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( Junyi Shen ),( Linye He ),( Chuan Li ),( Tianfu Wen ),( Weixia Chen ),( Changli Lu ),( Lvnan Yan ),( Bo Li ),( Jiayin Yang ) 대한간학회 2017 Gut and Liver Vol.11 No.5
Background/Aims: Solitary hepatocellular carcinoma (HCC) is a subgroup of HCCs. We aimed to establish nomograms for predicting the survival of solitary HCC patients after hepatectomy. Methods: A total of 538 solitary HCC patients were randomly classified into training and validation sets. A Cox model was used to identify predictors of overall survival (OS) in the training set. A nomogram was generated based on these predictors and was validated using the validation set. Results: Tumor size, microvascular invasion, and major vascular invasion were significantly associated with OS in the training set. Nomograms were developed based on these predictors in the multivariate analysis. The C-index was 0.75 for the OS nomogram and 0.72 for the recurrence-free sur-vival nomogram. Compared to the index of conventional stag-ing systems for predicting survival (0.71 for Barcelona Clinic Liver Cancer, 0.66 for the seventh American Joint Committee on Cancer, 0.68 for Cancer of the Liver Italian Program, and 0.70 for Hong Kong Liver Cancer), the index of the OS nomo-gram was significantly higher. Moreover, the calibration curve fitted well between the predicted and observed survival rate. Similarly, in the validation set, the nomogram discrimination was superior to those of the four staging systems (p<0.001). Conclusions: The nomograms demonstrated good discrimi-nation performance in predicting 3- and 5-year survival rates for solitary HCCs after hepatectomy. (Gut Liver 2017;11:684- 692)
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Design and Implementation on Spatial Science and Technology Information Database of CSI
Chen, Xiu Wan,Deng, Zheng Quan,Lu, Zhi Gao,Ma, Jia,Lin, Jia Yuan,Zhang, Wen Jiang,Luo, Tianfu,Liu, Baofu 대한원격탐사학회 2000 International Symposium on Remote Sensing Vol.16 No.1
Remote Sensing technology, which is characterized by producing imagery an multi-platform, different temporal and spatial resolution, has greatly improved mankind's capability of acquisition, processing and application of spatial information. The increase of spatial data sources and the development, applications and industrialization of spatial information technology are urging the need of spatial data sharing and exchanging. Based an a brief introduction an the China Spatial Information Network (CSI) and its database system, the CSI Spatial Science and Technology Information Database (SSTID) management system was designed and implemented in this paper.
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Liangliang Xu,Lian Li,Peng Wang,Ming Zhang,Yanfang Zhang,Xiangyong Hao,Lvnan Yan,Bo Li,Tianfu Wen,Mingqing Xu 거트앤리버 소화기연관학회협의회 2019 Gut and Liver Vol.13 No.6
Background/Aims: Microvascular invasion (MVI) is an established risk factor for hepatocellular carcinoma (HCC). However, prediction models that specifically focus on the individual prognoses of HCC patients with MVI is lacking. Methods: A total of 385 HCC patients with MVI were randomly assigned to training and validation cohorts in a 2:1 ratio. The outcomes were disease-free survival (DFS) and overall survival (OS). Prognostic nomograms were established based on the results of multivariate analyses. The concordance index (C-index), calibration plots and Kaplan-Meier curves were employed to evaluate the accuracy, calibration and discriminatory ability of the models. Results: The independent risk factors for both DFS and OS included age, tumor size, tumor number, the presence of gross vascular invasion, and the presence of Glisson’s capsule invasion. The platelet-tolymphocyte ratio was another risk factor for OS. On the basis of these predictors, two nomograms for DFS and OS were constructed. The C-index values of the nomograms for DFS and OS were 0.712 (95% confidence interval [CI], 0.679 to 0.745; p<0.001) and 0.698 (95% CI, 0.657 to 0.739; p<0.001), respectively, in the training cohort and 0.704 (95% CI, 0.650 to 0.708; p<0.001) and 0.673 (95% CI, 0.607 to 0.739; p<0.001), respectively, in the validation cohort. The calibration curves showed optimal agreement between the predicted and observed survival rates. The Kaplan-Meier curves suggested that these two nomograms had satisfactory discriminatory abilities. Conclusions: These novel predictive models have satisfactory accuracy and discriminatory abilities in predicting the prognosis of HCC patients with MVI after hepatectomy.
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