scholarly journals Development and validation of pretreatment nomogram for disease‐specific mortality in gastric cancer‐A competing risk analysis

2021 ◽  
Author(s):  
Etsuro Bando ◽  
Xinge Ji ◽  
Michael W. Kattan ◽  
Maria Bencivenga ◽  
Giovanni Manzoni ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Risk Analysis ◽  
Competing Risk ◽  
Competing Risk Analysis ◽  
Specific Mortality ◽  
Disease Specific Mortality ◽  
Development And Validation ◽  
Disease Specific

Development and validation of a pretreatment nomogram to predict disease-specific mortality in gastric cancer.

2020 ◽  
Vol 38 (4_suppl) ◽  
pp. 312-312
Author(s):  
Etsuro Bando ◽  
Xinge Ji ◽  
Michael W. Kattan ◽  
Maria Bencivenga ◽  
Giovanni De Manzoni ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Risk Model ◽  
External Validation ◽  
Multivariable Analysis ◽  
Individual Risk ◽  
American Joint Committee ◽  
Specific Mortality ◽  
Disease Specific Mortality ◽  
The University ◽  
Disease Specific

312 Background: The American Joint Committee on Cancer (AJCC) has increasingly recognized the need for individual risk prediction model. The AJCC has emphasized the attractiveness of disease-specific mortality (DSM), which can properly control for competing events. as an endpoint of risk model, as well as overall survival (OS) and disease-specific survival (DSS). For the era of tailored therapy, we aimed to develop a new pretreatment gastric cancer nomogram for prediction of DSM. Methods: The nomogram was developed using data of 5,231 patients with primary gastric cancer treated at Shizuoka Cancer Center (Shizuoka, Japan), and it was created with a Fine and Gray competing-risks proportional hazards regression model. Fifteen clinical variables, which were obtained at pretreatment, were collected and registered, to develop the nomogram. Data of independent cohort of patients from the University of Verona (Italy; 389 patients) formed the external validation cohort. The model was validated internally and externally using measures of discrimination (Harrell’s C-index), calibration and decision curve analysis. Results: In the development procedure, multivariable analysis for DSM selected 14 variables for constructing the nomogram. The developed nomogram showed good discrimination, with a C-index of 0.887 (95%CI; 0.881-0.894); that of the American Joint Committee on Cancer (AJCC) clinical stage was 0.794 (0.784-0.804). In the external validation procedure, the C-index was 0.713 (0.680-0.746) (AJCC, 0.582, 0.539-0.622) in the University of Verona cohort. The nomogram performed well in the calibration and decision curve analyses when applied to both the internal and external validation cohorts. Conclusions: This new pretreatment risk model accurately predicts DSM in gastric cancer and can be used for patient counseling in clinical practice and stratification in clinical trials.

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