zero CCS. The incidence of significant stenosis was 0.6%, 7.6%, 13.3% and 26.9% for each patient group, respectively(P < 0.001), which represented a significant stepwise increase as CCS increased. The combined incidence of high-risk plaque and significant stenosis was 1.9%, 17.7%, 26.9% and 40.3% in each patient group, respectively(P < 0.001), again representing a significant stepwise increase with CCS. The rate of major coronary event was 0%, 4.0%, 7.9% and 17.2% in each patient group, respectively(P < 0.001), another significant stepwise increase as CCS increased. CONCLUSION Stepwise increased risk of coronary events associated with increasing CCS is caused by increasing incidence of significant stenosis, while that of high-risk plaque remains the same.展开更多
Objoctive： There is heterogeneity in the prognosis of gastric cancers staged according to the tumornodes-metastasis （TNM） system. This study evaluated the prognostic potential of an immune score system to supplemen...Objoctive： There is heterogeneity in the prognosis of gastric cancers staged according to the tumornodes-metastasis （TNM） system. This study evaluated the prognostic potential of an immune score system to supplement the TNM staging system. Mothodsg An immunohistochemical analysis was conducted to assess the density of T cells, B cells, and myeloid-derived suppressor cells （MDSCs） in cancer tissues from 100 stage IIIA gastric cancer patients; the expression of the high-mobility group protein B1 （HMGB1） was also evaluated in cancer cells. The relationship between the overall survival （OS）, disease-free survival （DFS）, and immunological parameters was analyzed.Results： An immune score system was compiled based on the prognostic role of the density ofT cells, B cells, MDSCs, and the expression of HMGB1 in cancer tissues. The median 5-year survival of this group of patient was 32%. However, the 5-year survival rates of 80.0%, 51.7%, 0%, 5.8%, and 0% varied among the patients with an immune score of 4 to those with an immune score of 0 based on the immune score system, respectively. Similarly, differences in DFS rates were observed among the immune score subgroups. Concluslons： An immune score system could effectively identify the prognostic heterogeneity within stage IliA gastric cancer patients, implying that this immune score system may potentially supplement the TNM staging system, and help in identifying a more homogeneous group of patients who on the basis of prognosis can undergo adjuvant therapy.展开更多
Machine learning methods, one type of methods used in artificial intelligence, are now widely used to analyze two-dimensional (2D) images in various fields. In these analyses, estimating the boundary between two regio...Machine learning methods, one type of methods used in artificial intelligence, are now widely used to analyze two-dimensional (2D) images in various fields. In these analyses, estimating the boundary between two regions is basic but important. If the model contains stochastic factors such as random observation errors, determining the boundary is not easy. When the probability distributions are mis-specified, ordinal methods such as probit and logit maximum likelihood estimators (MLE) have large biases. The grouping estimator is a semiparametric estimator based on the grouping of data that does not require specific probability distributions. For 2D images, the grouping is simple. Monte Carlo experiments show that the grouping estimator clearly improves the probit MLE in many cases. The grouping estimator essentially makes the resolution density lower, and the present findings imply that methods using low-resolution image analyses might not be the proper ones in high-density image analyses. It is necessary to combine and compare the results of high- and low-resolution image analyses. The grouping estimator may provide theoretical justifications for such analysis.展开更多
文摘AIM To investigate the relationship between coronary calcium score(CCS) and vulnerable plaque/significant stenosis using coronary computed tomographic angiography(CCTA). METHODS CCTA was performed in 651 patients and these patients were divided into the four groups(CCS 0, 1-100, 101-400 and > 400). We studied the incidence of high-risk plaque, including positive remodeling, low attenuation plaque, spotty calcification, and napkin-ring sign, and significant stenosis in each group. RESULTS High-risk plaque was found in 1.3%, 10.1%, 13.3% and 13.4% of patients with CCS 0, 1-100, 101-400 and > 400, respectively(P < 0.001). The difference was only significant for patients with zero CCS. The incidence of significant stenosis was 0.6%, 7.6%, 13.3% and 26.9% for each patient group, respectively(P < 0.001), which represented a significant stepwise increase as CCS increased. The combined incidence of high-risk plaque and significant stenosis was 1.9%, 17.7%, 26.9% and 40.3% in each patient group, respectively(P < 0.001), again representing a significant stepwise increase with CCS. The rate of major coronary event was 0%, 4.0%, 7.9% and 17.2% in each patient group, respectively(P < 0.001), another significant stepwise increase as CCS increased. CONCLUSION Stepwise increased risk of coronary events associated with increasing CCS is caused by increasing incidence of significant stenosis, while that of high-risk plaque remains the same.
基金support from the National Nature Science Foundation of China ( Grant No.81272341, 81401156)Research Program of Guangzhou Municipal Health Bureau Foundation of China (Grant No.20141A011085, 20141A011088)The PhD Start-up Fund Guangzhou Medical University (Grant No.2013C49)
文摘Objoctive： There is heterogeneity in the prognosis of gastric cancers staged according to the tumornodes-metastasis （TNM） system. This study evaluated the prognostic potential of an immune score system to supplement the TNM staging system. Mothodsg An immunohistochemical analysis was conducted to assess the density of T cells, B cells, and myeloid-derived suppressor cells （MDSCs） in cancer tissues from 100 stage IIIA gastric cancer patients; the expression of the high-mobility group protein B1 （HMGB1） was also evaluated in cancer cells. The relationship between the overall survival （OS）, disease-free survival （DFS）, and immunological parameters was analyzed.Results： An immune score system was compiled based on the prognostic role of the density ofT cells, B cells, MDSCs, and the expression of HMGB1 in cancer tissues. The median 5-year survival of this group of patient was 32%. However, the 5-year survival rates of 80.0%, 51.7%, 0%, 5.8%, and 0% varied among the patients with an immune score of 4 to those with an immune score of 0 based on the immune score system, respectively. Similarly, differences in DFS rates were observed among the immune score subgroups. Concluslons： An immune score system could effectively identify the prognostic heterogeneity within stage IliA gastric cancer patients, implying that this immune score system may potentially supplement the TNM staging system, and help in identifying a more homogeneous group of patients who on the basis of prognosis can undergo adjuvant therapy.
文摘Machine learning methods, one type of methods used in artificial intelligence, are now widely used to analyze two-dimensional (2D) images in various fields. In these analyses, estimating the boundary between two regions is basic but important. If the model contains stochastic factors such as random observation errors, determining the boundary is not easy. When the probability distributions are mis-specified, ordinal methods such as probit and logit maximum likelihood estimators (MLE) have large biases. The grouping estimator is a semiparametric estimator based on the grouping of data that does not require specific probability distributions. For 2D images, the grouping is simple. Monte Carlo experiments show that the grouping estimator clearly improves the probit MLE in many cases. The grouping estimator essentially makes the resolution density lower, and the present findings imply that methods using low-resolution image analyses might not be the proper ones in high-density image analyses. It is necessary to combine and compare the results of high- and low-resolution image analyses. The grouping estimator may provide theoretical justifications for such analysis.
