scholarly journals Development and external validation of a prognostic nomogram for gastric cancer using the national cancer registry

Oncotarget ◽  
2016 ◽  
Vol 7 (24) ◽  
pp. 35853-35864 ◽  
Author(s):  
Jianjun Liu ◽  
Qirong Geng ◽  
Zhimin Liu ◽  
Shangxiang Chen ◽  
Jing Guo ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Cancer Registry ◽  
External Validation ◽  
National Cancer Registry

scholarly journals On the Effect of Triplet or Doublet Chemotherapy in Advanced Gastric Cancer: Results From a National Cancer Registry

2016 ◽  
Vol 14 (11) ◽  
pp. 1379-1388 ◽  
Author(s):  
Alberto Carmona-Bayonas ◽  
Paula Jiménez-Fonseca ◽  
Maria Luisa Sánchez Lorenzo ◽  
Avinash Ramchandani ◽  
Elena Asensio Martínez ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Advanced Gastric Cancer ◽  
Cancer Registry ◽  
National Cancer Registry ◽  
Doublet Chemotherapy

scholarly journals Treatment of advanced gastric cancer based on Lauren’s histological: Real-world data from the AGAMEMON National Cancer Registry

2017 ◽  
Vol 28 ◽  
pp. v231
Author(s):  
A. Carmona-Bayonas ◽  
P. Jiménez-Fonseca ◽  
R. Hernández ◽  
A. Custodio ◽  
J.M. Cano ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Advanced Gastric Cancer ◽  
Cancer Registry ◽  
Real World ◽  
Real World Data ◽  
World Data ◽  
National Cancer Registry

scholarly journals A radiomics-based model for predicting prognosis of locally advanced gastric cancer in the preoperative setting

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jaeseung Shin ◽  
Joon Seok Lim ◽  
Yong-Min Huh ◽  
Jie-Hyun Kim ◽  
Woo Jin Hyung ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Advanced Gastric Cancer ◽  
Cox Regression ◽  
Characteristic Curve ◽  
External Validation ◽  
Locally Advanced ◽  
Clinical Model ◽  
Locally Advanced Gastric Cancer ◽  
Contrast Enhanced Ct ◽  
Preoperative Ct

AbstractThis study aims to evaluate the performance of a radiomic signature-based model for predicting recurrence-free survival (RFS) of locally advanced gastric cancer (LAGC) using preoperative contrast-enhanced CT. This retrospective study included a training cohort (349 patients) and an external validation cohort (61 patients) who underwent curative resection for LAGC in 2010 without neoadjuvant therapies. Available preoperative clinical factors, including conventional CT staging and endoscopic data, and 438 radiomic features from the preoperative CT were obtained. To predict RFS, a radiomic model was developed using penalized Cox regression with the least absolute shrinkage and selection operator with ten-fold cross-validation. Internal and external validations were performed using a bootstrapping method. With the final 410 patients (58.2 ± 13.0 years-old; 268 female), the radiomic model consisted of seven selected features. In both of the internal and the external validation, the integrated area under the receiver operating characteristic curve values of both the radiomic model (0.714, P < 0.001 [internal validation]; 0.652, P = 0.010 [external validation]) and the merged model (0.719, P < 0.001; 0.651, P = 0.014) were significantly higher than those of the clinical model (0.616; 0.594). The radiomics-based model on preoperative CT images may improve RFS prediction and high-risk stratification in the preoperative setting of LAGC.

scholarly journals South African National Cancer Registry: Effect of withheld data from private health systems on cancer incidence estimates

2015 ◽  
Vol 105 (2) ◽  
pp. 107 ◽  
Author(s):  
E Singh ◽  
J M Underwood ◽  
C Nattey ◽  
C Babb ◽  
M Sengayi ◽  
...  
Keyword(s):  
South African ◽  
Health Systems ◽  
Cancer Registry ◽  
Cancer Incidence ◽  
National Cancer Registry
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